[Federal Register: August 4, 2003 (Volume 68, Number 149)]
[Notices]
[Page 45949-45988]
From the Federal Register Online via GPO Access [wais.access.gpo.gov]
[DOCID:fr04au03-137]
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DEPARTMENT OF THE TREASURY
Office of the Comptroller of the Currency
[Docket No. 03-15]
FEDERAL RESERVE SYSTEM
[Docket No. OP-1153]
FEDERAL DEPOSIT INSURANCE CORPORATION
DEPARTMENT OF THE TREASURY
Office of Thrift Supervision
[No. 2003-28]
Internal Ratings-Based Systems for Corporate Credit and
Operational Risk Advanced Measurement Approaches for Regulatory Capital
AGENCIES: Office of the Comptroller of the Currency (OCC), Treasury;
Board of Governors of the Federal Reserve System (Board); Federal
Deposit Insurance Corporation (FDIC); and Office of Thrift Supervision
(OTS), Treasury.
ACTION: Draft supervisory guidance with request for comment.
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SUMMARY: The OCC, Board, FDIC, and OTS (the Agencies) are publishing
for industry comment two documents that set forth draft supervisory
guidance for implementing proposed revisions to the risk-based capital
standards in the United States. These proposed revisions, which would
implement the New Basel Capital Accord in the United States, are
published as an advance notice of proposed rulemaking (ANPR) elsewhere
in today's Federal Register. Under the advanced approaches for credit
and operational risk described in the ANPR, banking organizations would
use internal estimates of certain risk components as key inputs in the
determination of their regulatory capital requirements. The Agencies
believe that supervisory guidance is necessary to balance the
flexibility inherent in the advanced approaches with high standards
that promote safety and soundness and encourage comparability across
institutions.
The first document sets forth Draft Supervisory Guidance on
Internal Ratings-Based Systems for Corporate Credit (corporate IRB
guidance). This document describes supervisory expectations for
institutions that intend to adopt the advanced internal ratings-based
approach (A-IRB) for credit risk as set forth in today's ANPR. The
corporate IRB guidance is intended to provide supervisors and
institutions with a clear description of the essential components and
characteristics of an acceptable A-IRB framework. The guidance focuses
specifically on corporate credit portfolios; further guidance is
expected at a later date on other credit portfolios (including, for
example, retail and commercial real estate portfolios).
The second document sets forth Draft Supervisory Guidance on
Operational Risk Advanced Measurement Approaches for Operational Risk
(AMA guidance). This document outlines supervisory expectations for
institutions that intend to adopt an advanced measurement approach
(AMA) for operational risk as set forth in today's ANPR.
The Agencies are seeking comments on the supervisory standards set
forth in both documents. In addition to seeking comment on specific
aspects of the supervisory guidance set forth in the documents, the
Agencies are seeking comment on the extent to which the supervisory
guidance strikes the appropriate balance between flexibility and
specificity. Likewise, the Agencies are seeking comment on whether an
appropriate balance has been struck between the regulatory requirements
set forth in the ANPR and the supervisory standards set forth in these
documents.
DATES: Comments must be received no later than November 3, 2003.
ADDRESSES: Comments should be directed to:
OCC: Please direct your comments to: Office of the Comptroller of
the Currency, 250 E Street, SW., Public Information Room, Mailstop 1-5,
Washington, DC 20219, Attention: Docket No. 03-15; fax number (202)
874-4448; or Internet address: regs.comments@occ.treas.gov. Due to
delays in paper mail delivery in the Washington area, we encourage the
submission of comments by fax or e-mail whenever possible. Comments may
be inspected and photocopied at the OCC's Public Information Room, 250
E Street, SW., Washington, DC. You may make an appointment to inspect
comments by calling (202) 874-5043.
Board: Comments should refer to Docket No. OP-1153 and may be
mailed to Ms. Jennifer J. Johnson, Secretary, Board of Governors of the
Federal Reserve System, 20th Street and Constitution Avenue, NW.,
Washington, DC, 20551. However, because paper mail in the Washington
area and at the Board of Governors is subject to delay, please consider
submitting your comments by e-mail to regs.comments@federalreserve.gov,
or faxing them to the Office of the Secretary at 202/452-3819 or 202/
452-3102. Members of the public may inspect comments in Room MP-500 of
the Martin Building between 9 a.m. and 5 p.m. on weekdays pursuant to
Sec. 261.12, except as provided in Sec. 261.14, of the Board's Rules
Regarding Availability of Information, 12 CFR 261.12 and 261.14.
FDIC: Written comments should be addressed to Robert E. Feldman,
Executive Secretary, Attention: Comments, Federal Deposit Insurance
Corporation, 550 17th Street, NW., Washington, DC, 20429. Commenters
are encouraged to submit comments by facsimile transmission to (202)
898-3838 or by electronic mail to Comments@FDIC.gov. Comments also may
be hand-delivered to the guard station at the rear of the 550 17th
Street Building (located on F Street) on business days between 8:30
a.m. and 5 p.m. Comments may be inspected and photocopied at the FDIC's
Public Information Center, Room 100, 801 17th Street, NW., Washington,
DC between 9 a.m. and 4:30 p.m. on business days.
OTS: Send comments to Regulation Comments, Chief Counsel's Office,
Office of Thrift Supervision, 1700 G Street, NW., Washington, DC 20552,
Attention: No. 2003-28. Delivery: Hand deliver comments to the Guard's
desk, east lobby entrance, 1700 G Street, NW., from 9 a.m. to 4 p.m. on
business days, Attention: Regulation Comments, Chief Counsel's Office,
Attention: No. 2003-28. Facsimiles: Send facsimile transmissions to FAX
Number (202) 906-6518, Attention: No 2003-28. e-mail: Send e-mails to
regs.comments@ots.treas.gov, Attention: No. 2003-28, and include your
name and telephone number. Due to temporary disruptions in mail service
in the Washington, DC area, commenters are encouraged to send comments
by fax or e-mail, if possible.
FOR FURTHER INFORMATION CONTACT:
OCC: Corporate IRB guidance: Jim Vesely, National Bank Examiner,
Large Bank Supervision (202/874-5170 or james.vesely@occ.treas.gov);
AMA guidance: Tanya Smith, Senior International Advisor, International
Banking & Finance (202/874-4735 or tanya.smith@occ.treas.gov).
Board: Corporate IRB guidance: David Palmer, Supervisory Financial
Analyst, Division of Banking Supervision and Regulation (202/452-2904
or david.e.palmer@frb.gov); AMA guidance: T. Kirk Odegard, Supervisory
Financial Analyst, Division of Banking Supervision and Regulation (202/
530-6225 or thomas.k.odegard@frb.gov). For users of Telecommunications
Device for
[[Page 45950]]
the Deaf (``TDD'') only, contact 202/263-4869.
FDIC: Corporate IRB guidance and AMA guidance: Pete D. Hirsch,
Basel Project Manager, Division of Supervision and Consumer Protection
(202/898-6751 or phirsch@fdic.gov).
OTS: Corporate IRB guidance and AMA guidance: Michael D. Solomon,
Senior Program Manager for Capital Policy (202/906-5654); David W.
Riley, Project Manager (202/906-6669), Supervision Policy; Teresa A.
Scott, Counsel (Banking and Finance) (202/906-6478); or Eric
Hirschhorn, Principal Financial Economist (202/906-7350), Regulations
and Legislation Division, Office of the Chief Counsel, Office of Thrift
Supervision, 1700 G Street, NW., Washington, DC 20552.
Document 1: Draft Supervisory Guidance on Internal Ratings-Based
Systems for Corporate Credit
Table of Contents
I. Introduction
A. Purpose
B. Overview of Supervisory Expectations
1. Ratings Assignment
2. Quantification
3. Data Maintenance
4. Control and Oversight Mechanisms
C. Scope of Guidance
D. Timing
II. Ratings for IRB Systems
A. Overview
B. Credit Ratings
1. Rating Assignment Techniques
a. Expert Judgment
b. Models
c. Constrained Judgment
C. IRB Ratings System Architecture
1. Two-Dimensional Rating System
a. Definition of Default
b. Obligor Ratings
c. Loss Severity Ratings
2. Other Considerations of IRB Rating System Architecture
a. Timeliness of Ratings
b. Multiple Ratings Systems
c. Recognition of the Risk Mitigation Benefits of Guarantees
3. Validation Process
a. Ratings System Developmental Evidence
b. Ratings System Ongoing Validation
c. Back Testing
III. Quantification of IRB Systems
A. Introduction
1. Stages of the Quantification Process
2. General Principles for Sound IRB Quantification
B. Probability of Default (PD)
1. Data
2. Estimation
3. Mapping
4. Application
C. Loss Given Default (LGD)
1. Data
2. Estimation
3. Mapping
4. Application
D. Exposure at Default (EAD)
1. Data
2. Estimation
3. Mapping
4. Application
E. Maturity (M)
F. Validation
Appendix to Part III: Illustrations of the Quantification Process
IV. Data Maintenance
A. Overview
B. Data Maintenance Framework
1. Life Cycle Tracking
2. Rating Assignment Data
3. Example Data Elements
C. Data Element Functions
1. Validation and Refinement
2. Developing Parameter Estimates
3. Applying Rating System Improvements Historically
4. Calculating Capital Ratios and Reporting to the Public
5. Supporting Risk Management
D. Managing data quality and integrity
1. Documentation and Definitions
2. Electronic Storage
3. Data Gaps
V. Control and Oversight Mechanisms
A. Overview
B. Independence in the Rating Approval Process
C. Transparency
D. Accountability
1. Responsibility for Assigning Ratings
2. Responsibility for Rating System Performance
E. Use of Ratings
F. Rating System Review (RSR)
G. Internal Audit
1. External Audit
H. Corporate Oversight
I. Introduction
A. Purpose
This document describes supervisory expectations for banking
organizations (institutions) adopting the advanced internal ratings-
based approach (IRB) for the determination of minimum regulatory risk-
based capital requirements. The focus of this guidance is corporate
credit portfolios. Retail, commercial real estate, securitizations, and
other portfolios will be the focus of later guidance. This draft
guidance should be considered with the advance notice of proposed
rulemaking (ANPR) on revisions to the risk-based capital standard
published elsewhere in today's Federal Register.
The primary objective of IRB is to enhance the sensitivity of
regulatory capital requirements to credit risk. To accomplish that
objective, IRB harnesses a bank's own risk rating and quantification
capabilities. In general, the IRB approach reflects and extends recent
developments in risk management and banking supervision. However, the
degree to which any individual bank will need to modify its own credit
risk management practices to deliver accurate and consistent IRB risk
parameters will vary from institution to institution.
This guidance is intended to provide supervisors and institutions
with a clear description of the essential components and
characteristics of an acceptable IRB framework. Toward that end, this
document sets forth IRB system supervisory standards that are
highlighted in bold and designated by the prefix ``S.'' Whenever
possible, these supervisory standards are principle-based to enable
institutions to implement the framework flexibly. However, when
prudential concerns or the need for standardization override the desire
for flexibility, the supervisory standards are more detailed.
Ultimately, institutions must have credit risk management practices
that are consistent with the substance and spirit of the standards in
this guidance.
The IRB conceptual framework outlined in this document is intended
neither to dictate the precise manner by which institutions should seek
to meet supervisory expectations, nor to provide technical guidance on
how to develop such a framework. As institutions develop their IRB
systems in anticipation of adopting them for regulatory capital
purposes, supervisors will be evaluating, on an individual bank basis,
the extent to which institutions meet the standards outlined in this
document. In evaluating institutions, supervisors will rely on this
supervisory guidance as well as examination procedures, which will be
developed separately. This document assumes that readers are familiar
with the proposed IRB approach to calculating minimum regulatory
capital articulated in the ANPR.
B. Overview of Supervisory Expectations
Rigorous credit risk measurement is a necessary element of advanced
risk management. Qualifying institutions will use their internal rating
systems to associate a probability of default (PD) with each obligor
grade, as well as a loss given default (LGD) with each credit facility.
In addition, institutions will estimate exposure at default (EAD) and
will calculate the effective remaining maturity (M) of credit
facilities.
Qualifying institutions will be expected to have an IRB system
consisting of four interdependent components:
[sbull] A system that assigns ratings and validates their accuracy
(Chapter 1),
[sbull] A quantification process that translates risk ratings into
IRB parameters (Chapter 2),
[sbull] A data maintenance system that supports the IRB system
(Chapter 3), and,
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[sbull] Oversight and control mechanisms that ensure the system is
functioning as intended and producing accurate ratings (Chapter 4).
Together these rating, quantification, data, and oversight
mechanisms present a framework for defining and improving the
evaluation of credit risk.
It is expected that rating systems will operate dynamically. As
ratings are assigned, quantified and used, estimates will be compared
with actual results and data will be maintained and updated to support
oversight and validation efforts and to better inform future estimates.
The rating system review and internal audit functions will serve as
control mechanisms that ensure that the process of ratings assignment
and quantification function according to policy and design and that
noncompliance and weaknesses are identified, communicated to senior
management and the board, and addressed. Rating systems with
appropriate data and oversight feedback mechanisms foster a learning
environment that promotes integrity in the rating system and continuing
refinement.
IRB systems need the support and oversight of the board and senior
management to ensure that the various components fit together
seamlessly and that incentives to make the system rigorous extend
across line, risk management, and other control groups. Without strong
board and senior management support and involvement, rating systems are
unlikely to provide accurate and consistent risk estimates during both
good and bad times.
The new regulatory minimum capital requirement is predicated on an
institution's internal systems being sufficiently advanced to allow a
full and accurate assessment of its risk exposures. Under the new
framework, an institution could experience a considerable capital
shortfall in the most difficult of times if its risk estimates are
materially understated. Consequently, the IRB framework demands a
greater level of validation work and controls than supervisors have
required in the past. When properly implemented, the new framework
holds the potential for better aligning minimum capital requirements
with the risk taken, pushing capital requirements higher for
institutions that specialize in riskier types of lending, and lower for
those that specialize in safer risk exposures.
Supervisors will evaluate compliance with the supervisory standards
for each of the four components of an IRB system. However, evaluating
compliance with each of the standards individually will not be
sufficient to determine an institution's overall compliance. Rather,
supervisors and institutions must also evaluate how well the various
components of an institution's IRB system complement and reinforce one
another to achieve the overall objective of accurate measures of risk.
In performing their evaluation, supervisors will need to exercise
considerable supervisory judgment, both in evaluating the individual
components and the overall IRB framework. A summary of the key
supervisory expectations for each of the IRB components follows.
Ratings Assignment
The first component of an IRB system involves the assignment and
validation of ratings (see Chapter 1). Ratings must be accurately and
consistently applied to all corporate credit exposures and be subject
to initial and ongoing validation. Institutions will have latitude in
designing and operating IRB rating systems subject to five broad
standards:
Two-dimensional risk-rating system--IRB institutions must be able
to make meaningful and consistent differentiations among credit
exposures along two dimensions--obligor default risk and loss severity
in the event of a default.
Rank order risks--IRB institutions must rank obligors by their
likelihood of default, and facilities by the loss severity expected in
default.
Calibration--IRB obligor ratings must be calibrated to values of
the probability of default (PD) parameter and loss severity ratings
must be calibrated to values of the loss given default (LGD) parameter.
Accuracy--Actual long-run actual default frequencies for obligor
rating grades must closely approximate the PDs assigned to those grades
and realized loss rates on loss severity grades must closely
approximate the LGDs assigned to those grades.
Validation process--IRB institutions must have ongoing validation
processes for rating systems that include the evaluation of
developmental evidence, process verification, benchmarking, and the
comparison of predicted parameter values to actual outcomes (back-
testing).
Quantification
The second component of an IRB system is a quantification process
(see Chapter 2). Since obligor and facility ratings may be assigned
separately from the quantification of the associated PD and LGD
parameters, quantification is addressed as a separate process. The
quantification process must produce values not only for PD and LGD but
also for EAD and for the effective remaining maturity (M). The
quantification of those four parameters is expected to be the result of
a disciplined process. The key considerations for effective
quantification are as follows:
Process--IRB institutions must have a fully specified process
covering all aspects of quantification (reference data, estimation,
mapping, and application).
Documentation--The quantification process, including the role and
scope of expert judgment, must be fully documented and updated
periodically.
Updating--Parameter estimates and related documentation must be
updated regularly.
Review--A bank must subject all aspects of the quantification
process, including design and implementation, to an appropriate degree
of independent review and validation.
Constraints on Judgment--Judgmental adjustments may be an
appropriate part of the quantification process, but must not be biased
toward lower risk estimates.
Conservatism--Parameter estimates must incorporate a degree of
conservatism that is appropriate for the overall robustness of the
quantification process.
Data Maintenance
The third component of an IRB system is an advanced data management
system that produces credible and reliable risk estimates (see Chapter
3). The broad standard governing an IRB data maintenance system is that
it supports the requirements for the other IRB system components, as
well as the institution's broader risk management and reporting needs.
Institutions will have latitude in managing their data, subject to the
following key data maintenance standards:
Life Cycle Tracking--Institutions must collect, maintain, and
analyze essential data for obligors and facilities throughout the life
and disposition of the credit exposure.
Rating Assignment Data--Institutions must capture all significant
quantitative and qualitative factors used to assign the obligor and
loss severity rating.
Support of IRB System--Data collected by institutions must be of
sufficient depth, scope, and reliability to:
[sbull] Validate IRB system processes,
[sbull] Validate parameters,
[sbull] Refine the IRB system,
[sbull] Develop internal parameter estimates,
[sbull] Apply improvements historically,
[sbull] Calculate capital ratios,
[sbull] Produce internal and public reports, and
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[sbull] Support risk management.
Control and Oversight Mechanisms
The fourth component of an IRB system is comprised of control and
oversight mechanisms that ensure that the various components of the IRB
system are functioning as intended (see Chapter 4). Given the various
uses of internal risk ratings, including their direct link to
regulatory capital requirements, there is enormous, sometimes
conflicting, pressure on banks' internal rating systems. Control
structures are subject to the following broad standards:
Interdependent System of Controls--IRB institutions must implement
a system of interdependent controls that include the following
elements:
[sbull] Independence,
[sbull] Transparency,
[sbull] Accountability,
[sbull] Use of ratings,
[sbull] Rating system review,
[sbull] Internal audit, and
[sbull] Board and senior management oversight.
Checks and Balances--Institutions must combine the various control
mechanisms in a way that provides checks and balances for ensuring IRB
system integrity.
The system of oversight and controls required for an effective IRB
system may operate in various ways within individual institutions. This
guidance does not prescribe any particular organizational structure for
IRB oversight and control mechanisms. Banks have broad latitude to
implement structures that are most effective for their individual
circumstances, as long as those structures support and enhance the
institution's ability to satisfy the supervisory standards expressed in
this document.
C. Scope of Guidance
This draft guidance reflects work performed by supervisors to
evaluate and compare current practices at institutions with the
concepts and requirements for an IRB framework. For instances in which
a range of practice was observable, examples are provided on how
certain practices may or may not qualify. However, in many other
instances, practices were at such an early stage of development that it
was not feasible to describe specific examples. In those cases,
requirements tend to be principle-based and without examples. Given
that institutions are still in the early stages of developing
qualifying IRB systems, it is expected that this guidance will evolve
over time to more explicitly take into account new and improving
practices.
D. Timing
S. An IRB system must be operating fully at least one year prior to
the institution's intended start date for the advanced approach.
As noted in the ANPR, the significant challenge of implementing a
fully complying IRB system requires that institutions and supervisors
have sufficient time to observe whether the IRB system is delivering
risk-based capital figures with a high level of integrity. The ability
to observe the institution's ratings architecture, validation, data
maintenance and control functions in a fully operating environment
prior to implementation will help identify how well the IRB system
design functions in practice. This will be particularly important given
that in the first year of implementation institutions will not only be
subject to the new minimum capital requirements, but will also be
disclosing risk-based capital ratios for the public to rely upon in the
assessment of the institution's financial health.
II. Ratings for IRB Systems
A. Overview
This chapter describes the design and operation of risk-rating
systems that will be acceptable in an internal ratings-based (IRB)
framework. Banks will have latitude in designing and operating IRB
rating systems, subject to five broad standards:
Two-dimensional risk-rating system--IRB institutions must be able
to make meaningful and consistent differentiations among credit
exposures along two dimensions--obligor default risk and loss severity
in the event of a default.
Rank order risks--IRB institutions must rank obligors by their
likelihood of default, and facilities by the loss severity expected in
default.
Calibration--IRB obligor ratings must be calibrated to values of
the probability of default (PD) parameter and loss severity ratings
must be calibrated to values of the loss given default (LGD) parameter.
Accuracy--Actual long-run actual default frequencies for obligor
rating grades must closely approximate the PDs assigned to those grades
and actual loss rates on loss severity grades must closely approximate
the LGDs assigned to those grades.
Validation process--IRB institutions must have ongoing validation
processes for rating systems that include the evaluation of
developmental evidence, process verification, benchmarking, and the
comparison of predicted parameter values to actual outcomes (back-
testing).
B. Credit Ratings
In general, a credit rating is a summary indicator of the relative
risk on a credit exposure. Credit ratings can take many forms. The most
widely known credit ratings are the public agency ratings, which are
expressed as letters; bank internal ratings tend to be expressed as
whole numbers--for example, 1 through 10. Some rating model outputs are
expressed in terms of probability of default or expected default
frequency, in which case they may be more than relative measures of
risk. Regardless of the form, meaningful credit ratings share two
characteristics:
[sbull] They group credits to discriminate among possible outcomes.
[sbull] They rank the perceived levels of credit risk.
Banks have used credit ratings of various types for a variety of
purposes. Some ratings are intended to rank obligors by risk of default
and some are intended to rank facilities\1\ by expected loss, which
incorporates risk of default and loss severity. Bank rating systems
that are geared solely to expected loss will need to be amended to meet
the two-dimensional requirements of the IRB approach.
Rating Assignment Techniques
Banks use different techniques, such as expert judgment and models,
to assign credit risk ratings. For banks using the IRB approach, how
ratings are assigned is important because different techniques will
require different validation processes and control mechanisms to ensure
the integrity of the rating system. To assist the discussion of rating
architecture requirements, described below are some of the current
rating assignment techniques. Any of these techniques--expert judgment,
models, constrained judgment, or a combination thereof--could be
acceptable within an IRB system, provided the bank meets the standards
outlined in this document.
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\1\ Facilities--loans, lines, or other separate extensions of
credit to an obligor.
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Expert Judgment
Historically, banks have used expert judgment to assign ratings to
commercial credits. With this technique, an individual weighs relevant
information and reaches a conclusion about the appropriate risk rating.
Presumably, the rater makes informed judgments based on knowledge
gained through experience and training.
[[Page 45953]]
The key feature of expert-judgment systems is flexibility. The
prevalence of judgmental rating systems reflects the view that the
determinants of default are too complicated to be captured by a single
quantitative model. The quality of management is often cited as an
example of a risk determinant that is difficult to assess through a
quantitative model. In order to foster internal consistency, banks
employing expert judgment rating systems typically provide narrative
guidelines that set out ratings criteria. However, the expert must
decide how narrative guidelines apply to a given set of circumstances.
The flexibility possible in the assignment of judgmental ratings
has implications for the types of ratings review that are feasible. As
part of the ratings validation process, banks will attempt to confirm
that raters follow bank policy. However, two individuals exercising
judgment can use the same information to support different ratings.
Thus, the review of an expert judgment rating system will require an
expert who can identify the impact of policy and the impact of judgment
on a rating.
Models
In recent years, models have been developed for use in rating
commercial credits. In a model-based approach, inputs are numeric and
provide quantitative and qualitative information about an obligor. The
inputs are combined using mathematical equations to produce a number
that is translated into a categorical rating. An important feature of
models is that the rating is perfectly replicable by another party,
given the same inputs.
The models used in credit rating can be distinguished by the
techniques used to develop them. Some models may rely on statistical
techniques while others rely on expert-judgment techniques.
Statistical models. Statistically developed models are the result
of statistical optimization, in which well-defined mathematical
criteria are used to choose the model that has the closest fit to the
observed data. Numerous techniques can be used to build statistical
models; regression is one widely recognized example. Regardless of the
specific statistical technique, a knowledgeable independent reviewer
will have to exercise judgment in evaluating the reasonableness of a
model's development, including its underlying logic, the techniques
used to handle the data, and the statistical model building techniques.
Expert-derived models.\2\ Several banks have built rating models by
asking their experts to decide what weights to assign to critical
variables in the models. Drawing on their experience, the experts first
identify the observable variables that affect the likelihood of
default. They then reach agreement on the weights to be assigned to
each of the variables. Unlike statistical optimization, the experts are
not necessarily using clear, consistent criteria to select the weights
attached to the variables. Indeed, expert-judgment model building is
often a practical choice when there is not enough data to support a
statistical model building. Despite its dependence on expert judgment,
this method can be called model-based as long as the result--the
equation, most likely with linear weights--is used as the basis to rate
the credits. Once the equation is set, the model shares the feature of
replicability with statistically derived models. Generally, independent
credit experts use judgment to evaluate the reasonableness of the
development of these models.
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\2\ Some banks have developed credit rating models that they
refer to as ``scorecards,'' but they have used expert judgment to
derive the weights. While they are models, they are not scoring
models in the now conventional use of the term. In its conventional
use, the term scoring model is reserved for a rating model derived
using statistical techniques.
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Constrained Judgment
The alternatives just described present the extremes, but in
practice, many banks use rating systems that combine models with
judgment. Two approaches are common.
Judgmental systems with quantitative guidelines or model results as
inputs. Historically, the most common approach to rating has involved
individuals exercising judgment about risks, subject to policy
guidelines containing quantitative criteria such as minimum values for
particular financial ratios. Banks develop quantitative criteria to
guide individuals in assigning ratings, but often believe that those
criteria do not adequately reflect the information needed to assign a
rating.
One version of this constrained judgment approach features a model
output as one among several criteria that an individual may consider in
assigning ratings. The individual assigning the rating is responsible
for prioritizing the criteria, reconciling conflicts between criteria,
and if warranted, overriding some criteria. Even if individuals
incorporate model results as one of the factors in their ratings, they
will exercise judgment in deciding what weight to attach to the model
result. The appeal of this approach is that the model combines many
pieces of information into a single output, which simplifies analysis,
while the rater retains flexibility regarding the use of the model
output.
Model-based ratings with judgmental overrides. When banks use
rating models, individuals are generally permitted to override the
results under certain conditions and within tolerance levels for
frequency. Credit-rating systems in which individuals can override
models raise many of the same issues presented separately by pure
judgment and model-based systems. If overrides are rare, the system can
be evaluated largely as if it is a model-based system. If, however,
overrides are prevalent, the system will be evaluated more like a
judgmental system.
Since constrained judgment systems combine features of both expert
judgment and model-based systems, their evaluation will require the
skills required to evaluate both of these other systems.
C. IRB Ratings System Architecture
Two-Dimensional Rating System
S. IRB risk rating systems must have two rating dimensions--obligor
and loss severity ratings.
S. IRB obligor and loss severity ratings must be calibrated to
values of the probability of default (PD) and the loss given default
(LGD), respectively.
Regardless of the type of rating system(s) used by an institution,
the IRB approach imposes some specific requirements. The first
requirement is that an IRB rating system must be two-dimensional. Banks
will assign obligor ratings, which will be associated with a PD. They
will also either assign a loss severity rating, which will be
associated with LGD values, or directly assign LGD values to each
facility. The process of assigning the obligor and loss severity
ratings--hereafter referred to as the rating system--is discussed
below, and the process of calibrating obligor and loss severity ratings
to PD and LGD parameters is discussed in Chapter 2.
S. Banks must record obligor defaults in accordance with the IRB
definition of default.
Definition of Default
The consistent identification of defaults is fundamental to any IRB
rating system. For IRB purposes, a default is considered to have
occurred with regard to a particular obligor when either or both of the
two following events have taken place:
[sbull] The obligor is past due more than 90 days on any material
credit
[[Page 45954]]
obligation to the banking group. Overdrafts will be considered as being
past due once the customer has breached an advised limit or been
advised of a limit smaller than current outstandings.
[sbull] The bank considers that the obligor is unlikely to pay its
credit obligations to the banking group in full, without recourse by
the bank to actions such as liquidating collateral (if held).
Any obligor (or its underlying credit facilities) that meets one or
more of the following conditions is considered unlikely to pay and
therefore in default:
[sbull] The bank puts the credit obligation on non-accrual status.
[sbull] The bank makes a charge-off or account-specific provision
resulting from a significant perceived decline in credit quality
subsequent to the bank taking on the exposure.
[sbull] The bank sells the credit obligation at a material credit-
related economic loss.
[sbull] The bank consents to a distressed restructuring of the
credit obligation where this is likely to result in a diminished
financial obligation caused by the material forgiveness, or
postponement, of principal, interest or (where relevant) fees.
[sbull] The bank has filed for the obligor's bankruptcy or a
similar order in respect of the obligor's credit obligation to the
banking group.
[sbull] The obligor has sought or has been placed in bankruptcy or
similar protection where this would avoid or delay repayment of the
credit obligation to the banking group.
While most conditions of default currently are identified by bank
reporting systems, institutions will need to augment data capture
systems to collect those default circumstances that may not have been
traditionally identified. These include facilities that are current and
still accruing but where the obligor declared or was placed in
bankruptcy. They must also capture so called ``silent defaults''--
defaults when the loss on a facility was avoided by liquidating
collateral.
Loan sales on which a bank experiences a material loss due to
credit deterioration are considered a default. Material credit related
losses are defined as XX. (The agencies seek comment on how to define
``material'' loss in the case of loans sold at a discount). Banks
should ensure that they have adequate systems to identify such
transactions and to maintain adequate records so that reviewers can
assess the adequacy of the institution's decision-making process in
this area.
Obligor Ratings
S. Banks must assign discrete obligor grades.
While banks may use models to estimate probabilities of default for
individual obligors, the IRB approach requires banks to group the
obligors into discrete grades. Each obligor grade, in turn, must be
associated with a single PD.
S. The obligor-rating system must result in a ranking of obligors
by likelihood of default.
The proper operation of the obligor-rating system will feature a
ranking of obligors by likelihood of default. For example, if a bank
uses a rating system based on a 10-point scale, with 1 representing
obligors of highest financial strength and 10 representing defaulted
obligors, grades 2 through 9 should represent groups of ever-increasing
risk. In a rating system in which risk increases with the grade, an
obligor with a grade 4 is riskier than an obligor with a grade 2, but
need not be twice as risky.
S. Separate exposures to the same obligor must be assigned to the
same obligor rating grade.
As noted above, the IRB framework requires that the obligor rating
be distinct from the loss severity rating, which is assigned to the
facility. Collateral and other facility characteristics should not
influence the obligor rating. For example, in a 1-to-10 rating system,
where risk increases with the number grade, a defaulted borrower with a
fully cash-secured transaction should be rated a 10--defaulted--
regardless of the remote expectation of loss. Likewise, a borrower
whose financial condition warrants the highest investment grade rating
should be rated a 1 even if the bank's transactions are subordinate to
other creditors and unsecured. Since the rating is assigned to the
obligor and not the facility, separate exposures to the same obligor
must be assigned to the same obligor rating grade.
At the bottom of any IRB system rating scale is a default grade.
Once an obligor is considered to be in default for IRB purposes, that
obligor must be assigned a default grade until such time as its
financial condition and performance improve sufficiently to clearly
meet the bank's internal rating definition for one of its non-default
grades. Once an obligor is in default on any material credit obligation
to the subject bank, all of its facilities at that institution are
considered to be in default.
S. In assigning an obligor to a rating category, the bank must
assess the risk of obligor default over a period of at least one year.
S. Obligor ratings must reflect the impact of financial distress.
In assigning an obligor to a rating category, the bank must assess
the risk of obligor default over a period of at least one year. This
use of a one-year assessment horizon does not mean that a bank should
limit its consideration to outcomes for that obligor that are most
likely over that year; the rating must take into account possible
adverse events that might increase an obligor's likelihood of default.
Rating Philosophy--Decisions Underlying Ratings Architecture
S. Banks must adopt a ratings philosophy. Policy guidelines should
describe the ratings philosophy, particularly how quickly ratings are
expected to migrate in response to economic cycles.
S. A bank's capital management policy must be consistent with its
ratings philosophy in order to avoid capital shortfalls in times of
systematic economic stress.
In the IRB framework, banks assign obligors to groups that are
expected to share common default frequencies. That general description,
however, still leaves open different possible implementations,
depending on how the bank defines the set of possible adverse events
that the obligor might face. A bank must decide whether obligors are
grouped by expected common default frequency over the next year (a so-
called point-in-time rating system) or by an expected common default
frequency over a wider range of possible stress outcomes (a so-called
through-the-cycle rating system). Choosing between a point-in-time
system and a through-the-cycle system yields a rating philosophy.
In point in time rating systems, obligors are assigned to groups
that are expected to share a common default frequency in a particular
year. Point-in-time ratings change from year to year as borrowers'
circumstances change, including changes due to the economic
possibilities faced by the borrowers. Since the economic circumstances
of many borrowers reflect the common impact of the general economic
environment, the transitions in point-in-time ratings will reflect that
systematic influence. A Merton-style probability of default prediction
model is commonly believed to be an example of a point-in-time approach
to rating (although that may depend on the specific implementation of
the model).
Through-the-cycle rating systems do not ask the question, what is
the probability of default over the next year.
[[Page 45955]]
Instead, they assign obligors to groups that would be expected to share
a common default frequency if the borrowers in them were to experience
distress, regardless of whether that distress is in the next year.
Thus, as the descriptive title suggests, this rating philosophy
abstracts from the near-term economic possibilities and considers a
richer assessment of the possibilities. Like point-in-time ratings,
through the cycle ratings will change from year to year due to changes
in borrower circumstance. However, since this rating philosophy
abstracts from the immediate economic circumstance and considers the
implications of hypothetical stress circumstances, year to year
transitions in ratings will be less influenced by changes in the actual
economic environment. The ratings agencies are commonly believed to use
through-the-cycle rating approaches.
Current practice in many banks in the U.S. is to rate obligors
using an approach that combines aspects of both point-in-time and
through the cycle approaches. The explanation provided by banks that
combine those approaches is that they want rating transitions to
reflect the directional impact of changes in the economic environment,
but that they do not want all of the volatility in ratings associated
with a point-in-time approach.
Regardless of which ratings philosophy a bank chooses, an IRB bank
must articulate clearly its approach and the implications of that
choice. As part of the choice of rating philosophy, the bank must
decide whether the same ratings philosophy will be employed for all of
the bank's portfolios. And management must articulate the implications
that the bank's ratings philosophy has on the bank's capital planning
process. If a bank chooses a ratings philosophy that is likely to
result in ratings transitions that reflect the impact of the economic
cycle, its capital management policy must be designed to avoid capital
shortfalls in times of systematic economic stress.
Obligor-Rating Granularity
S. An institution must have at least seven obligor grades that
contain only non-defaulted borrowers and at least one grade to which
only defaulted borrowers are assigned.
The number of grades used in a rating system should be sufficient
to reasonably ensure that management can meaningfully differentiate
risk in the portfolio, without being so large that it limits the
practical use of the rating system. To determine the appropriate number
of grades beyond the minimum seven non-default grades, each institution
must perform its own internal analysis.
S. An institution must justify the number of obligor grades used in
its rating system and the distribution of obligors across those grades.
The mere existence of an exposure concentration in a grade (or
grades) does not, by itself, reflect weakness in a rating system. For
example, banks may focus on a particular type of lending, such as
asset-based lending, in which the borrowers may have similar default
risk. Banks with such focused lending activities may use close to the
minimum number of obligor grades, while banks with a broad range of
lending activities should have more grades. However, banks with a high
concentration of obligors in a particular grade are expected to perform
a thorough analysis that supports such a concentration.
A significant concentration within an obligor grade may be
suspected if the financial strength of the borrowers within that grade
varies considerably. If obligors seem unduly concentrated, then
management should ask themselves the following questions:
[sbull] Are the criteria for each grade clear? Those rating
criteria may be too vague to allow raters to make clear distinctions.
Ambiguity may be an issue throughout the rating scale or it may be
limited to the most commonly used ratings.
[sbull] How diverse are the obligors? That is how many market
segments (for example, large commercial, middle market, private
banking, small business, geography, etc.) are significantly represented
in the bank's borrower population? If a bank's commercial loan
portfolio is not concentrated in one market segment, its risk rating
distribution is not likely to be concentrated.
[sbull] How broad are the bank's internal rating categories
compared to those of other lenders? The bank may be able to learn
enough from publicly available information to adjust its rating
criteria.
Some banks use ``modifiers'' to provide more risk differentiation
to a given rating system. A risk rating modified with a plus, minus or
other indicator does not constitute a separate grade unless the bank
has developed a distinct rating definition and criteria for the
modified grade. In the absence of such distinctions, grades such as 5,
5+, and 5- are viewed as a single grade for regulatory capital purposes
regardless of the existence of the modifiers.
Loss Severity Ratings
S. Banks must rank facilities by the expected severity of the loss
upon default.
The second dimension of an IRB system is the loss severity rating,
which is calibrated to LGD. A facility's LGD estimate is the loss the
bank is likely to incur in the event that the obligor defaults, and is
expressed as a percentage of exposure at the time of default. LGD
estimates can be assigned either through the use of a loss severity
rating system or they can be directly assigned to each facility.
LGD analysis is still in very early stages of development relative
to default risk modeling. Academic research in this area is relatively
sparse, data are not abundant, and industry practice is still widely
varying and evolving. Given the lack of data and the lack of research
into LGD modeling, some banks are likely, as a first step, to segment
their portfolios by a handful of available characteristics and
determine the appropriate LGDs for those segments. Over time, banks'
LGD methodologies are expected to evolve. Long-standing banking
experience and existing research on LGD, while preliminary, suggests
that collateral values, seniority, industry, etc. are predictive of
loss severity.
S. Banks must have empirical support for LGD rating systems
regardless of whether they use an LGD grading system or directly assign
LGD estimates.
Whether a bank chooses to assign LGD values directly or,
alternatively, to rate facilities and then quantify the LGD for the
rating grades, the key requirement is that it will need to identify
facility characteristics that influence LGD. Each of the loss severity
rating categories must be associated with an empirically supported LGD
estimate. In much the same way an obligor-rating system ranks exposures
by the probability of default, a facility rating system must rank
facilities by the likely loss severity.
Regardless of the method used to assign LGDs (loss severity grades
or direct LGD estimation), data used to support the methodology must be
gathered systematically. For many banks, the quality and quantity of
data available to support the LGD estimation process will have an
influence on the method they choose.
Stress Condition LGDs
S. Loss severity ratings must reflect losses expected during
periods with a relatively high number of defaults.
Like obligor ratings, which group obligors by expected default
frequency, loss severity ratings assign facilities to groups that are
expected to experience a common loss severity. However, the different
treatment accorded to PD and LGD in the model used to calculate IRB
capital requirements mandates an
[[Page 45956]]
asymmetric treatment of obligor and loss severity ratings. Obligor
ratings assign obligors to groups that are expected to experience
common default frequencies across a number of years, some of which are
years of general economic stress and some of which are not. In
contrast, loss severity ratings (or estimates) must pertain to losses
expected during periods with a high number of defaults--particular
years that can be called stress conditions. For cases in which loss
severities do not have a material degree of cyclical variability, use
of a long-run default weighted average is appropriate, although stress
condition LGD generally exceeds these averages.
Loss Severity Rating/LGD Granularity
S. Banks must have a sufficiently fine loss severity grading system
or prediction model to avoid grouping facilities with widely varying
LGDs together.
While there is no stated minimum number of loss severity grades,
the systems that provide LGD estimates must be flexible enough to
adequately segment facilities with significantly varying LGDs. Banks
should have a sufficiently fine LGD grading system or LGD prediction
model to avoid grouping facilities with widely varying LGDs together.
For example, a bank using a loss severity rating-scale approach that
has credit products with a variety of collateral packages or financing
structures would be expected to have more LGD grades than those
institutions with fewer options in their credit products.
Other Considerations of IRB Rating System Architecture
Timeliness of Ratings
S. All risk ratings must be updated whenever new relevant
information is received, but must be updated at least annually.
A bank must have a policy that requires a dynamic ratings approach
ensuring that obligor and loss severity ratings reflect current
information. That policy must also specify minimum financial reporting
and collateral valuation requirements. For example, at the time of
servicing events, banks typically receive updated financial information
on obligors. For cases in which loss severity grades or estimates are
dependent on collateral values or other factors that change
periodically, that policy must take into account the need to update
these factors.
Banks' policies may include an alternative rating update timetable
for exposures below a de minimus amount that is justified by the lack
of materiality of the potential impact on capital. For example, some
banks use triggering events to prompt an update of their ratings on de
minimus exposures rather than adhering to a specific timetable.
Multiple Ratings Systems
Some banks may develop one risk-rating system that can be used
across the entire commercial loan portfolio. However, a bank can choose
to deploy any number of rating systems as long as all exposures are
assigned PD and LGD values. A different rating system could be used for
each business line and each rating system could use a different rating
scale. A bank could also use a different rating system for each
business line with each system using a common rating scale. Rating
models could be used for some portfolios and expert judgment systems
for others. An institution's complexity and sophistication, as well as
the size and range of products offered, will affect the types and
numbers of rating systems employed.
While using a number of rating systems is feasible, such a practice
might make it more difficult to meet supervisory standards. Each rating
system must conform to the standards in this guidance and must be
validated for accuracy and consistency. The requirement that each
rating systems be calibrated to parameter values imposes the ultimate
constraint, which is that ratings be applied consistently.
Recognition of the Risk Mitigation Benefits of Guarantees
S. Banks reflecting the risk-mitigating effect of guarantees must
do so by either adjusting PDs or LGDs, but not both.
S. To recognize the risk-mitigating effects of guarantees,
institutions must ensure that the written guarantee is evidenced by an
unconditional and legally enforceable commitment to pay that remains in
force until the debt is satisfied in full.
Adjustments for guarantees must be made in accordance with specific
criteria contained in the bank's credit policy. The criteria should be
plausible and intuitive, and should address the guarantor's ability and
willingness to meet its obligations. Banks are expected to gather
evidence that confirms the risk-mitigating effect of guarantees.
Other forms of written third-party support (for example, comfort
letters or letters of awareness) that are not legally binding should
not be used to adjust PD or LGD unless a bank can demonstrate through
analysis of internal data the risk-mitigating effect of such support.
Banks may not adjust PDs or LGDs to reflect implied support or verbal
assurances.
Regardless of the method used to recognize the risk-mitigating
effects of guarantees, a bank must adopt an approach that is applied
consistently over time and across the portfolio. Moreover, the onus is
on the bank to demonstrate that its approach is supported by logic and
empirical results. While guarantees may provide grounds for adjusting
PD or LGD, they cannot result in a lower risk weight than that assigned
to a similar direct obligation of the guarantor.\3\
---------------------------------------------------------------------------
\3\ The probability that an obligor and a guarantor (who
supports the obligor's debt) will both default on a debt is lower
than the probability that either the obligor or the guarantor will
default. This favorable risk-mitigation effect is known as the
reduced likelihood of ``double default.'' In determining their
rating criteria and procedures, banks are not permitted to consider
possible favorable effects of imperfect expected correlation between
default events for the borrower and guarantor for purposes of
regulatory capital requirements. Thus, the adjusted risk weight
cannot reflect the risk mitigation of double default. The ANPR
solicits public comment on the double-default issues.
---------------------------------------------------------------------------
Validation Process
S. IRB rating system architecture must be designed to ensure rating
system accuracy.
As part of their IRB rating system architecture, banks must
implement a process to ensure the accuracy of their rating systems.
Rating system accuracy is defined as the combination of the following
outcomes:
[sbull] The actual long-run average default frequency for each
rating grade is not significantly greater than the PD assigned to that
grade.
[sbull] The actual stress-condition loss rates experienced on
defaulted facilities are not significantly greater than the LGD
estimates assigned to those facilities.
Some differences across individual grades between observed outcomes
and the estimated parameter inputs to the IRB equations can be
expected. But if systematic differences suggest a bias toward lowering
regulatory capital requirements, the integrity of the rating system (of
either the PD or LGD dimensions or of both) becomes suspect. Validation
is the set of activities designed to give the greatest possible
assurances of ratings system accuracy.
S. Banks must have ongoing validation processes that include the
review of developmental evidence, ongoing monitoring, and the
comparison of predicted parameter values to actual outcomes (back-
testing).
Validation is an integral part of the rating system architecture.
Banks must have processes designed to give
[[Page 45957]]
reasonable assurances of their rating systems' accuracy. The ongoing
process to confirm and ensure rating system accuracy consists of:
[sbull] The evaluation of developmental evidence,
[sbull] Ongoing monitoring of system implementation and
reasonableness (verification and benchmarking), and
[sbull] Back-testing (comparing actual to predicted outcomes).
IRB institutions are expected to employ all of the components of
this process. However, the data to perform comprehensive back-testing
will not be available in the early stages of implementing an IRB rating
system. Therefore, banks will have to rely more heavily on
developmental evidence, quality control tests, and benchmarking to
assure themselves and other interested parties that their rating
systems are likely to be accurate. Since the time delay before rating
systems can be back-tested is likely to be an important issue--because
of the rarity of defaults in most years and the bunching of defaults in
a few years--the other parts of the validation process will assume
greater importance. If rating processes are developed in a learning
environment in which banks attempt to change and improve ratings, back
testing may be delayed even further. Validation in its early stages
will depend on bank management's exercising informed judgment about the
likelihood of the rating system working--not simply on empirical tests.
Ratings System Developmental Evidence
The first source of support for the validity of a bank's rating
system is developmental evidence. Evaluating developmental evidence
involves making a reasonable assessment of the quality of the rating
system by analyzing its design and construction. Developmental evidence
is intended to answer the question, Could the rating system be expected
to work reasonably if it is implemented as designed? That evidence will
have to be revisited whenever the bank makes a change to its rating
system. If a bank adopts a rating system and does not make changes,
this step will not have to be revisited. However, since rating systems
are likely to change over time as the bank learns about the
effectiveness of the system and incorporates the results of those
analyses, the evaluation of developmental evidence is likely to be an
ongoing part of the process. The particular steps taken in evaluating
developmental evidence will depend on the type of rating system.
Generally, the evaluation of developmental evidence will include a
body of expert opinion. For example, developmental evidence in support
of a statistical rating model must include information on the logic
that supports the model and an analysis of the statistical model-
building techniques. In contrast, developmental evidence in support of
a constrained-judgment system that features guidance values of
financial ratios might include a description of the logic and evidence
relating the values of the ratios to past default and loss outcomes.
Regardless of the type of rating system, the developmental evidence
will be more persuasive when it includes empirical evidence on how well
the ratings might have worked in the past. This evidence should be
available for a statistical model since such models are chosen to
maximize the fit to outcomes in the development sample. In addition,
statistical models should be supported by evidence that they work well
outside the development sample. Use of ``holdout'' sample evidence is a
good model-building practice to ensure that the model is not merely a
statistical quirk of the particular data set used to build the model.
Empirical developmental evidence of rating effectiveness will be
more difficult to produce for a judgmental rating system. Such evidence
would require asking raters how they would have rated past credits for
which they did not know the outcomes. Those retrospective ratings could
then be compared to the outcomes to determine whether the ratings were
correct on average. Conducting such tests, however, will be difficult
because historical data sets may not include all of the information
that an individual would have actually used in making a judgment about
a rating.
The sufficiency of the developmental evidence will itself be a
matter of informed expert opinion. Even if the rating system is model-
based, an evaluation of developmental evidence will entail judging the
merits of the model-building technique. Although no bright line tests
are feasible because expert judgment is essential to the evaluation of
rating system development, experts will be able to draw conclusions
about whether a well-implemented system would be likely to perform
satisfactorily.
Ratings System Ongoing Validation
The second source of analytical support for the validity of a bank
rating system is the ongoing analysis intended to confirm that the
rating system is being implemented and continues to perform as
intended. Such analysis involves process verification and benchmarking.
Process Verification
Verification activities address the question, Are the ratings being
assigned as intended? Specific verification activities will depend on
the rating approach. If a model is used for rating, verification
analysis begins by confirming that the computer code used to deploy the
model is correct. The computer code can be verified in a number of
established ways. For example, a qualified expert can duplicate the
code or check the code line by line. Process verification for a model
will also include confirmation that the correct data are being used in
the model.
For expert-judgment and constrained-judgment systems, verification
requires other individual reviewers to evaluate whether the rater
followed rating policy. The primary requirements for verification of
ratings assigned by individuals are:
[sbull] A transparent rating process,
[sbull] A database with information used by the rater, and
[sbull] Documentation of how the decisions were made.
The specific steps will depend on how much the process incorporates
specific guidelines and how much the exercise of judgment is allowed.
As the dependence on specific guidelines increases, other individuals
can more easily confirm that guidelines were followed by reference to
sufficient documentation. As the dependence on judgment rises, the
ratings review function will have to be staffed increasingly by experts
with appropriate skills and knowledge about the rating policies of the
bank.
Ratings process verification also includes override monitoring. If
individuals have the ability to override either models or policies in a
constrained-judgment system, the bank should have both a policy stating
the tolerance for overrides and a monitoring system for identifying the
occurrence of overrides. A reporting system capturing data on reasons
for overrides will facilitate learning about whether overrides improve
accuracy.
Benchmarking
S. Banks must benchmark their internal ratings against internal,
market and other third-party ratings.
Benchmarking is the set of activities that uses alternative tools
to draw inferences about the correctness of ratings before outcomes are
actually
[[Page 45958]]
known. The most important type of benchmarking of a rating system is to
ask whether another rater or rating method attaches the same rating to
a particular obligor or facility. Regardless of the rating approach,
the benchmark can be either a judgmental or a model-based rating.
Examples of such benchmarking include:
[sbull] Ratings reviewers who completely re-rate a sample of
credits rated by individuals in a judgmental system.
[sbull] An internally developed model is used to rate credits rated
earlier in a judgmental system.
[sbull] Individuals rate a sample of credits rated by a model.
[sbull] Internal ratings are compared against results from external
agencies or external models.
Because it will take considerable time before outcomes will be
available, using alternative ratings as benchmarks will be a very
important validation device. Such benchmarking must be applied to all
rating approaches, and the benchmark can be either a model or judgment.
At a minimum, banks must establish a process in which a representative
sample of its internal ratings is compared to third-party ratings
(e.g., independent internal raters, external rating agencies, models,
or other market data sources) of the same credits.
Benchmarking also includes activities designed to draw broader
inferences about whether the rating system--as opposed to individual
ratings--is working as expected. The bank can look for consistency in
ranking or consistency in the values of rating characteristics for
similarly rated credits. Examples of such benchmarking activities
include:
[sbull] Analyzing the characteristics of obligors that have
received common ratings.
[sbull] Monitoring changes in the distribution of ratings over
time.
[sbull] Calculating a transition matrix calculated from changes in
ratings in a bank's portfolio and comparing it to historical transition
matrices from internal bank data or publicly available ratings.
While benchmarking activities allow for inferences about the
correctness of the ratings system, they are the not same thing as back-
testing. The benchmark itself is a prediction and may be in error. If
benchmarking evidence suggests a pattern of rating differences, it
should lead the bank to investigate the source of the differences.
Thus, the benchmarking process illustrates the possibility of feedback
from ongoing validation to model development, underscoring the
characterization of validation as a process.
Back Testing
S. Banks must develop statistical tests to back-test their IRB
rating systems.
S. Banks must establish internal tolerance limits for differences
between expected and actual outcomes.
S. Banks must have a policy that requires remedial actions be taken
when policy tolerances are exceeded.
The third component of a validation process is back-testing, which
is the comparison of predictions with actual outcomes. Back-testing of
IRB systems is the empirical test of the accuracy of the parameter
values, PD and LGD, associated with obligor and loss severity ratings,
respectively. For IRB rating systems, back-testing addresses the
combined effectiveness of the assignment of obligor and loss severity
ratings and the calibration of the parameters PD and LGD attached to
those ratings.
At this time, there is no generally agreed-upon statistical test of
the accuracy of IRB systems. Banks must develop statistical tests to
back-test their IRB rating systems. In addition, banks must have a
policy that specifies internal tolerance limits for comparing back-
testing results. Importantly, that policy must outline the actions that
would be taken whenever policy limits are exceeded.
As a combined test of ratings effectiveness, back-testing is a
conceptual bridge between the ratings system architecture discussed in
this chapter and the quantification of parameters, discussed in Chapter
2. The final section of Chapter 2 discusses back-testing as one type of
quantitative test required to validate the quantification of parameter
values.
III. Quantification of IRB Systems
Ratings quantification is the process of assigning numerical values
to the four key components for internal ratings-based assessments of
credit-risk capital: probability of default (PD), the expected loss
given default (LGD), the expected exposure at default (EAD), and
maturity (M). Section I establishes an organizing framework for
considering IRB quantification and develops general principles that
apply to the entire process. Sections II through IV cover specific
principles or supervisory standards that apply to PD, LGD, and EAD
respectively. The maturity component, which is much less dependent on
statistical estimates and the use of data, receives somewhat different
treatment in section V. Validation of the quantification process is
covered in section VI.
A. Introduction
Stages of the Quantification Process
With the exception of maturity, the risk components are
unobservable and must be estimated. The estimation must be consistent
with sound practice and supervisory standards. In addition, a bank must
have processes to ensure that these estimates remain valid.
Calculation of risk components for IRB involves two sets of data:
the bank's actual portfolio data, consisting of current credit
exposures assigned to internal grades, and a ``reference data set,''
consisting of a set of defaulted credits (in the case of LGD and EAD
estimation) or both defaulted and non-defaulted credits (in the case of
PD estimation). The bank estimates a relationship between the reference
data set and probability of default, loss severity, or exposure; then
this estimated relationship is applied to the actual portfolio data for
which capital is being assessed.
Quantification proceeds through four logical stages: obtaining
reference data; estimating the reference data's relationship to the
parameters; mapping the correspondence between the reference data and
the portfolio's data; and applying the relationship between reference
data and parameters to the portfolio's data. (Readers may find it
helpful to refer to the appendix to this chapter, which illustrates how
this four-stage framework can be applied to ratings quantification
approaches in practice.) An evaluation of any bank's IRB quantification
process focuses on understanding how the bank implements each stage for
each of the key parameters, and on assessing the adequacy of the bank's
approach.
Data--First, the bank constructs a reference data set, or source of
data, from which parameters can be estimated.
Reference data sets include internal data, external data, and
pooled internal/external data. Important considerations include the
comparability of the reference data to the current credit portfolio,
whether the sample period ``appropriately'' includes periods of stress,
and the definition of default used in the reference data. The reference
data must be described using a set of observed characteristics;
consequently, the data set must contain variables that can be used for
this characterization. Relevant characteristics might include external
debt ratings, financial measures, geographic regions, or any other
factors that are believed to be
[[Page 45959]]
related in some way to PD, LGD, or EAD. More than one reference data
set may be used.
Estimation--Second, the bank applies statistical techniques to the
reference data to determine a relationship between characteristics of
the reference data and the parameters (PD, LGD, or EAD).
The result of this step is a model that ties descriptive
characteristics of the obligor or facility in the reference data set to
PD, LGD, or EAD estimates. In this context, the term `models' is used
in the most general sense; a model may be simple, such as the
calculation of averages, or more complicated, such as an approach based
on advanced regression techniques. This step may include adjustments
for differences between the IRB definition of default and the default
definition in the reference data set, or adjustments for data
limitations. More than one estimation technique may be used to generate
estimates of the risk components, especially if there are multiple sets
of reference data or multiple sample periods.
Mapping--Third, the bank creates a link between its portfolio data
and the reference data based on common characteristics.
Variables or characteristics that are available for the current
portfolio must be mapped to the variables used in the default, loss-
severity, or exposure model. (In some cases, the bank constructs the
link for a representative exposure in each internal grade, and the
mapping is then applied to all credits within a grade.) An important
element of mapping is making adjustments for differences between
reference data sets and the bank's portfolio. The bank must create a
mapping for each reference data set and for each combination of
variables used in any estimation model.
Application--Fourth, the bank applies the relationship estimated
for the reference data to the actual portfolio data.
The ultimate aim of quantification is to attribute a PD, LGD, or
EAD to each exposure within the portfolio, or to each internal grade if
the mapping was done at the grade level. This step may include
adjustments to default frequencies or loss rates to ``smooth'' the
final parameter estimates. If the estimates are applied to individual
transactions, the bank must in some way aggregate the estimates at the
grade level. In addition, if multiple data sets or estimation methods
are used, the bank must adopt a means of combining the various
estimates.
A number of examples are given in this chapter to aid exposition
and interpretation. None of the examples is sufficiently detailed to
incorporate all the considerations discussed in this chapter. Moreover,
technical progress in the area of quantification is rapid. Thus, banks
should not interpret an example that is consistent with the standard
being discussed, and that resembles the bank's current practice, as
creation of a ``safe harbor'' or as an indication that the bank's
practice will be approved as-is. Banks should consider this guidance in
its entirety when determining whether systems and practices are
adequate.
General Principles for Sound IRB Quantification
Several core principles apply to all elements of the overall
ratings quantification process; those general principles are discussed
in this introductory section. Each of these principles is, in effect, a
supervisory standard for IRB systems. Other supervisory standards,
specific to particular elements or parameters, are discussed in the
relevant sections.
Supervisory evaluation of IRB quantification requires consideration
of all of these principles and standards, both general and specific.
Particular practical approaches to ratings quantification may be highly
consistent with some standards, and less so with others. In any
particular case, an ultimate assessment relies on the judgment of
supervisors to weigh the strengths and weaknesses of a bank's chosen
approach, using these supervisory standards as a guide.
S. IRB institutions must have a fully specified process covering
all aspects of quantification (reference data, estimation, mapping, and
application). The quantification process, including the role and scope
of expert judgment, must be fully documented and updated periodically.
A fully specified quantification process must describe how all four
stages (data, estimation, mapping, and application) are implemented for
each parameter. Documentation promotes consistency and allows third
parties to review and replicate the entire process. Examples of third
parties that might use the documentation include rating-system
reviewers, auditors, and bank supervisors. Periodic updates to the
process must be conducted to ensure that new data, analytical
techniques, and evolving industry practice are incorporated into the
quantification process.
S. Parameter estimates and related documentation must be updated
regularly.
The parameter estimates must be updated at least annually, and the
process for doing so must be documented in bank policy. The update
should also evaluate the judgmental adjustments embedded in the
estimates; new data or techniques may suggest a need to modify those
adjustments. Particular attention should be given to new business lines
or portfolios in which the mix of obligors is believed to have changed
substantially. A material merger, acquisition, divestiture, or exit
clearly raises questions about the continued applicability of the
process and should trigger an intensive review and updating.
The updating process is particularly relevant for the reference
data stage because new data become available all the time. New data
must be incorporated, into the PD, LGD, and EAD estimates, using a
well-defined process.
S. A bank must subject all aspects of the quantification process,
including design and implementation, to an appropriate degree of
independent review and validation.
An independent review is an assessment conducted by persons not
accountable for the work being reviewed. The reviewers may be either
internal or external parties. The review serves as a check that the
quantification process is sound and works as intended; it should be
broad-based, and must include all of the elements of the quantification
process that lead to the ultimate estimates of PD, LGD, and EAD. The
review must cover the full scope of validation: evaluation of the
integrity of data inputs, analysis of the internal logic and
consistency of the process, comparison with relevant benchmarks, and
appropriate back-testing based on actual outcomes.
S. Judgmental adjustments may be an appropriate part of the
quantification process, but must not be biased toward lower estimates
of risk.
Judgment will inevitably play a role in the quantification process
and may materially affect the estimates. Judgmental adjustments to
estimates are often necessary because of some limitations on available
reference data or because of inherent differences between the reference
data and the bank's portfolio data. The bank must ensure that
adjustments are not biased toward optimistically low parameter
estimates for PD, LGD, and EAD. Individual assumptions are less
important than broad patterns; consistent signs of judgmental decisions
that lower parameter estimates materially may be evidence of bias.
[[Page 45960]]
The reasoning and empirical support for any adjustments, as well as
the mechanics of the calculation, must be documented. The bank should
conduct sensitivity analysis to demonstrate that the adjustment
procedure is not biased toward reducing capital requirements. The
analysis must consider the impact of any judgmental adjustments on
estimates and risk weights, and must be fully documented.
S. Parameter estimates must incorporate a degree of conservatism
that is appropriate for the overall robustness of the quantification
process.
In estimating values of PD, LGD, and EAD should be as precise and
accurate as possible. However, estimates of PD, LGD and EAD are
statistics, and thus inherently subject to uncertainty and potential
error. It is often possible to be reasonably confident that a risk
component or other parameter lies within a particular range, but
greater precision is difficult to achieve. Aspects of the ratings
quantification process that are apt to introduce uncertainty and
potential error include the following:
The estimation of coefficients of particular variables in a
regression-based statistical default or severity model.
[sbull] The calculation of average default or loss rates for
particular categories of credits in external default databases.
[sbull] The mapping between portfolio obligors or facilities and
reference data when the set of common characteristics does not align
exactly.
A general principle of the IRB approach is that a bank must adjust
estimates conservatively in the presence of uncertainty or potential
error. In many cases this corresponds to assigning a final parameter
estimate that increases required capital relative to the best estimate
produced through sound-practice estimation techniques. The extent of
this conservative adjustment should be related to factors such as the
relevance of the reference data, the quality of the mapping, the
precision of the statistical estimates, and the amount of judgment used
throughout the process. Margins of conservatism need not be added at
each step; indeed, that could produce an excessively conservative
result. The overall margin of conservatism should adequately account
for all uncertainties and weaknesses; this is the general
interpretation of requirements to incorporate appropriate degrees of
conservatism. Improvements in the quantification process (use of better
data, estimation techniques, and so on) may reduce the appropriate
degree of conservatism over time.
Estimates of PD, LGD, EAD, or other parameters or coefficients
should be presented with an accompanying sense of the statistical
precision of the estimates; this facilitates an assessment of the
appropriate degree of conservatism.
B. Probability of Default (PD)
Data
To estimate PD accurately, a bank must have a comprehensive
reference data set with observations that are comparable to the bank's
current portfolio of obligors. Clearly, the data set used for
estimation should be similar to the portfolio to which such estimates
will be applied. The same comparability standard applies to both
internal and external data sets.
To ensure ongoing applicability of the reference data, a bank must
assess the characteristics of its current obligors relative to the
characteristics of obligors in the reference data. Such variables might
include qualitative and quantitative obligor information, internal and
external rating, rating dates, and line of business or geography. To
this end, a bank must maintain documentation that fully describes all
explanatory variables in the data set, including any changes to those
variables over time. A well-defined and documented process must be in
place to ensure that the reference data are updated as frequently as is
practical, as fresh data become available or portfolio changes make
necessary.
S. The sample for the reference data must be at least five years,
and must include periods of economic stress during which default rates
were relatively high.
To foster more robust estimation, banks should use longer time
series when more than five years of data are available. However, the
benefits of using a longer time series (longer than five years) may
have to be weighed against a possible loss of data comparability. The
older the reference data, the less similar they are likely to be to the
bank's current portfolio; striking the correct balance is a matter of
judgment. Reference obligors must not differ from the current portfolio
obligors systematically in ways that seem likely to be related to
obligor default risk. Otherwise, the derived PD estimates may not be
applicable to the current portfolio.
Note that this principle does not simply restate the requirement
for five years of data: periods of stress during which default rates
are relatively high must be included in the data sample. Exclusion of
such periods biases PD estimates downward and unjustifiably lowers
regulatory capital requirements.
Example. A bank's reference data set covers the years 1987
through 2001. Each year includes identical data elements, and each
year is similarly populated. For its grade PD estimates, the bank
relies upon data from a sub-sample covering 1992 through 2001. The
bank provides no justification for dropping the years from 1987
through 1991. The bank contends that it is not necessary to include
those data, as the reference sample they use for estimation
satisfies the five-year requirement. This practice is not consistent
with the standard because the bank has not supported its decision to
ignore available data. The fact that the excluded years include a
recession would raise particular concerns.
S. The definition of default within the reference data must be
reasonably consistent with the IRB definition of default.
Regardless of the source of the reference data, a bank must apply
the same default definition throughout the quantification processes.
This fosters consistent estimation across parameters and reduces the
potential for undesired bias. In addition, consistent application of
the same definition across banks will permit true horizontal analysis
by supervisors and engaged market participants.
This standard applies to both internal and external reference data.
For internal data, a bank's default definition is expected to be
consistent with the IRB definition going forward. Banks will be
expected to make appropriate adjustments to their data systems such
that all defaults as defined for IRB are captured by the time a bank
fully implements its IRB system. For any historical or external data
that do not fully comply with the IRB definition of default, a bank
must make conservative adjustments to reflect such discrepancies.
Larger discrepancies require larger adjustments for conservatism.
Example. To identify defaults in its historical data, a bank
applies a consistent definition of ``placed on nonaccrual.'' This
definition is used in the bank's quantification exercises to
estimate PD, LGD, and EAD. The bank recognizes that use of the
nonaccrual definition fails to capture certain defaults as
identified in the IRB rules. Specifically, the bank indicates that
the following kinds of defaulted facilities would not have been
placed on nonaccrual: (1) Credit obligations that were sold at a
material credit-related economic loss, and (2) distressed
restructurings. To be consistent with the standard, the bank must
make a well-supported adjustment to its grade PD estimates to
reflect the difference in the default definitions.
Estimation
Estimation of PD is the process by which characteristics of the
reference
[[Page 45961]]
data are related to default frequencies.\4\ The relevant
characteristics that help to determine the likelihood of default are
referred to as ``drivers of default''. Drivers might include variables
such as financial ratios, management expertise, industry, and
geography.
---------------------------------------------------------------------------
\4\ The New Basel Capital Accord produced by the Basel Committee
on Banking Supervision discusses three techniques for PD estimation.
IRB banks are not constrained to select from among these three
techniques; they have broad flexibility to implement appropriate
approaches to quantification. The three Basel techniques are best
regarded not as a complete taxonomy of the possible approaches to PD
estimation, but rather as illustrations of a few of the many
possible approaches.
---------------------------------------------------------------------------
S. Estimates of default rates must be empirically based and must
represent a long-run average.
Estimates must capture average default experience over a reasonable
mix of high-default and low-default years of the economic cycle. The
average is labeled ``long-run'' because a long observation period would
span both peaks and valleys of the economic cycle. The emphasis should
not be on time-span; the long-run average concept captures the breadth,
not the length, of experience.
If the reference data are characterized by internal or external
rating grades, one estimation approach is to calculate the mean of one-
year realized default rates for each grade, giving equal weight to each
year's realized default rate. PD estimates generally should be
calculated in this manner.
Another approach is to pool obligors in a given grade over a number
of years and then calculate the mean default rate. In this case, each
year's default rate is weighted by the number of obligors. This
approach may underestimate default rates. For example, if lending
declines in recessions so that obligors are fewer in those years than
in others, weighting by number of obligors would dilute the effect of
the recession year on the overall mean. The obligor-weighted
calculation, or another approach, will be allowed only if the bank can
demonstrate that this approach provides a better estimate of the long-
run average PD. At a minimum, this would involve comparing the results
of both methods.
Statistical default prediction models may also play a role in PD
estimation. For example, the characteristics of the reference data
might include financial ratios or a distance-to-default measure, as
defined by a specific implementation of a Merton-style structural
model.
For a model-based approach to meet the requirement that ultimate
grade PD estimates be long-run averages, the reference data used in the
default model must meet the long-run requirement. For example, a model
can be used to relate financial ratios to likelihood of default based
on the outcome for the firms--default or non-default. Such a model must
be calibrated to capture the default experience over a reasonable mix
of good and bad years of the economic cycle. The same requirement would
hold for a structural model; distance to default must be calibrated to
default frequency using long-run experience. This applies to both
internal and vendor models, and a bank must verify that this
requirement is met.
Example 1. A bank uses external data from a rating agency to
estimate PD. The PD estimate for each agency grade is calculated as
the mean of yearly realized default rates over a time period (1980
through 2001) that includes several recessions and high-default
years. The bank provides support that this time period adequately
represents long-run experience. This illustrates an estimation
method that is consistent with the standard.
Example 2a. Like the institution in example 1, a bank maps
internal ratings to agency grades. The estimates for the agency
grades are set indirectly, using the default probabilities from a
default prediction model. The bank does so because although it links
internal and agency grades, the bank views the default model's
results as more predictive than the historical agency default
experience. For each agency grade, the bank calculates a PD estimate
as the mean of the model-based default probabilities for the agency-
rated obligors. In order to meet the long-run requirement, the bank
calculates the estimates over the seven years from 1995 through
2001. The bank demonstrates that this time period includes a
reasonable mix of high-default and low-default experience. This
estimation method is consistent with the standard.
Example 2b. In a variant of example 2a, a bank uses the mean
default frequency per agency rating grade for a single year, such as
2001. Empirical evidence shows that the mean default frequency for
agency grades varies substantially from year to year. A single year
thus does not reflect the full range of experience, because a long-
run average should be relatively stable year to year. Such
instability makes this estimation method unacceptable.
Example 2c. Another bank calculates the agency grade PD
estimates as the median default probability of companies in that
grade. The bank does so without demonstrating that the median is a
better statistical estimator than the mean. This estimation method
is not consistent with the standard. A median gives less weight to
obligors with high estimated default probabilities than a simple
mean does. The difference between mean and median can be material
because distributions of credits within grades often are
substantially skewed toward higher default probabilities: the
riskier obligors within a grade tend to have individual default
probabilities that are substantially worse than the median, while
the least risky have default probabilities only somewhat better than
the median.
S. Judgmental adjustments may play an appropriate role in PD
estimation, but must not be biased toward lower estimates.
The following examples illustrate how supervisors will evaluate
adjustments:
Example 1. A bank uses the last five years of internal default
history to estimate grade PDs. However, they recognize that the
internal experience does not include any high-default years. In
order to remedy this and still take advantage of its experience, the
bank uses external agency data to adjust the estimates upward. Using
the agency data, the bank calculates the ratio between the long-run
average and the mean default rate per grade over the last five
years. The bank assumes that the relationship observed in the agency
data applies to its portfolio, and adjusts the estimates for the
internal data accordingly. This practice is consistent with the
standard.
Example 2. A bank uses internal default experience to estimate
grade PDs. However, the bank has historically failed to recognize
defaults when the loss on the default obligation was avoided by
seizing collateral. The bank makes no adjustment for such missing
defaults. The realized default rate using the more inclusive
definition would be higher than that observed by the bank (and loss
severity rates would be correspondingly lower). This practice would
not be consistent with the standard, unless the bank demonstrates
that the necessary adjustment is immaterial.
Mapping
Mapping is the process of establishing a correspondence between the
bank's current obligors and the reference obligor data used in the
default model. Hence, mapping involves identifying how default-related
characteristics of the current portfolio correspond to the
characteristics of reference obligors. Such characteristics might
include financial and nonfinancial variables, and assigned ratings or
grades.
Mapping can be thought of as taking each obligor in the bank's
portfolio and characterizing it as if it were part of the reference
data. There are two broad approaches to the mapping process:
Obligor mapping: Each portfolio obligor is mapped to the reference
data based on its individual characteristics. For example, if a bank
applies a default model, a default probability will be generated for
each obligor. That individual default probability is then used to
assign each obligor to a particular internal grade, based on the bank's
established criteria. To obtain a final estimate of the grade PD in the
subsequent application stage, the bank averages the default
probabilities of individual obligors within each grade.
Grade mapping: Characteristics of the obligors within an internal
grade are
[[Page 45962]]
averaged or otherwise summarized to construct a ``typical'' or
representative obligor for each grade. Then, the bank maps that
representative obligor to the reference data. For example, if the bank
uses a default model, the default probability associated with that
typical obligor will serve as the grade PD in the application stage.
Alternatively, the bank may map the typical obligor to a particular
external rating grade based on quantitative and qualitative
characteristics, and assign the long-run default rate for that rating
to the internal grade in the application stage.
Either grade mapping or obligor mapping can be part of the
quantification process; either method can produce a single PD estimate
for each grade in the application stage. However, in the absence of
other compelling considerations, banks should use obligor mapping for
two reasons:
[sbull] First, default probabilities are nonlinear under many
estimation approaches. As a result, the default probability of the
typical obligor--the result of a grade mapping approach--is often lower
than the mean of the individual obligor default probabilities from the
obligor mapping approach. For example, consider a bank that maps to the
S&P scale and uses historical S&P bond default rates. For ease of
illustration, suppose that one internal grade contains only three
obligors that individually map to BB, BB-, and B+. The historical
default rates for these three grades are 1.07, 1.76, and 3.24 percent,
respectively (based on 1981-2001 data). Using obligor mapping, those
rates would be assigned directly to the three obligors, yielding a mean
PD of 2.02 percent for the grade. Using grade mapping, the grade PD
would be only 1.76, because the grade's typical obligor is rated BB-.
[sbull] Second, a hypothetical obligor with a grade's average
characteristics may not represent well the risks presented by the
grade's typical obligor. For example, a bank might observe that
obligors with high leverage and low earnings variability have about the
same default risk as obligors with low leverage and high earnings
variability. These two types of obligors might both end up in the same
grade, for example, Grade 6. If so, the typical obligor in Grade 6
would have moderate leverage and moderate earnings variability--a
combination that might fail to reflect any of the individual obligors
in Grade 6, and that could easily result in a PD for the grade that is
too low.
A bank electing to use grade mapping instead of obligor mapping
should be especially careful in choosing a ``typical'' obligor for each
grade. Doing so typically requires that the bank examine the actual
distribution of obligors within each grade, as well as the
characteristics of those obligors. Banks should be aware that different
measures of central tendency (such as mean, median, or mode) will give
different results, and that these different results may have a material
effect on a grade's PD; they must be able to justify their choice of a
measure. Banks must have a clear and consistent policy toward the
calculation.
S. The mapping must be based on a robust comparison of available
data elements that are common to the portfolio and the reference data.
Sound mapping practice uses all common elements that are available
in the data as the basis for mapping. If a bank chooses to ignore
certain common variables or to weight some variables more heavily than
others, those choices must be supported. Mapping should also take into
account differences in rating philosophy (for example, point-in-time or
through-the-cycle) between any ratings embedded in the reference data
set and the bank's own rating regime.
A mapping should be plausible, and should be consistent with the
rating philosophy established by the bank as part of its obligor rating
policy. For a bank that uses grade mapping, levels and ranges of key
variables within each internal grade should be close to values of
similar variables for corresponding obligors within the reference data.
The standard allows for use of a limited set of common variables
that are predictive of default risk, in part to permit flexibility in
early years when data may be far from ideal. Nevertheless, banks will
eventually be expected to use variables that are widely recognized as
the most reliable predictors of default risk in mapping exercises. In
the meantime, banks relying on data elements that are weak predictors
must compensate by making their estimates more conservative. For
example, leverage and cash flow are widely recognized to be reliable
predictors of corporate default risk. Borrower size is also predictive,
but less so. A mapping based solely on size is by nature less reliable
than one based on leverage, cash flow, and size.
Example 1. In estimating PD, a bank relies on observed default
rates on bonds in various agency grades for PD quantification. To
map its internal grades to the agency grades, the bank identifies
variables that together explain much of the rating variation in the
bond sample. The bank then conducts a statistical analysis of those
same variables within its portfolio of obligors, using a
multivariate distance calculation to assign each portfolio obligor
to the external rating whose characteristics it matches most closely
(for example, assigning obligors to ratings so that the sum of
squared differences between the external grade averages and the
obligor's characteristics is minimized). This practice is broadly
consistent with the standard.
Example 2. A bank uses grade mapping to link portfolio obligors
to the reference data set described by agency ratings. The bank
looks at publicly rated portfolio obligors within an internal grade
to determine the most common external rating, does the same for all
grades, and creates a correspondence between internal and external
ratings. The strength of the correspondence is a function of the
number of externally rated obligors within each grade, the
distribution of those external ratings within each grade and the
similarity of externally rated obligors in the grade to those not
externally rated. This practice is broadly consistent with this
standard, but would require a comparison of rating philosophies and
may require adjustments and the addition of margins of conservatism.
S. A mapping process must be established for each reference data
set and for each estimation model.
Banks should never assume that a mapping is self-evident. Even a
rating system that has been explicitly designed to replicate external
agency ratings may or may not be effective in producing a replica;
formal mapping is still necessary. Indeed, in such a system the kind of
analysis involved in mapping may help identify inconsistencies in the
rating process itself.
A mapping process is needed even where the reference obligors come
from internal historical experience. Banks must not assume that
internal data do not require mapping, because changes in bank strategy
or external economic forces may alter the composition of internal
grades or the nature of the obligors in those grades over time.
Mappings must be reaffirmed regardless of whether rating criteria or
other aspects of the ratings system have undergone explicit changes
during the period covered by the reference data set.
Banks often use multiple reference data sets, and then combine the
resulting estimates to get a grade PD. A bank that does that must
conduct a rigorous mapping process for each data set.
Supervisors expect all meaningful characteristics of obligors to be
factored directly into the rating process; this should include
characteristics like the obligor's industry or physical location. But
in some circumstances, certain effects related to industry, geography,
or other factors are not reflected in rating assignments or default
estimates. In such cases, it may be appropriate for banks to capture
the impact of the
[[Page 45963]]
omissions by using different mappings for different business lines or
types of obligors. Supervisors expect this practice to be transitional;
banks will eventually be required to incorporate the omitted effects
into the rating system and the estimation process as they are uncovered
and documented, rather than adjusting the mapping.
Example 1. The bank maps its internal grades carefully to one
rating agency, and then assumes a correspondence to another agency's
scale despite known differences in the rating methods of the two
agencies. The bank then applies a mean of the grade default rates
from these two public debt-rating agencies to its internal grades.
This practice is not consistent with the standard, because the bank
should map to each agency's scale separately.
Example 2. A bank uses internal historical data as its reference
data. The bank computes a mean default rate for each grade as the
grade PD for capital purposes, and asserts that mapping is
unnecessary because ``its strong credit culture ensures that a 4 is
always a 4.'' This practice is not consistent with the standard,
because no mapping has been done; there is no assurance that a
representative obligor in a grade today is comparable to an obligor
in that same grade in the past.
S. The mapping must be updated and independently validated
regularly.
The appropriate mapping between a bank's portfolio and the
reference data may change over time. For example, relationships between
internal grades and external agency grades may change during the
economic cycle because of differences in rating philosophy. Similarly,
distance-to-default measures for obligors in a given grade may not be
constant over time. These likely changes make it imperative that the
bank update all mappings regularly.
Sound validation practices may include tests for internal
consistency such as ``reverse mapping.'' Using this technique, a bank
evaluates obligors from the reference data set as if they were subject
to the bank's rating system (that is, part of the bank's current
portfolio). The bank's mapping is then applied to these reverse-mapped
obligors to see whether the mapped characterization of the reference
obligor is consistent with that of the initial evaluation.\5\ Another
valuable technique is to apply different mapping methods and compare
the results. For example, mappings based on financial ratio comparisons
can be rechecked using mappings based on available external ratings.
---------------------------------------------------------------------------
\5\ For example, suppose a bank asserts that its Grade 3
corresponds to an S&P rating of A. Applying reverse mapping, the
bank would take a sample of A-rated obligors from the reference
data, run them through the bank's rating process (perhaps a
simplified version), and check to see that those obligors usually
receive a grade of 3 on the bank's internal scale.
Example. A bank mapped its internal grades to the rating scale
of one public debt-rating agency in 1992. Since then, the bank has
completed a major acquisition of another large bank and
significantly changed its business mix in other ways. The bank
continues to use the same mapping, without reassessing its validity.
This practice is not consistent with the standard.
Application
In the application stage, the bank applies the PD estimation method
to the current portfolio of obligors using the mapping process. It
obtains final PD estimates for each rating grade, which will be used to
calculate minimum regulatory capital. To arrive at those estimates, a
bank may adjust the raw results derived from the estimation stage. For
example, it might aggregate individual obligor default probabilities to
the rating grade level, or smooth results because a rating grade's PD
estimate was higher than a lower quality grade. The bank must explain
and support all adjustments when documenting its quantification
process.
Example. A bank uses external data to estimate long-run average
PDs for each grade. The resulting PD estimate for Grade 2 is
slightly higher than the estimate for Grade 3, even though Grade 2
is supposedly of higher credit quality. The bank uses statistics to
demonstrate that this anomaly occurred because defaults are rare in
the highest quality rating grades. The bank judgmentally adjusts the
PD estimates for grades 2 and 3 to preserve the expected
relationship between obligor grade and PD, but requires that total
risk-weighted assets across both grades using the adjusted PD
estimates be no less than total risk-weighted assets based on the
unadjusted estimates, using a typical distribution of obligors
across the two grades. Such an adjustment during the application
stage is consistent with this guidance.
S. IRB institutions that aggregate the default probabilities of
individual portfolio obligors when calculating PD estimates for
internal grades must have a clear policy governing the aggregation
process.
As noted above, mapping may be grade-based or obligor-based. Grade-
based mappings naturally provide a single PD per grade, because the
estimated default model is applied to the representative obligor for
each grade. In contrast, obligor-based mappings must aggregate in some
manner the individual PD estimates to the grade level. The expectation
is that the grade PD estimate will be calculated as the mean. The bank
will be allowed to calculate this estimate differently only if it can
demonstrate that the alternative method provides a better estimate of
the long-run average PD. To obtain this evidence, the bank must at
least compare the results of both methods.
S. IRB institutions that combine estimates from multiple sets of
reference data must have a clear policy governing the combination
process, and must examine the sensitivity of the results to alternative
combinations.
Because a bank should make use of as much information as possible
when mapping, it will usually use multiple data sets. The manner in
which the data or the estimates from those multiple data sets are
combined is extremely important. A bank must document its justification
for the particular combination methods selected. Those methods must be
subject to appropriate approval and oversight.
The data may come from the same basic data source but from
different time periods or from different data sources altogether. For
example, banks often combine internal data with external data, use
external data from different sample periods, or combine results from
corporate-bond default databases with results from equity-based models
of obligor default. Different combinations will produce different PD
estimates. The bank should investigate alternative combinations and
document the impact on the estimates. When ultimate results are highly
sensitive to how estimates from different data sources are combined,
the bank must choose among the alternatives conservatively.
C. Loss Given Default (LGD)
The LGD estimation process is similar to the PD estimation process.
The bank identifies a reference data set of defaulted credits and
relevant descriptive characteristics. Once the bank obtains these data
sets (with the facility characteristics), it must select a technique to
estimate the economic loss per dollar of exposure at default, for a
defaulted exposure with a given array of characteristics. The bank's
portfolio must then be mapped, so that the model can be applied to
generate an estimate of LGD for each portfolio transaction or severity
grade.
Data
Unlike reference data sets used for PD estimation, data sets for
severity estimation contain only exposures to defaulting obligors. At
least two broad categories of data are necessary to produce LGD
estimates.
First, data must be available to calculate the actual economic loss
experienced for each defaulted facility. Such data may include the
market value of the facility at default, which can be
[[Page 45964]]
used to proxy a recovery rate. Alternatively, economic loss may be
calculated using the exposure at the time of default, loss of
principal, interest, and fees, the present value of subsequent
recoveries and related expenses (or the costs as calculated using an
approved allocation method), and the appropriate discount rate.
Second, factors must be available to group the defaulted facilities
in meaningful ways. Characteristics that are likely to be important in
predicting loss rates include whether or not the facility is secured
and the type and coverage of collateral if the facility is secured,
seniority of the claim, general economic conditions, and obligor's
industry. Although these factors have been found to be significant in
existing academic and industry studies, a bank's quantification of LGD
certainly need not be limited to these variables. For example, a bank
might expand its loss severity research by examining many other
potential drivers of severity (characteristics of an obligor that might
help the bank predict the severity of a loss), including obligor size,
line of business, geographic location, facility type, obligor ratings
(internal or external), historical internal severity grade, or tenor of
the relationship.
A bank must ensure that the reference data remains applicable to
its current portfolio of facilities. It must implement established
processes to ensure that reference data sets are updated when new data
become available. All data sources, variables, and the overall
processes concerning data collection and maintenance must be fully
documented, and that documentation should be readily available for
review.
S. The sample period for the reference data must be at least seven
years, and must include periods of economic stress during which
defaults were relatively high.
Seven years is the minimum sample period for the LGD reference
data. A longer sample period is desirable, because more default
observations will be available for analysis and may serve to refine
severity estimates. In any case, a bank must select a sample period
that includes episodes of economic stress, which are defined as periods
with a relatively high number of defaults. Inclusion of stress periods
increases the size and potentially the breadth of the reference data
set. According to some empirical studies, the average loss rate is
higher during periods of stress.
Example. A bank intends to rely primarily on internal data when
quantifying all parameter estimates, including LGD. Its internal
data cover the period 1994 through 2000. The bank will continue to
extend its data set as time progresses. Its current policy mandates
that credits be resolved within two years of default, and the data
set contains the most recent data available. Although the current
data set satisfies the seven-year requirement, the bank is aware
that it does not include stress periods. In comparing its loss
estimates with rates published in external studies for similarly
stratified data, the bank observes that its estimates are
systematically lower. To be consistent with the standard, the bank
must take steps to include stress periods in its estimates.
S. The definition of default within the reference data must be
reasonably consistent with the IRB definition of default.
This standard parallels a similar standard in the section on PD.
The following examples illustrate how it applies in the case of LGD.
Example 1. For LGD estimation, a bank includes in its default
data base only defaulted facilities that actually experience a loss,
and excludes credits for which no loss was recorded because
liquidated collateral covered the loss (effectively applying a
``loss given loss'' concept). This practice is not consistent with
the standard because the bank's default definition for LGD is
narrower than the IRB definition.
Example 2. A bank relies on external data sources to estimate
LGD because it lacks sufficient internal data. One source uses
``bankruptcy filing'' to indicate default while another uses
``missed principal or interest payment,'' and the two sources result
in significantly different loss estimates for the severity grades
defined by the bank. The bank's practice is not consistent with the
standard, and the bank should determine whether the definitions used
in the reference data sets differ substantially from the IRB
definition. If so, and the differences are difficult to quantify,
the bank should seek other sources of reference data. For more minor
differences, the bank may be able to make appropriate adjustments
during the estimation stage.
Estimation
Estimation of LGD is the process by which characteristics of the
reference data are related to loss severity. The relevant
characteristics that help explain how severe losses tend to be upon
default might include variables such as seniority, collateral, facility
type, or business line.
S. The estimates of loss severity must be empirically based and
must reflect the concept of ``economic loss.''
Loss severity is defined as economic loss, which is different from
accounting measures of loss. Economic loss captures the value of
recoveries and direct and indirect costs discounted to the time of
default, and it should be measured for each defaulted facility. The
scope of the cash flows included in recoveries and costs is meant to be
broad. Workout costs that can be clearly attributed to certain
facilities or types of facilities must be reflected in the bank's LGD
assignments for those exposures. When such allocation is not practical,
the bank may assign those costs using factors based on broad averages.
A bank must establish a discount rate that reflects the time value
of money and the opportunity cost of funds to apply to recoveries and
costs. The discount rate must be no less than the contract interest
rate on new originations of a type similar to the transaction in
question, for the lowest-quality grade in which a bank originates such
transactions.\6\ Where possible, the rate should reflect the fixed rate
on newly originated exposures with term corresponding to the average
resolution period of defaulting assets.
---------------------------------------------------------------------------
\6\ The appropriate discount rate for IRB purposes may differ
from the contract rate required under FAS 114 for accounting
purposes.
---------------------------------------------------------------------------
Ideally, severity should be measured once all recoveries and costs
have been realized. However, a bank may not resolve a defaulted
obligation for many years following default. For practical purposes,
banks may choose to close the period of observation before this final
resolution occurs--that is, at a point in time when most costs have
been incurred and when recoveries are substantially complete. Banks
that do so should estimate the additional costs and recoveries that
would likely occur beyond this period and include them in the LGD
estimates. A bank must document its choice of the period of
observation, and how it estimated additional costs and recoveries
beyond this period.
LGD for each type of exposure must be the loss per default
(expressed as a percentage of exposure at default) expected during
periods when default rates are relatively high. This expected loss rate
is referred to as ``stress-condition LGD.'' For cases in which loss
severities do not have a material degree of cyclical variability, use
of the long-run default-weighted average is appropriate, although
stress-condition LGD generally exceeds this average.
The drivers of severity can be linked to loss estimates in a number
of ways. One approach is to segment the reference defaults into groups
that do not overlap. For example, defaults could be grouped by business
line, predominant collateral type, and loan-to-value coverage. The LGD
estimate for each category is the mean loss calculated over the
category's defaulted facilities. Loss must be calculated as the
default-weighted average (where individual defaults receive equal
weight) rather than the average of
[[Page 45965]]
annual loss rates, and must be based on results from periods during
which default rates were relatively numerous if loss rates are
materially cyclical.
Banks can also draw estimates of LGD from a statistical model. For
example, they can build a regression model of severity using data on
loss severity and some quantitative measures of the loss drivers. Any
model must meet the requirements for model validation discussed in
Chapter 1. Other methods for computing LGD could also be appropriate.
Example 1. A bank has internal data on defaulted facilities,
including information on business line, facility type, seniority,
and predominant collateral type (if the facility is secured). The
data allow for a reasonable calculation of economic loss. The data
span eight years and include three years that can be termed high-
default years. After analyzing the economic cycle using internal and
external data, the bank concludes that the data show no evidence of
material cyclical variability in loss severities, and that the
default data span enough experience to allow estimation of a long-
run average. On the basis of preliminary analysis, the bank
determines that the drivers of loss severity for large corporate
facilities are similar to those for middle-market loans, and that
the two groups can be estimated as a pool. Again on the basis of
preliminary analysis, the bank segments this pool by seniority and
by six collateral groupings, including unsecured. These groupings
contain enough defaults to allow reasonably precise estimates. The
loss severity estimates are then calculated by averaging loss rates
within each segment. This practice is consistent with the standard.
Example 2. A bank uses internal data in which information on
security and seniority is lacking. The bank groups corporate and
middle-market defaulted facilities into a single pool and calculates
the LGD estimate as the mean loss rate. No adjustments for the lack
of data are made in the estimation or application steps. This
practice is unacceptable because there is ample external evidence
that security and seniority matter in these segments. A bank with
such limited internal default data must incorporate external or
pooled data into the estimation.
Example 3. A bank determines that a business unit--for example,
a unit dedicated to a particular type of asset-based lending--forms
a homogeneous pool for the purposes of estimating loss severity.
That is, although the facilities in this pool may differ in some
respects, the bank determines that they share a similar loss
experience in default. The bank must provide reasonable support for
this pooling through analysis of lending practices and available
internal and external data. In this example, the mean of a single
segment is consistent with the standard.
S. Judgmental adjustments may play an appropriate role in LGD
estimation, but must not be biased toward lower estimates.
It is difficult to make general statements about good and bad
practices in this area, because adjustments can take many different
forms. The following examples illustrate how supervisors would be
likely to evaluate particular adjustments observed in practice.
Example 1. A bank divides observed defaults into segments
according to collateral type. One of the segments has too few
observations to produce a reliable estimate. Relying on external
data and judgment, the bank determines that the segment's estimated
severity of loss falls somewhere between the estimates for two other
categories. This segment's severity is set judgmentally to be the
mean of the estimates for the other segments. This practice is
consistent with the standard.
Example 2. A bank does not know when recoveries (and related
costs) occurred in a portfolio segment; therefore, it cannot
properly discount the segment's cash flows. However, the bank has
sufficient internal data to calculate economic loss for defaulted
facilities in another portfolio segment. The bank can support the
assumption that the timing of cash flows for the two segments is
comparable. Using the available data and informed judgment, the bank
estimates that the measured loss without discounting should be
grossed up to account for the time value of money and the
opportunity cost of funds. This practice is consistent with the
standard.
Example 3. A bank segments internal defaults in a business unit
by some factors, including collateral. Although the available
internal and external evidence indicates a higher LGD, the bank
judgmentally assigns a loss estimate of 2 percent for facilities
secured by cash collateral. The basis for this adjustment is that
the lower estimate is justified by the expectation that the bank
would do a better job of following policies for monitoring cash
collateral in the future. Such an adjustment is generally not
appropriate because it is based on projections of future performance
rather than realized experience. This practice is not consistent
with the standard.
Mapping
LGD mapping follows the same general principles that PD mapping
does. A mapping must be plausible and must be based on a comparison of
severity-related data elements common to both the reference data and
the current portfolio. The mapping approach is expected to be unbiased,
such that the exercise of judgment does not consistently lower LGD
estimates. The default definitions in the reference data and the
current portfolio of obligors should be comparable. The mapping process
must be updated regularly, well-documented, and independently reviewed.
S. A bank must conduct a robust comparison of available common
elements in the reference data and the portfolio.
Mapping involves matching facility-specific data elements available
in the current portfolio to the factors in the reference data set used
to estimate expected loss severity rates. Examples of factors that
influence loss rates include collateral type and coverage, seniority,
industry, and location.
At least three kinds of mapping challenges may arise. First, even
if similarly named variables are available in the reference data and
portfolio data, they may not be directly comparable. For example, the
definition of particular collateral types, or the meaning of
``secured,'' may vary from one application to another. Hence, a bank
must ensure that linked variables are truly similar. Although
adjustments to enhance comparability can be appropriate, they must be
rigorously developed and documented. Second, levels of aggregation may
vary. For example, the reference data may only broadly identify
collateral types, such as financial and nonfinancial. The bank's
information systems for its portfolio might supply more detail, with a
wide variety of collateral type identifiers. To apply the estimates
derived from the reference data, the internal data must be regrouped to
match the coarser level of aggregation in the reference data. Third,
reference data often do not include workout costs and will often use
different discounting. Judgmental adjustments for such problems must be
well-documented and, as much as possible, empirically based.
S. A mapping process must be established for each reference data
set and for each estimation model.
Mapping is never self-evident. Even when reference data are drawn
from internal default experience, a bank must still link the
characteristics of the reference data with those of the current
portfolio.
Different data sets and different approaches to severity estimation
may be entirely appropriate, especially for different business segments
or product lines. Each mapping process must be specified and
documented.
Application
At the application stage, banks apply the LGD estimation framework
to their current portfolio of credit exposures. Doing so might require
them to aggregate individual LGD estimates into broader averages (for
example, into discrete severity grades) or to combine estimates in
various ways.
The inherent variability of recovery, due in part to unanticipated
circumstances, demonstrates that no facility type is wholly risk-free,
regardless of structure, collateral type, or collateral coverage. The
existence of
[[Page 45966]]
recovery risk dictates that application of a zero percent LGD is not
acceptable.
S. IRB institutions that aggregate LGD estimates for severity
grades from individual exposures within those grades must have a clear
policy governing the aggregation process.
Banks with discrete severity grades compute a single estimate of
LGD for a representative exposure within each of those grades. If a
bank with a discrete scale of severity grades maps those grades to the
reference data using grade mapping, there will be a single estimate of
LGD for each grade, and the bank does not need to aggregate further.
However, if the bank maps at the individual transaction level, the bank
may then choose to aggregate those individual LGD estimates to the
grade level and use the grade LGD in capital calculations. Because
different methods of aggregation are possible, a bank must have a clear
policy regarding how aggregation should be accomplished; in general,
simple averaging is preferred. (This standard is irrelevant for banks
that choose to assign LGD estimates directly to individual exposures
rather than grades, because aggregation is not required in that case.)
S. An IRB institution must have a policy describing how it combines
multiple sets of reference data.
Multiple data sets may produce superior estimates of loss severity,
if the results are appropriately combined. Combining such sets
differently usually produces different estimates of LGD. As a matter of
internal policy, a bank should investigate alternative combinations,
and document the impact on the estimates. If the results are highly
sensitive to the manner in which different data sources are combined,
the bank must choose conservatively among the alternatives.
D. Exposure at Default (EAD)
Compared with PD and LGD quantification, EAD quantification is less
advanced. As such, it is addressed in somewhat less detail in this
guidance than are PD and LGD quantification. Banks should continue to
innovate in the area EAD estimation, refining and improving practices
in EAD measurement and prediction. Additional supervisory guidance will
be provided as more data become available and estimation techniques
evolve.
A bank must provide an estimate of expected EAD for each facility
in its portfolio. EAD is defined as the bank's expected gross dollar
exposure of the facility upon the obligor's default. For fixed
exposures like term loans, EAD is equal to the current amount
outstanding. For variable exposures such as loan commitments or lines
of credit, exposure is equal to current outstandings plus an estimate
of additional drawings up to the time of default. This additional
drawdown, identified as loan equivalent exposure (LEQ) in many
institutions, is typically expressed as a percentage of the current
total committed but undrawn amount. EAD can thus be represented as:
EAD = current outstanding + LEQ x (total committed-current outstanding)
As it is the LEQ that must be estimated, LEQ is the focus of this
guidance.
Even though EAD estimation is less sophisticated than PD and LGD
estimation, a bank still develops EAD estimates by working through the
four stages that produce the other types of quantification: The bank
must use a reference data set; it must apply an estimation technique to
produce an expected total dollar exposure at default for a facility
with a given array of characteristics; it must map its current
portfolio to the reference data; and, by applying the estimation model,
it must generate an EAD estimate for each portfolio facility or
facility-type, as the case may be.
Data
Like reference data sets used for LGD estimation, LEQ data sets
contain only exposures to defaulting obligors. In many cases, the same
reference data may be used for both LGD and LEQ. In addition to
relevant descriptive characteristics (referred to as ``drivers'') that
can be used in estimation, the reference data must include historical
information on the exposure (both drawn and undrawn amounts) as of some
date prior to default, as well as the drawn exposure at the date of
default.
As discussed below under ``Estimation,'' LEQ estimates may be
developed using either a cohort method or a fixed-horizon method. The
bank's reference data set must be structured so that it is consistent
with the estimation method the bank applies. Thus, the data must
include information on the total commitment, the undrawn amount, and
the exposure drivers for each defaulted facility, either at fixed
calendar dates for the cohort method or at a fixed interval prior to
the default date for the fixed-horizon method.
The reference data must contain variables that enable the bank to
group the exposures to defaulted obligors in meaningful ways. Obligor
and facility risk ratings are commonly believed to be significant
characteristics for predicting additional drawdown. Since less
empirical research has been done on EAD estimation, little is known
about other potential drivers of EAD. Among the many possibilities,
banks may consider time from origination, time to expiration or
renewal, economic conditions, risk rating changes, or certain types of
covenants. Some potential drivers may be linked to a bank's credit risk
management skills, while others may be exogenous. Industry practice is
likely to improve as banks extend their research to identify other
meaningful drivers of EAD.
A bank must ensure continued applicability of the reference data to
its current portfolio of facilities. The reference data must include
the types of variable exposures found in a bank's current portfolio.
The definitions of default and exposure in the reference data should be
consistent with the IRB definition of default, and consistent with the
definitions used for PD and LGD quantification. Established processes
must be in place to ensure that reference data sets are updated when
new data are available. All data sources, variables, and the overall
processes governing data collection and maintenance must be fully
documented, and that documentation should be readily available for
review.
Seven years of data are required for EAD (or LEQ) estimation. The
sample should include periods during which default rates were
relatively high, and ideally cover a complete economic cycle.
Estimation
To derive LEQ estimates, characteristics of the reference data are
related to additional drawings preceding a default event. The
estimation process must be capable of producing a plausible estimate of
LEQ to support the EAD calculation for each facility. Two broad types
of estimation methods are used in practice, the cohort method and the
fixed-horizon method.
Under the cohort method, a bank groups defaults into discrete
calendar periods (such as a year or a quarter). The bank then estimates
the relationship between the drivers as of the start of that calendar
period, and EAD or LEQ for each exposure to a defaulter. For each
exposure category (that is, for each combination of exposure drivers
identified by the bank), the LEQ estimate is calculated as the mean
additional drawing for facilities in that category. To combine results
for multiple periods into a single long-run average, the period-by-
period means should be weighted by the proportion of defaults occurring
in each period.
Under the fixed-horizon method, for each exposure to a defaulted
obligor the
[[Page 45967]]
bank compares additional drawdowns to the total commitment but undrawn
amount that existed at the start of a fixed interval prior to the date
of the default (the horizon). For example, the bank might base its
estimates on a reference data set that supplies the actual exposure at
default along with the drawn and undrawn amounts (as well as relevant
drivers) at a date a fixed number of months prior to the date of each
default, regardless of the actual calendar date on which the default
occurred. Estimates of LEQ are computed from the average drawdowns that
occur over the fixed-horizon interval, for whatever combinations of the
driving variables the bank has determined are relevant for explaining
and predicting exposure at default.
Evidence may indicate that LEQ estimates are positively correlated
with economic downturns; that is, it may be that LEQs increase during
high-default periods. If so, the higher drawdowns that occur during
high-default periods are denoted ``stress-condition LEQs,'' analogous
to the ``stress-condition LGDs'' discussed earlier in this chapter. For
any exposure type whose LEQ estimates exhibit material cyclicality, a
bank must use the stress-condition LEQ for purposes of calculating EAD.
In general, all available data should be used; particular
observations or time periods should not be excluded from the data
sample. Any adjustments a bank makes to the estimation results should
be justified and fully documented. The analysis should be refreshed
periodically as new data become available, and a bank should have a
process in place to ensure that advances in analytical techniques and
industry practice are considered as they emerge and are incorporated as
appropriate. LEQ estimates should be updated at least annually.
Detailed documentation, ongoing validation, and adequate oversight are
fundamental controls that support a sound estimation process.
Mapping
If the same variables that drive exposure in the reference data are
also available for facilities in the portfolio, mapping may be
relatively easy. However, the bank must still review the definitions to
ensure that variables that seem to be the same actually are. If the
relevant variables are not available in a bank's current portfolio
information system, the bank will encounter the same mapping
complexities that it does when mapping for PD and LGD in similar
circumstances. A bank should have well-documented policies that govern
the mapping. Any exceptions to mapping policy should be reviewed,
justified and fully documented. Mapping may be done for each exposure
or for broad categories of exposure; the latter would be analogous to
the ``grade mapping'' discussed earlier in this chapter.
Application
In the application stage, the estimated relationship between
drivers and LEQ is applied to the bank's actual portfolio. To ensure
that estimated EAD is at least as large as the currently drawn amount
for all exposures, LEQs must not be negative. Multiple reference data
sets may be used for LEQ estimation and combined at the application
stage; those combinations should be rigorously developed, approved, and
documented. Any smoothing or use of expert judgment to adjust the
results should be well-justified and clearly documented. This includes
any adjustment for definitions of default that do not meet the
supervisory standards. The less robust the process, the more
conservative the result should be.
Some facility types may be treated as exceptions, and assigned an
LEQ that does not vary with characteristics such as line of business or
risk rating. Such exceptional treatment should be clearly justified,
and the justification should be fully documented.
EAD may be particularly sensitive to changes in the way banks
manage individual credits. For example, a change in policy regarding
covenants may have a significant impact on LEQ. When such changes take
place, the bank should consider them when making its estimates--and it
should do so from a conservative point of view. Policy changes likely
to significantly increase LEQ should prompt immediate increases in LEQ
estimates. If a bank's policy changes seem likely to reduce LEQ,
estimates should be reduced only after the bank accumulates a
significant amount of actual experience under the new policy to support
the reductions.
E. Maturity (M)
A bank must assign a value of effective remaining maturity (M) to
each credit exposure in its portfolio. In general, M is the weighted-
average number of years to receipt of the cash flows the bank expects
under the contractual terms of the exposure, where the weights are
equal to the fraction of the total undiscounted cash flow to be
received at each date. Mathematically, M is given by:
[GRAPHIC] [TIFF OMITTED] TN04AU03.008
where wt is the fraction of the total cash flow received at
time t, that is:
[GRAPHIC] [TIFF OMITTED] TN04AU03.009
Ct is the undiscounted cash flow received at time t, with t
measured in years from the date of the calculation of M.
Effective maturity, sometimes referred to as ``average life,'' need
not be a whole number, and often is not. For example, if 33 percent of
the cash flow is expected at the end of one year (t=1) and the other 67
percent two years from today (t=2), then M is calculated as:
M = (1x0.33) + (2x0.67) = 1.67
for an effective maturity of 1.67 years. This value of M would be used
in the IRB capital calculation.
The relevant cash flows are the future payments the bank expects to
receive from the obligor, regardless of form; they may include payments
of interest or fees, principal repayments, or other types of payments
depending on the structure of the transaction. For exposures whose cash
flow schedule is virtually predetermined unless the obligors defaults
(fixed-rate loans, for example), the calculation of the weighted-
average remaining maturity is straightforward, using the scheduled
timing and amounts of the individual undiscounted cash flows. These
cash flows should be the contractually expected payments; the bank
should not take into account the possibility of delayed or reduced cash
flows due to potential future default.
Cash flows associated with other types of credit exposures may be
somewhat less certain. In such cases, the bank must establish a method
of projecting expected cash flows. In general, the method used for any
exposure should be the same as the one used by the bank for purposes of
valuation or risk management. The method must be well-documented and
subject to independent review and approval. A bank must demonstrate
that the method used is standard industry practice, that it is widely
used within the bank for purposes other than regulatory capital
calculations, or both.
To be conservative, a bank may set M equal to the maximum number of
years the obligor could take to fully discharge the contractual
obligation (provided that the maximum is not longer than five years, as
noted below). In many cases, this maximum will correspond to the stated
or nominal maturity of the instrument. Banks must make this
conservative choice (maximum nominal maturity) if the timing and
amounts of
[[Page 45968]]
the cash flows on the exposure cannot be projected with a reasonable
degree of confidence.
Certain over-the-counter derivatives contracts and repurchase
transactions may be subject to master netting agreements. In such
cases, the bank may compute a single value of M for the transactions as
a group by weighting each individual transaction's effective maturity
by that transaction's share of the total notional value subject to the
netting agreement, and summing the result across all of the
transactions.
For IRB capital calculations, the value of M for any exposure is
subject to certain upper and lower limits, regardless of the actual
effective maturity of the exposure. In all cases, the value of M should
be no greater than 5 years. If an exposure clearly has an effective
maturity that exceeds this upper limit, the bank may simply use a value
of M=5 rather than calculating the actual effective maturity.
For most exposures, the value of M must be no less than one year.
For certain short-term exposures (repo-style transactions, money market
transactions, trade finance-related transactions, and exposures arising
from payment and settlement processes) that are not part of a bank's
ongoing financing of a borrower and that have an original maturity of
less than three months, M may be set as low as one day. For over-the-
counter derivative and repurchase-style transactions subject to a
master netting agreement, weighted average maturity must be set at no
less than five days.
F. Validation
Values of PD, LGD, and EAD are estimates with implications for
credit risk and the future performance of a bank's credit portfolio
under IRB; in essence, they are forecasts. ``Validation'' of these
estimates describes the full range of activities used to assess their
quality as forecasts of default rates, loss severity rates, and
exposures at default. Chapter 1 discusses validation of IRB systems in
general; this section focuses specifically on ratings quantification,
which includes the assignment of PD to obligor grades and the
assignment of LGD, EAD, and M to exposures.
S. A validation process must cover all aspects of IRB
quantification.
Banks must have a process for validating IRB quantification; their
policies must state who is accountable for validation, and describe the
actions that will proceed from the different possible results.
Validation should focus on the three estimated IRB parameters (PD, LGD,
and EAD). Although the established validation process should result in
an overall assessment of IRB quantification for each parameter, it also
must cover each of the four stages of the quantification process as
described in preceding sections of this chapter (data, estimation,
mapping, and application). The validation process must be fully
documented, and must be approved by appropriate levels of the bank's
senior management. The process must be updated periodically to
incorporate new developments in validation practices and to ensure that
validation methods remain appropriate; documentation must be updated
whenever validation methods change.
Banks should use a variety of validation approaches or tools; no
single validation tool can completely and conclusively assess IRB
quantification. Three broad types of tools that are useful in this
regard are evaluation of the conceptual soundness of the approach to
quantification (evaluation of logic), comparison to other sources of
data or estimates (benchmarking), and comparisons of actual outcomes to
predictions (back-testing). Each of these types of tools has a role to
play in validation, although the role varies across the four stages of
quantification.
Evaluation of logic is essential in validating all stages of the
quantification process. The quantification process requires banks to
adopt methods, choose variables, and make adjustments; each of these
actions requires an exercise of judgment. Validation should ensure that
these judgments are plausible and informed.
A bank should also validate estimates by comparing them with
relevant external sources, a process broadly described as benchmarking.
``External'' in this context refers to anything other than the specific
reference data, estimation approach, or mapping under consideration.
Reference data can be compared with other data sources; choices of
variables can be compared with similar choices made by others;
estimation results can be compared with the results of alternative
estimation methods using the same reference data. Other data sources
may show that default and severity rates across the economy or the
banking system are high or low relative to other periods, or may reveal
unusual effects in parts of the quality spectrum.
Effective validation must compare actual results with predictions.
Such comparisons, often referred to as ``back-testing,'' are valuable
comprehensive tests of the rating system and its quantification.
However, they are only one element of the broader validation regime,
and should not be a bank's only method of validation. Because they test
the results of the rating system as a whole, they are unlikely to
identify specific reasons for any divergence between expectations and
realizations. Rather they will indicate only that further investigation
is necessary.
By applying back-testing to the reference data set as it is updated
with new data, a bank can improve the estimation process. To further
improve the process, a bank must regularly compare realized default
rates, loss severities, and exposure-at-default experience from its
portfolio with the PD, LGD, and EAD estimates on which capital
calculations are based. Realizations should be compared with expected
ranges based on the estimates. These expected ranges should take into
account the bank's rating philosophy (the relative weight given to
current and stress conditions in assigning ratings). Depending on that
philosophy, year-by-year realized default rates and loss severities may
be expected to differ significantly from the long-run average. If a
bank adjusts final estimates to be conservative, it should likely do
its back-testing on the unadjusted estimates.
A bank's quantitative testing methods and other validation
techniques should be robust to economic cycles. A sound validation
process should take business cycles into account, and any adjustments
for stages of the cycle should be clearly specified in advance and
fully documented as part of the validation policy. The fact that a year
has been ``unusual'' should not be taken as a reason to abandon the
bank's standard validation practices.
S. A bank must comprehensively validate parameter estimates at
least annually, must document the results, and must report these
results to senior management.
A full and comprehensive annual validation is a minimum for
effective risk management under IRB. More frequent validation may be
appropriate for certain parts of the IRB system and in certain
circumstances; for example, during high-default periods, banks should
compute realized default and loss severity rates more frequently,
perhaps quarterly. They must document the results of validation, and
must report them to appropriate levels of senior risk management.
S. The validation policy must outline appropriate remedial
responses to the results of parameter validation.
The goal of validation should be to continually improve the rating
process and its quantification. To this end, the bank should establish
thresholds or accuracy tolerances for validation results. Results that
breach thresholds
[[Page 45969]]
should bring an appropriate response; that response should depend on
the results and should not necessarily be to adjust the parameter
estimates. When realized default, severity, or exposures rates diverge
from expected ranges, those divergences may point to issues in the
estimation or mapping elements of quantification. They may also
indicate potential problems in other parts of the ratings assignment
process. The bank's validation policy must describe (at least in broad
terms) the types of responses that should be considered when relevant
action thresholds are crossed.
Appendix to Part III: Illustrations of the Quantification Process
This appendix provides examples to show how the logical
framework described in this guidance, with its four stages (data,
estimation, mapping, and application), applies when analyzing
typical current bank practices. The framework is broadly
applicable--for PD or LGD or EAD; using internal, external, or
pooled reference data; for simple or complex estimation methods--
although the issues and concerns that arise at each stage depend on
a bank's approach. These examples are intended only to illustrate
the logic of the four-stage IRB quantification framework, and should
not be taken to endorse the particular techniques presented in the
examples. In fact, certain aspects of the examples are not
consistent with the standards outlined in this guidance.
Example 1: PD Estimation From Bond Data
[sbull] A bank establishes a correspondence between its internal
grades and external rating agency grades; the bank has determined
that its Grade 4 is equivalent to \3/4\ BB and \1/4\ B on the
Standard and Poor's scale.
[sbull] The bank regularly obtains published estimates of mean
default frequencies for publicly rated BB and B obligors in North
America from 1970 through 2002.
[sbull] The BB and B historical default frequencies are weighted
75/25, and the result is a preliminary PD for the bank's internal
Grade 4 credits.
[sbull] However, the bank then increases the PD by 10 percent to
account for the fact that the S&P definition of default is more
lenient than the IRB definition.
[sbull] The bank makes a further adjustment to ensure that the
resulting grade PD is greater than the PD attributed to Grade 3 and
less than the PD attributed to Grade 5.
[sbull] The result is the final PD estimate for Grade 4.
Process Analysis for Example 1
Data--The reference data set consists of issuers of publicly
rated debt in North America over the period 1970 through 2002. The
data description is very basic: each issuer in the reference data is
described only by its rating (such as AAA, AA, A, BBB, and so on).
Estimation--The bank could have estimated default rates itself
using a database purchased from Standard and Poor's, but since these
estimates would just be the mean default rates per year for each
grade, the bank could just as well (and in this example does) use
the published historical default rates from S&P; in essence, the
estimation step has been outsourced to S&P. The 10 percent
adjustment of PD is part of the estimation process in this case
because the adjustment was made prior to the application of the
agency default rates to the internal portfolio data.
Mapping--The bank's mapping is an example of a grade mapping;
internal Grade 4 is linked to the 75/25 mix of BB and B. Based on
the limited information presented in the example, this step should
be explored further. Specifically, how did the bank determine the
75/25 mix?
Application--Although the application step is relatively
straightforward in this case, the bank does make the adjustment of
the Grade 4 PD estimate to give it the desired relationship to the
adjacent grades. This adjustment is part of the application stage
because it is made after the adjusted agency default rates are
applied to the internal grades.
Example 2: PD Estimation Using a Merton-Type Equity-Based Model
[sbull] A bank obtains a 20-year database of North American
firms with publicly traded equity, some of which defaulted during
the 20-year period.
[sbull] The bank uses the Merton approach to modeling equity in
these firms as a contingent claim, constructing an estimate of each
firm's distance-to-default at the start of each year in the
database. The bank then ranks the firm-years within the database by
distance-to-default, divides the ordered observations into 20 equal
groups or buckets, and computes a mean historical one-year default
frequency for each bucket. That default frequency is taken as an
estimate of the applicable PD for any obligor within the range of
distance-to-default values represented by each of the 20 buckets.
[sbull] The bank next looks at all obligors with publicly traded
shares within each of its internal grades, applies the same Merton-
type model to compute distance-to-default at quarter-end, sorts
these observations into the 20 buckets from the previous step, and
assigns the corresponding PD estimate.
[sbull] For each internal grade, the bank computes the mean of
the individual obligor default probabilities and uses that average
as the grade PD.
Process Analysis for Example 2
Data--The reference data set consists of the North American
firms with publicly traded equity in the acquired database. The
reference data are described in this case by a single variable,
specifically an identifier of the specific distance-to-default range
from the Merton model (one of the 20 possible in this case) into
which a firm falls in any year.
Estimation--The estimation step is simple: the average default
rate is calculated for each distance-to-default bucket. Since the
data cover 20 years and a wide range of economic conditions, the
resulting estimates satisfy the long-run average requirement.
Mapping--The bank maps selected portfolio obligors to the
reference data set using the distance-to-default generated by the
Merton model. However, not all obligors can be mapped, since not all
have traded equity. This introduces an element of uncertainty into
the mapping that requires additional analysis by the bank: were the
mapped obligors representative of other obligors in the same grade?
The bank would need to demonstrate comparability between the
publicly traded portfolio obligors and those not publicly traded. It
may be appropriate for the bank to make conservative adjustments to
its ultimate PD estimates to compensate for the uncertainty in the
mapping. The bank also would need further analysis to demonstrate
that the implied distance-to-default for each internal grade
represented long-run expectations for obligors assigned to that
grade; this could involve computing the Merton model for portfolio
obligors over several years of relevant history that span a wide
range of credit conditions.
Application--The final step is aggregation of individual
obligors to the grade level through calculation of the mean for each
grade, and application of this grade PD to all obligors in the
grade. The bank might also choose to modify PD assignments further
at this stage, combining PD estimates derived from other sources,
applying adjustments for cyclicality, introducing an appropriate
degree of conservatism, or making other adjustments.
Example 3: LGD Estimation From Internal Default Data
[sbull] For each loan in its portfolio, a bank records
collateral coverage as a percentage, as well as which of four types
of collateral applies.
[sbull] A bank has retained data on all defaulted loans since
1995. For each defaulted loan in the database, the bank has a record
of the collateral type within the same four broad categories.
However, collateral coverage is only recorded at three levels (low,
moderate, or high, depending on the ratio of collateral to exposure
at default).
[sbull] The bank also records the timing and discounted value of
recoveries net of workout costs for each defaulted loan in the
database. Cash flows are tracked from the date of default to a
``resolution date,'' defined as the point at which the remaining
balance is less than 5 percent of the exposure at the time of
default. A recovery percentage is computed, equal to the value of
recoveries discounted to the date of default, divided by the
exposure at default.
[sbull] For each cell (each of the 12 combinations of collateral
type and coverage), the bank computes a simple mean LGD percentage
as the mean of one minus the recovery percentage. One of the
categories has a mean LGD of less than zero (recoveries have
exceeded exposure on average), so the bank sets the LGD at zero to
be conservative.
[sbull] The bank assigns an estimate of expected LGD to each
loan in the current portfolio by using collateral information to
slot it into one of the 12 cells. The bank then applies the mean
historical LGD for that cell and adjusts the result upward by 10
percent to compensate for the fact that the loss data come from a
period believed to be unusually good economic performance.
[[Page 45970]]
Process Analysis for Example 3
Data--The reference data is the collection of historical
defaults with the loss amounts from the bank's historical portfolio.
The reference data are described by the two categorical variables
(levels of collateral coverage and types of collateral). It would be
important to determine whether the defaults over the past few years
are comparable to defaults from the current portfolio. One would
also want to ask why the bank ignores potentially valuable
information by converting the continuous data on collateral coverage
into a trimodal categorical variable.
Estimation--Conceptually, the bank is using a ``loss severity
model'' in which 12 binary variables, one for each loan coverage/
type combination, explain the percentage loss. The coefficients on
the variables are just the mean loss figures from the reference
data.
Mapping--Mapping in this case is fairly straightforward, since
all of the relevant characteristics of the reference data are also
in the loan system for the current portfolio. However, the bank
should determine whether the variables are being recorded in the
same way (for example, the same definitions of collateral types),
otherwise some adjustment might be needed.
Application--The bank is able to apply the loss model by simply
plugging in the relevant values for the current portfolio (or what
amounts to the same thing, looking up the cell mean). The bank's
assignment of zero LGD for one of the cells merits special
attention; while the bank represented this assignment as
conservative, the adjustment does not satisfy the supervisory
requirement that LGD must exceed zero. A larger upward adjustment is
necessary. Finally, the upward adjustment of the LGD numbers to
account for the benign environment in which the reference data were
generated presents one additional wrinkle. The bank must provide a
well-documented, empirically based analysis of why a 10 percent
upward adjustment is sufficient.
IV. Data Maintenance
A. Overview
Institutions using the IRB approach for regulatory capital purposes
will need advanced data management practices to produce credible and
reliable risk estimates. The guiding principle governing an IRB data
maintenance system is that it must support the requirements for the
quantification, validation, control and oversight mechanisms described
in this guidance, as well as the institution's broader risk management
and reporting needs. The precise data elements to be collected will be
dictated by the features and methodology of the IRB system employed by
the institution. The necessary data elements will therefore vary by
institution and even among business lines within an institution.
Institutions will have latitude in managing their data, subject to
the following key data maintenance standards:
Life Cycle Tracking--institutions must collect, maintain, and
analyze essential data for obligors and facilities throughout the life
and disposition of the credit exposure.
Rating Assignment Data--institutions must capture all significant
quantitative and qualitative factors used to assign the obligor and
loss severity ratings.
Support of IRB System--data collected by institutions must be of
sufficient depth, scope, and reliability to:
[sbull] Validate IRB system processes,
[sbull] Validate parameters,
[sbull] Refine the IRB system,
[sbull] Develop internal parameter estimates,
[sbull] Apply improvements historically,
[sbull] Calculate capital ratios,
[sbull] Produce internal and public reports, and
[sbull] Support risk management.
This chapter covers the requirements for maintaining internal data.
Reference data sets used for estimating IRB parameters are discussed in
Chapter 2.
B. Data Maintenance Framework
Life Cycle Tracking
S. Institutions must collect, maintain, and analyze essential data
for obligors and facilities throughout the life and disposition of the
credit exposure.
Using a life cycle or ``cradle to grave'' concept for each obligor
and facility supports front-end validation, back-testing, system
refinements and risk parameter estimates. A depiction of life-cycle
tracking follows:
[GRAPHIC] [TIFF OMITTED] TN04AU03.001
Data elements must be recorded at origination and whenever the
rating is reviewed, regardless of whether the rating is actually
changed. Data elements associated with current and past ratings must be
retained and include the following:
[sbull] Key borrower and facility characteristics,
[sbull] Ratings for obligor and loss severity grades,
[sbull] Key factors used to assign the ratings,
[sbull] Person or model responsible for assigning the rating,
[sbull] Date rating assigned, and
[sbull] Overrides to the rating and authorizing individual.
At disposition, data elements must include:
[sbull] Nature of disposition: renewal, repayment, loan sale,
default, restructuring,
[sbull] For defaults: exposure, actual recoveries, source of
recoveries, costs of workouts and timing,
[sbull] Guarantor support,
[sbull] Sale price for loans sold, and
[sbull] Other key elements that the bank deems necessary.
[[Page 45971]]
Rating Assignment Data
S. Institutions must capture all significant quantitative and
qualitative factors used to assign the obligor and loss severity
rating.
Assigning a rating to an obligor requires the systematic collection
of various borrower characteristics as these factors are critical to
validating the rating system. Obligors are rated using various methods,
as discussed in Chapter 1. Each of these methods presents different
challenges for input collection. For example, in judgmental rating
systems, the factors used in the ratings decision have not
traditionally been explicitly recorded. For purposes of an IRB
approach, institutions that use expert and constrained judgment must
record these factors and deliver them to the data warehouse.
For loss severity estimates, institutions must record the basic
structural characteristics of facilities and the factors used in
developing the facility rating or LGD estimate. These often include the
seniority of the credit, the amount and type of collateral, the most
recent collateral valuation date and its fair value.
Institutions must also track any overrides of the obligor or loss
severity rating. Tracking overrides separately allows risk managers to
identify whether the outcome of such overrides suggests either problems
with rating criteria, or an improper level of discretion in adjusting
the ratings.
Example Data Elements
For illustrative purposes, the following section provides examples
of the kinds of data elements institutions will collect under an IRB
data maintenance framework.
General descriptive obligor and facility data
The data below could be contained within a loan record or derived
from various sources within the data warehouse. Guarantor data
requirements are the same as for the obligor.
Obligor/Guarantor Data
[sbull] General data: name, address, industry
[sbull] ID number (unique for all related parent/sub relationships)
[sbull] Rating, date, and rater
[sbull] PD percentage corresponding to rating
General Facility Characteristics
[sbull] Facility amounts: committed, outstanding
[sbull] Facility type: Term, revolver, bullet, amortizing, etc.
[sbull] Purpose: acquisition, expansion, liquidity, inventory,
working capital
[sbull] Covenants
[sbull] Facility ID number
[sbull] Origination and maturity dates
[sbull] Last renewal date
[sbull] Obligor ID link
[sbull] Rating, date and rater
[sbull] LGD dollar amount or percentage
[sbull] EAD dollar amount or percentage
Rating Assignment Data
The data below provide an example of the categories and types of
data that institutions must retain in order to continually validate and
improve rating systems. These data items should tie directly to the
documented criteria that the institution employs in assigning ratings,
both qualitative and quantitative. For example, rating criteria often
include ranges of leverage or cash flow for a particular obligor
rating. In addition, qualitative factors, such as management
effectiveness can be recorded in numeric form. For example, a 1 may
equate to exceptionally strong management, and a 5 to very weak. The
rating data elements collected should be complete enough so that others
can review the relevant factors driving the rating decisions.
Quantitative Factors in Obligor Ratings
[sbull] Asset and sale size
[sbull] Key ratios used within rating criteria:
--profitability,
--cash flow,
--leverage,
--liquidity, and
--other relevant factors.
Qualitative Factors in Obligor Ratings
[sbull] Quality of earnings and cash flow
[sbull] Management effectiveness, reliability
[sbull] Strategic direction, industry outlook, position
[sbull] Country factors and political risk
[sbull] Other relevant factors
External Factors in Obligor Ratings
[sbull] Public debt rating and trend
[sbull] External credit model score and trend
Rating Notations
[sbull] Flag for overrides or exceptions
[sbull] Authorized individual for changing rating
Key Facility Factors in LGD Ratings
[sbull] Seniority
[sbull] Collateral type: (cash, marketable securities, AR, stock,
RE, etc.)
[sbull] Collateral value and valuation date
[sbull] Advance rates, LTV
[sbull] Industry
[sbull] Geography
Rating Notations
[sbull] Flag for overrides or exceptions
[sbull] Authorized individual for changing rating
Final Disposition Data
Only recently have institutions begun to collect more complete data
about a loan's disposition. Many institutions maintain subsidiary
systems for their problem credits with details recorded, at times
manually, on systems that were not linked with the institution's
central loan or risk management systems. The unlinked data are a
significant hindrance in developing reliable PD, LGD, and EAD
estimates.
In advanced systems, the ``grave'' portion of obligor and exposure
tracking is an essential component for producing and validating risk
estimates and is an important feedback mechanism for adjusting and
improving risk estimates over time. Essential data elements are
outlined below.
Obligor/Guarantor
[sbull] Default date
[sbull] Circumstances of default (for example, nonaccrual,
bankruptcy chapters 7-11, nonpayment)
Facility
[sbull] Outstandings at default
[sbull] Amounts undrawn and outstanding plus time series prior to
and through default
Disposition
[sbull] Amounts recovered and dates (including source: cash,
collateral, guarantor, etc.)
[sbull] Collection cost and dates
[sbull] Discount factors to determine economic cost of collection
[sbull] Final disposition (for example, restructuring or sale)
[sbull] Sales price, if applicable
[sbull] Accounting items (charge-offs to date, purchased discounts)
C. Data Element Functions
S. Data elements must be of sufficient depth, scope, and
reliability to:
[sbull] Validate IRB system processes,
[sbull] Validate parameters,
[sbull] Refine the IRB system,
[sbull] Develop internal parameter estimates,
[sbull] Apply improvements historically,
[sbull] Calculate capital ratios,
[sbull] Produce internal and public reports, and
[sbull] Support risk management.
Validation and Refinement
The data elements collected by institutions must be capable of
meeting
[[Page 45972]]
the validation requirements described in Chapters 1 and 2. These
requirements include validating the institution's IRB system processes,
including the ``front end'' aspects such as assigning ratings so that
any issues can be identified early. The data must support efforts to
identify whether raters and models are following rating criteria and
policies and whether ratings are consistent across portfolios. In
addition, data must support the validation of parameters, particularly
the comparison of realized outcomes with estimates. Thorough data on
default and disposition characteristics are of paramount importance for
parameter back-testing.
A rich source of data for validation efforts provides insights on
the performance of the IRB system, and contributes to a learning
environment in which refinements can be made to the system. These
potential refinements include enhancements to rating assignment
controls, processes, criteria or model coefficients, rating system
architecture and parameter estimates.
Developing Parameter Estimates
As detailed in Chapter 2, institutions will be developing their PD,
LGD, and EAD parameter estimates using reference data sets comprised of
internal, pooled, and external data. Institutions are expected to work
toward eventually using as much of their own experience as possible in
their reference data sets.
Applying Rating System Improvements Historically
For loss severity estimates, institutions must record the basic
structural characteristics of facilities and the factors used in
developing the facility rating or LGD estimate. These often include the
seniority of the credit, the amount and type of collateral, the most
recent collateral valuation date and its fair value.
To maintain a consistent series of information for credit risk
monitoring and validation purposes, institutions need to be able to
apply historically improvements they make to their rating systems. In
the example below, a bank experiences unexpected and rapid migrations
and defaults in its grade 4 category during 2006. Analysis of the
actual financial condition of borrowers that defaulted compared with
those that did not suggests the debt-to-EBITDA range for its expert
judgment criteria of 3.0 to 5.5 is too broad. Research indicates that
grade 4 should be redefined to include only borrowers with debt-to-
EBITDA ratios of 3.0-4.5 and grade 5 as 4.5-6.5. In 2007, the change is
initiated, but prior years' numbers are not recast (see Exhibit A).
Consequently, a break in the series prevents the bank from evaluating
credit quality changes over several years and from identifying whether
applying the new rating criteria historically provides reasonable
results.
[GRAPHIC] [TIFF OMITTED] TN04AU03.007
Recognizing the need to provide senior managers and board members
with a consistent risk trend, the new criteria are applied historically
to obligors in grades 4 and 5 as reflected in Exhibit B. The original
ratings assigned to the grades are maintained along with notations
describing what the grade would be under the new rating criteria. If
the precise weight an expert has given one of the redefined criteria is
unknown, institutions are expected to make estimates on a best efforts
basis. After the retroactive reallocation process, the bank observes
that the mix of obligors in grade 5 declined somewhat over the past
several years while the mix in grade 4 increased slightly. This
contrasts with the trend identified before the retroactive
reallocation. The result is that the multiyear transition statistics
for grades 4 and 5 provide risk managers a clearer picture of risk.
[[Page 45973]]
[GRAPHIC] [TIFF OMITTED] TN04AU03.002
This example is based on applying ratings historically using data
already collected by the bank. However, for some rating system
refinements, institutions may identify in the future drivers of default
or loss that might not have been collected for borrowers or facilities
in the past. That is why institutions are encouraged to collect data
that they believe may serve as a stronger predictor of default in the
future. For example, certain elements of a borrower's cash flow might
currently be suspected to overstate actual operational health for a
particular industry. In the future, should an institution decide to
deduct this item from cash flow with a resulting downgrade of many
obligor ratings, the institution that collected these data could apply
this rating change for prior years. This would provide the benefit of
providing a consistent picture of risk over time and also present
opportunities to validate the new criteria using historical data.
Recognizing that institutions will not be able to anticipate fully the
data they might find useful in the future, institutions are expected to
reallocate grades on a best efforts basis when practical.
Calculating Capital Ratios and Reporting to the Public
Data retained by the bank will be essential for regulatory risk-
based capital calculations and public reporting under the Pillar 3
disclosures. These uses underscore the need for a well-defined data
maintenance framework and strong controls over data integrity. Control
processes and data elements themselves should also be subject to
periodic verification and testing by internal and external auditors.
Supervisors will rely on these processes and also perform testing as
circumstances warrant.
Supporting Risk Management
The information that can be gleaned from more extensive data
collection will support a broad range of risk management activities.
Risk management functions will rely on accurate and timely data to
track credit quality, make informed portfolio risk mitigation
decisions, and perform portfolio stress tests. Trends developed from
obligor and facility risk rating data will be used to support internal
capital allocation models, pricing models, ALLL calculations, and
performance management measures, among others. Summaries of these are
included in reports to institutions' boards of directors, regulators,
and in public disclosures.
D. Managing Data Quality and Integrity
Because data are collected at so many different stages involving a
variety of groups and individuals, there are numerous challenges to
ensuring the quality of the data. For example:
[sbull] Data will be retained over long timeframes,
[sbull] Qualitative risk-rating variables will have subjective
elements and will be open to interpretation, and
[sbull] Exposures will be acquired through mergers and purchases,
but without an adequate and easily retrievable institutional rating
history.
Documentation and Definitions
S. Institutions must document the process for delivering, retaining
and updating inputs to the data warehouse and ensuring data integrity.
Given the many challenges presented by data for an IRB system, the
management of data must be formalized. Fully documenting how the
institution's flow of data is managed provides a means for evaluating
whether the data maintenance framework is functioning as intended.
Moreover, institutions must be able to communicate to individuals
developing or delivering various data the precise definition of the
items intended to be collected. Consequently, a ``data dictionary'' is
necessary to ensure consistent inputs from individuals and data vendors
and to allow third parties (such as the rating system review function,
auditors, or bank supervisors) to evaluate data quality and integrity.
S. Institutions must develop comprehensive definitions for the data
elements used within each credit group or business line (a ``data
dictionary'').
Electronic Storage
S. Institutions must store data in electronic format to allow
timely retrieval for analysis, validation of risk rating systems, and
required disclosures.
To meet the significant data management challenges presented by the
validation and control features of an IRB system, institutions will
need to store their data electronically. Institutions will have a
variety of storage techniques and potentially a variety of systems to
create their data
[[Page 45974]]
warehouses. IRB data requirements can be achieved by melding together
existing accounting, servicing, processing, workout and risk management
systems, provided the linkages among these systems are well documented
and include sufficient edit and integrity checks to ensure the data can
be used reliably.
Institutions without electronic databases would need to resort to
manual reviews of paper files for ongoing back-testing and ad hoc
``forensic'' data mining and would be unable to perform that work in
the timely and comprehensive manner required of IRB systems. Forensic
mining of paper files to build an initial data warehouse from the
institution's credit history is encouraged. In some instances, paper
research may be necessary to identify data elements or factors not
originally considered significant in estimating the risk of a
particular class of obligor or facility.
Data Gaps
Rating histories are often lost or are irretrievable for loans
acquired through mergers, acquisitions, or portfolio purchases.
Institutions are encouraged wherever practical to collect any missing
historical rating assignment driver data and to re-grade the acquired
obligors and facilities for prior periods. In cases where retrieving
historical data is not practical, institutions may attempt to create a
rating history through a careful mapping of the legacy system and the
new rating structure. Mapped ratings should be reviewed thoroughly for
accuracy. The level of effort placed on filling data gaps should be
commensurate with the size of the new exposures to be newly
incorporated into the institution's IRB system.
V. Control and Oversight Mechanisms
A. Overview
Banks' internal rating systems are the foundation for credit-risk
management practices and play an important role in pricing, reserving,
portfolio management, performance measurement, economic capital
modeling, and long-term capital planning. Banks adopting the IRB
approach will also use their credit-risk ratings to determine
regulatory capital levels. The pivotal and varied uses of such risk
ratings put enormous, sometimes conflicting, pressure on banks'
internal rating systems. The consequences of inaccurate ratings and
their associated estimates are significant, particularly as they affect
minimum regulatory capital requirements.
As risk ratings and their related parameters become better
integrated in institutions' decision making, conflicting incentives
arise that, if not well managed, can lead to overly optimistic or
biased ratings. For example, sales and marketing staff (relationship
managers or RMs) are typically compensated according to the volume of
business they generate. That may predispose the RMs to assign more
favorable ratings in order to achieve rate-of-return and sales
objectives. More favorable ratings may create the appearance of higher
risk-adjusted returns and business line profitability. Banks need to be
aware of the full range of incentive conflicts that arise, and must
develop effective controls to keep these incentive conflicts in check.
Banks will have latitude in designing and implementing their
control structures subject to the following principle:
IRB institutions must implement a system of controls that includes
the following elements: independence, transparency, accountability, use
of ratings, rating system review, internal audit, and board and senior
management oversight. While banks will have flexibility in how these
elements are combined, they must incorporate sufficient checks and
balances to ensure that the credit risk management system is
functioning properly.
Banks additionally will want to embody the following more generic
principles in their control system: separation of duties, balancing
incentives, and layers of review. Table 4.1 lists the key components of
an IRB control and oversight system. How these control mechanisms can
best be combined to reinforce one another is a key challenge for banks
implementing IRB systems:
Table 4.1 Control and Oversight Mechanisms
[GRAPHIC] [TIFF OMITTED] TN04AU03.003
[[Page 45975]]
As the following examples indicate, how a bank conducts its
business will influence how it designs its control structure. A bank
using an expert-judgment system will likely establish a different set
of controls than a bank using mainly models. Recognizing that its
expert-judgment system is less than fully transparent, a bank could
offset this vulnerability by opting for complete independence in the
rating approval process and an enhanced rating system review.
Other considerations would influence the choice of controls when
banks use models to assign ratings. While the ratings produced by
models are transparent, a model's performance depends on how well the
model was developed, the model's logic, and the quality of the data
used to implement the model. Banks that use models to assign ratings
must implement a system of controls that addresses model development,
testing and implementation, data integrity and overrides. These
activities would be covered by a comprehensive and independent rating
system review and by ongoing spot checks on the accuracy of model
inputs. Other control mechanisms such as accountability and audit would
also be required.
B. Independence in the Rating Approval Process
An independent rating process is one in which the parties
responsible for approving ratings and transactions are separate from
sales and marketing and in which the persons approving ratings are
principally compensated on risk-rating accuracy. As relative
independence increases, the likelihood of accurate ratings assignments
grows markedly.
S. Ratings must be subject to independent approval or review.
One way institutions can better achieve objective and accurate risk
ratings is by ensuring that its rating approval process is independent.
Institutions that firmly separate sales/marketing from credit are
better able to manage the conflict between the goal of high sales
volume and the need for good credit quality. An institution whose
rating process is less independent must compensate by strengthening
other control and oversight mechanisms. A significant factor in the
evaluation of the rating system will be the assessment of whether such
compensating controls are sufficient to offset a less-than-independent
ratings process. While the overriding objective is to achieve
independence in the rating approval process, in some instances, the
relative materiality of a portfolio and cost/benefit trade-offs may
support a less rigorous control process.
The degree of independence achieved in the rating process depends
on how an institution is organized and how it conducts its lending
activities.
Rating Approval Processes
Responsibility for recommending and approving ratings varies by
institution and, quite often, by portfolio.\7\ At some institutions,
ratings are assigned and approved by relationship managers (RMs); at
others, deal teams assign ratings that are later approved by credit
officers. Still other institutions have independent credit officers
assign and approve ratings. The culture of an institution and its
business mix generally determine whether the business line or credit
function is ultimately responsible for ratings.
---------------------------------------------------------------------------
\7\ Rating processes vary by institution but generally involve
an ``assignor'' and an ``approver.'' For instance, at many
organizations the rating assignor is the person who ``owns'' the
relationship (such as a ``relationship manager'') and the rating
approver is an individual with credit authority (a ``credit risk
manager''). In some cases, the rating assignor and approver are the
same. Banks that separate the rating assignment and approval
processes do so in order to minimize potential conflicts of interest
and the potential for rating errors.
---------------------------------------------------------------------------
The subsections that follow describe various rating assignment and
approval structures used by banking organizations and the challenges
that emerge in ensuring objective and consistent ratings. Any of the
following structures can work as long as ratings are subject to an
independent approval or review process, and are not unduly influenced
by the line of business:
Relationship Managers. As noted earlier, relationship managers are
primarily responsible for marketing the bank's products and services,
and their compensation is tied to the volume of business they generate.
When RMs also have responsibility for assigning and approving ratings,
there is an inherent conflict of interest. Credit quality and the
ability to produce timely and accurate risk ratings are generally not
major factors in an RM's compensation, even when he or she has
responsibility for assigning and approving ratings. In addition, RMs
also may become too close to the borrower to maintain their objectivity
and remain unbiased. When banks delegate rating responsibility to RMs,
they must offset the lack of independence with rigorous controls to
prevent bias from affecting the rating process. Such controls must
operate in practice, not just on paper, and would include, at a
minimum, a comprehensive, independent post-closing review of ratings by
a rating system review function.
Deal Team. Some major banks employ a ``deal-team'' structure for
credit origination and rating assignment. Using this approach, all
members of the team--credit officers, investment bankers, underwriters,
and others--contribute to analyzing creditworthiness, underwriting the
deal, and assigning ratings.
On the one hand, deal teams increase the access of credit officers
to information on obligors and transactions early in the underwriting
process, enabling them to make more informed credit decisions and to
influence facility structure to address obligors' weaknesses. On the
other hand, participation in the deal team could compromise the credit
officer's objectivity. While credit officers typically report to an
independent credit-risk-management function, they also have allegiance
to the deal team that reports to executives within the sales and
marketing line of business. In addition, credit officers may defer to
the members of the team whose compensation is based on the revenue and
sales volume they generate for the bank. Banks that maintain deal teams
must ensure that the credit officer's independence is safeguarded
through independent reporting lines and well-defined performance
measures (e.g., adherence to policy, rating accuracy and timeliness).
Credit Officers. Some banks give sole responsibility for assigning
and approving ratings to credit officers who report to an independent
credit function. In addition to assigning and approving and assigning
initial ratings, credit officers regularly monitor the condition of
obligors and refresh ratings as necessary. The potential downside of
this structure is that these credit officers may have limited access to
borrower information. Those credit officers that have a separate
reporting line and whose compensation is principally based on their
risk-rating accuracy are typically more independent than RMs or deal
teams.
Models. At some institutions, models assign ratings directly; at
other institutions, models and judgment are combined to rate credits.
Models introduce a high degree of independence to the rating process,
but they too require human oversight and controls. Banks that use
models must incorporate an independent judgmental review of the rating
assignments to ensure that all relevant information is considered and
to identify potential rating errors. Judgmental reviews are also needed
when model outputs are
[[Page 45976]]
overridden. In addition, controls are needed to ensure accuracy of data
inputs. When a bank uses a model to assign risk ratings, an individual
obligor's rating is ``transparent.'' However, the model itself is not
``transparent'' without a great deal of effort to document how the
model functions.
C. Transparency
Transparency is the ability of a third party, such as rating system
reviewers, auditors or bank supervisors, to observe how the rating
system operates and to understand the pertinent characteristics of
individual ratings.
S. IRB institutions must have a transparent rating system.
Transparency in a rating system is achieved through documentation
that covers the following:
[sbull] The rating system's design, purpose, performance horizon,
and performance standards;
[sbull] The rating assignment process, including procedures for
adjustments and overrides;
[sbull] Rating definitions and criteria, scorecard criteria, and
model specifications;
[sbull] Parameter estimates and the process for their estimation;
[sbull] Definition of the data elements to be warehoused to support
controls, oversight, validation, and parameter estimation; and
[sbull] Specific responsibilities of, and performance standards
for, individuals and units involved in the rating system and its
oversight.
Transparency allows third parties (such as rating system review,
auditors, or supervisors) to evaluate whether the rating system is
performing as intended. Without transparency, it is difficult to hold
people accountable for ratings errors and to validate the performance
of the system.
S. Rating criteria must be clear and specific and must include
qualitative and quantitative factors.
To produce transparent individual ratings, a bank's policies must
contain clear, detailed ratings definitions. Banks should specify
criteria for each factor that raters must consider, which may require
unique rating definitions for certain industries. Banks should consider
criteria for factors such as liquidity, sales and profitability, debt
service and fixed charge coverage, minimum equity support, position
within the industry, strength of management. A rating system with vague
criteria or one merely defined by PDs or LGDs is not transparent. For
example, the following rating definitions are not transparent because
they require the rater to do too much interpreting:
Borrower exhibits satisfactory quality and demonstrates acceptable
principal and interest repayment capacity in the near term.
Lower tier company in a cyclical industry. Unbalanced position with
tight liquidity and high leverage. Declining or erratic profitability
and marginal debt service capacity. Management is untested.
D. Accountability
``Accountability'' is holding people responsible for their actions
and establishing adverse consequences for inaccurate ratings.
S. Policies must identify the parties responsible for rating
accuracy and rating system performance.
For accountability to be effective, it should be both observable
and ingrained in the culture. Persons who assign and approve rate
credits, derive parameter estimates, or oversee rating systems must be
held accountable for complying with rating system policies and ensuring
that aspects of the rating system within their control are as unbiased
and accurate as possible. These persons must have the tools and
resources necessary to carry out their responsibilities, and their
performance should be evaluated against clear and specific objectives
documented in policy.
Responsibility for Assigning Ratings
S. Individuals must be held accountable for complying with rating
system policies and for assigning accurate ratings, and their
performance and compensation must be linked to well-defined measurable
performance standards.
Responsibilities of raters should be clear, and performance should
be measured against specific objectives. Performance evaluation and
incentive compensation should be tied to performance goals. Examples of
performance measures include:
[sbull] Number and frequency of rating errors,
[sbull] Significance of errors (for example, multiple downgrades),
and
[sbull] Proper and consistent application of criteria, including
override criteria.
Responsibility for Rating System Performance
Just as individuals will be held accountable for the accuracy of
ratings, an individual must be held responsible for the overall
performance of the rating system. This individual must ensure that the
rating system and all of its component parts--rating assignments,
parameter estimation, data collection, control and oversight
mechanisms--are functioning as intended. While these components often
are housed within separate units of the organization, an individual
must be responsible for ensuring that the parts work together
effectively and efficiently.
E. Use of Ratings
S. Ratings used for regulatory capital must be the same ratings
used to guide day-to-day credit risk management activities.
The different uses and applications of the risk-rating system's
outputs should promote greater accuracy and consistency of credit-risk
evaluations across an organization. Ratings and the associated default,
loss, and EAD estimates need to be incorporated within the credit-risk
management, internal capital allocation, and corporate governance
functions of IRB banks.
S. Banks that use parameter estimates for risk management that are
different from those used for regulatory capital must provide a well-
documented rationale for the differences.
PD and LGD parameters used for regulatory capital purposes may not
be appropriate for other uses purposes. For example, PD estimates used
to estimate reserve needs could reflect current economic conditions
that are different from the longer term view appropriate to
calculations of regulatory capital. When banks employ different
estimates, those parameters must be defensible and supported by the
following:
[sbull] Qualitative and quantitative analysis of the logic and
rationale for the difference(s); and
[sbull] Senior management approval of the difference(s).
F. Rating System Review (RSR)
S. Banks must have a comprehensive, coordinated, independent review
process to ensure that ratings are accurate and that the rating system
is performing as intended.
Rating system review (RSR) ensures that the rating system as a
whole is functioning as intended. A broad range of responsibilities
come under RSR's purview, as outlined in Table 4.2:
Table 4.2.--Responsibilities of Rating System Review
------------------------------------------------------------------------
-------------------------------------------------------------------------
Scope of Review:
Design of the rating system.
Compliance with policies and procedures, including application of
criteria.
Check of all risk-rating grades for accuracy.
Consistency across industries/portfolios/geographies.
[[Page 45977]]
Model development.
Model use, including inputs and outputs.
Overrides and policy exceptions.
Quantification process.
Back-testing (perform or review).
Actual and predicted ratings transitions.
Benchmarking against third-party data sources (perform or review).
Adequacy of data maintenance.
Analysis and Reporting:
Identify errors and flaws.
Recommend corrective action.
------------------------------------------------------------------------
For each of these responsibilities, RSR is largely checking and
confirming the work of others and ensuring that the rating system's
components work well together. RSR's testing and review should identify
current and potential weaknesses and should lead to recommendations and
corrective action such as
[sbull] Adjusting policies and procedures,
[sbull] Requiring additional training of staff,
[sbull] Investing in infrastructure improvements,
[sbull] Adjusting rating criteria, and
[sbull] Adjusting parameter estimates.
S. Rating system review must report significant findings to senior
management and the board quarterly.
RSR's role is to identify issues and areas of concern and report
findings to the area that is accountable. When issues are systematic,
RSR should bring them to the attention of senior management and the
board.
The activities of this function could be distributed across
multiple areas or housed within one unit. Organizations will choose a
structure that fits within their management and oversight framework.
These units must always have high standing within the organization and
should be staffed by individuals possessing the requisite stature,
skills, and experience.
Like internal audit, RSR must be independent from all in-house
designers and developers (that is, system and model designers) and
raters (that is, ratings and parameter assigners) in the risk-rating
process. RSR's independence eliminates potential conflicts of interest
and gives the group credibility when it reports findings and
conclusions to the board and senior management.
G. Internal Audit
S. An independent internal audit function must determine whether
rating system controls function as intended.
S. Internal audit must evaluate annually whether the bank is in
compliance with the risk-based capital regulation and supervisory
guidance.
Internal audit determines whether the bank's system of controls
over internal ratings and the related parameters is robust. In its
evaluation of controls, internal audit must consider any trade-offs
made between the various mechanisms and confirm their continued
appropriateness and relevance. As part of its review of control
mechanisms, audit will evaluate the depth, scope, and quality of RSR's
work and will conduct limited testing to ensure that their conclusions
are well founded. The amount of testing will depend on whether audit is
the primary or secondary reviewer of that work.
Internal audit will report to the board and management on whether
the bank is in compliance with the IRB standards. This report will
allow the board and management to disclose that its rating processes
and the controls surrounding these processes are in compliance with the
IRB standards. This will be critical for public disclosure and ongoing
work of supervisors.
External Audit
As part of the process of certifying financial statements, external
auditors will confirm that the institution's capital position is fairly
presented. To verify that actual capital exceeds regulatory minimums
and to confirm compliance with the IRB rules, the external auditors
must ascertain that the IRB system is rating credit risk appropriately
and linking these ratings to appropriate estimates. Auditors must
evaluate the bank's internal control functions and its compliance with
the risk-based capital regulation and supervisory guidance.
H. Corporate Oversight
S. The full board or a committee of the board must approve key
elements of the IRB system.
Consistent with sound practice, bank management must ensure that a
corporate culture exists in which institutional needs are readily
identified and appropriate resources are brought to bear to rectify
shortcomings. In the IRB context, senior management and the board of
directors must ensure the objectivity and accuracy of the bank's
credit-risk management systems and approach.
Either the full board or a committee of the board should approve
key elements of the risk-rating system. Information provided to the
board should be sufficiently detailed to allow directors to confirm the
continuing appropriateness of the institution's rating approach and to
verify the adequacy of the controls supporting the rating system.
S. Senior management must ensure that all components of the IRB
system, including controls, are functioning as intended and comply with
the risk-based capital regulation and supervisory guidance.
Senior management's oversight should be even more active than that
of the board of directors. Senior management should articulate what it
expects of the technical and operational units of the risk-rating
system, as well as what it expects of the units that manage the
system's controls. To oversee the risk-rating system, senior management
must have an extensive understanding of credit policies, underwriting
standards, lending practices, and collection and recovery practices,
and must be able to understand how these factors affect default and
loss estimates. Senior management should not only oversee the controls
process (its traditional role) but also should periodically meet with
raters and validators to discuss the rating system's performance, areas
needing improvement, and the status of efforts to improve previously
identified deficiencies.
The depth and frequency of information provided to the board and
senior management must be commensurate with their oversight
responsibilities and the condition of the institution. These reports
should include the following information:
[sbull] Risk profile by grade,
[sbull] Risk rating migration across grades with emphasis on
unexpected results,
[sbull] Changes in parameter estimates by grade,
[sbull] Comparison of realized PD, LGD, and EAD rates against
expectations,
[sbull] Reports measuring changes in regulatory and economic
capital,
[sbull] Results of capital stress testing, and
[sbull] Reports generated by rating system review, audit, and other
control units.
Although all of an institution's controls must function smoothly,
independently, and in concert with the others, the direction and
oversight provided by the board and senior management are perhaps most
important to ensure that the IRB system is functioning properly.
Document 2: Draft Supervisory Guidance on Operational Risk Advanced
Measurement Approaches for Regulatory Capital
Table of Contents
I. Purpose
II. Background
III. Definitions
IV. Banking Activities and Operational Risk
V. Corporate Governance
A. Board and Management Oversight
[[Page 45978]]
B. Independent Firm-wide Risk Management Function
C. Line of Business Management
VI. Operational Risk Management Elements
A. Operational Risk Policies and Procedures
B. Identification and Measurement of Operational Risk
C. Monitoring and Reporting
D. Internal Control Environment
VII. Elements of an AMA Framework
A. Internal Operational Risk Loss Event Data
B. External Data
C. Business Environment and Internal Control Factor Assessments
D. Scenario Analysis
VIII. Risk Quantification
A. Analytical Framework
B. Accounting for Dependence
IX. Risk Mitigation
X. Data Maintenance
XI. Testing and Verification
Appendix A: Supervisory Standards for the AMA
I. Purpose
The purpose of this guidance is to set forth the expectations of
the U.S. banking agencies for banking institutions that use Advanced
Measurement Approaches (AMA) for calculating the operational risk
capital charge under the new capital regulation. Institutions using the
AMA will have considerable flexibility to develop operational risk
measurement systems appropriate to the nature of their activities,
business environment, and internal controls. An institution's
operational risk regulatory capital requirement will be calculated as
the amount needed to cover its operational risk at a level of
confidence determined by the supervisors, as discussed below. Use of an
AMA is subject to supervisory approval.
This draft guidance should be considered with the advance notice of
proposed rulemaking (ANPR) on revisions to the risk-based capital
standard published elsewhere in today's Federal Register. As with the
ANPR, the Agencies are seeking industry comment on this draft guidance.
In addition to seeking comment on all specific aspects of this
supervisory guidance, the Agencies are seeking comment on the extent to
which the supervisory guidance strikes the appropriate balance between
flexibility and specificity. Likewise, the Agencies are seeking comment
on whether an appropriate balance has been struck between the
regulatory requirements set forth in the ANPR and the supervisory
standards set forth in this guidance.
II. Background
Effective management of operational risk is integral to the
business of banking and to institutions' roles as financial
intermediaries. Although operational risk is not a new risk,
deregulation and globalization of financial services, together with the
growing sophistication of financial technology, new business activities
and delivery channels, are making institutions' operational risk
profiles (i.e., the level of operational risk across an institution's
activities and risk categories) more complex.
This guidance identifies the supervisory standards (S) that
institutions must meet and maintain to use an AMA for the regulatory
capital charge for operational risk. The purpose of the standards is to
provide the foundation for a sound operational risk framework, while
allowing institutions to identify the most appropriate mechanisms to
meet AMA requirements. Each institution will need to consider its
complexity, range of products and services, organizational structure,
and risk management culture as it develops its AMA. Operational risk
governance processes need to be established on a firm-wide basis to
identify, measure, monitor, and control operational risk in a manner
comparable with the treatment of credit, interest rate, and market
risks.
Institutions will be expected to develop a framework that measures
and quantifies operational risk for regulatory capital purposes. To do
this, institutions will need a systematic process for collecting
operational risk loss data, assessing the risks within the institution,
and adopting an analytical framework that translates the data and risk
assessments into an operational risk exposure (see definition below).
The analytical framework must incorporate a degree of conservatism that
is appropriate for the overall robustness of the quantification
process. Because institutions will be permitted to calculate their
minimum regulatory capital on the basis of internal processes, the
requirements for data capture, risk assessment, and the analytical
framework described below are detailed and specific.
Effective operational risk measurement systems are built on both
quantitative and qualitative risk assessment techniques. While the
output of the regulatory framework for operational risk is a measure of
exposure resulting in a capital number, the integrity of that estimate
depends not only on the soundness of the measurement model, but also on
the robustness of the institution's underlying risk management
processes. In addition, supervisors view the introduction of the AMA as
an important tool to further promote improvements in operational risk
management and controls at large banking institutions.
This document provides both AMA supervisory standards and a
discussion of how those standards should be incorporated into an
operational risk framework. The relevant supervisory standards are
listed at the beginning of each section and a full compilation of the
standards is provided in Appendix A. Not every section has specific
supervisory standards. When spanning more than one section, supervisory
standards are listed only once.
Institutions will be required to meet, and remain in compliance
with, all the supervisory standards to use an AMA framework. However,
evaluating an institution's qualification with each of the individual
supervisory standards will not be sufficient to determine an
institution's overall readiness for AMA. Instead, supervisors and
institutions must also evaluate how well the various components of an
institution's AMA framework complement and reinforce one another to
achieve the overall objectives of an accurate measure and effective
management of operational risk. In performing their evaluation,
supervisors will exercise considerable supervisory judgment, both in
evaluating the individual components and the overall operational risk
framework.
An institution's AMA methodology will be assessed as part of the
ongoing supervision process. This will allow supervisors to incorporate
existing supervisory efforts as much as possible into the AMA
assessments. Some elements of operational risk (e.g., internal controls
and information technology) have long been subject to examination by
supervisors. Where this is the case, supervisors will make every effort
to leverage off these examination activities to assess the
effectiveness of the AMA process. Substantive weaknesses identified in
an examination will be factored into the AMA qualification process.
III. Definitions
There are important definitions that institutions must incorporate
into an AMA framework. They are:
[sbull] Operational risk: The risk of loss resulting from
inadequate or failed internal processes, people and systems, or from
external events. The definition includes legal risk, which is the risk
of loss resulting from failure to comply with laws as well as prudent
ethical standards and contractual obligations. It also includes the
exposure to litigation from all aspects of an institution's
[[Page 45979]]
activities. The definition does not include strategic or reputational
risks.\8\
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\8\ An institution's definition of risk may encompass other risk
elements as long as the supervisory definition is met.
---------------------------------------------------------------------------
[sbull] Operational risk loss: The financial impact associated with
an operational event that is recorded in the institution's financial
statements consistent with Generally Accepted Accounting Principles
(GAAP). Financial impact includes all out-of-pocket expenses associated
with an operational event but does not include opportunity costs,
foregone revenue, or costs related to investment programs implemented
to prevent subsequent operational risk losses. Operational risk losses
are characterized by seven event factors associated with:
i. Internal fraud: An act of a type intended to defraud,
misappropriate property or circumvent regulations, the law or company
policy, excluding diversity/discrimination events, which involve at
least one internal party.
ii. External fraud: An act of a type intended to defraud,
misappropriate property or circumvent the law, by a third party.
iii. Employment practices and workplace safety: An act inconsistent
with employment, health or safety laws or agreements, from payment of
personal injury claims, or from diversity/discrimination events.
iv. Clients, products, and business practices: An unintentional or
negligent failure to meet a professional obligation to specific clients
(including fiduciary and suitability requirements), or from the nature
or design of a product.
v. Damage to physical assets: The loss or damage to physical assets
from natural disaster or other events.
vi. Business disruption and system failures: Disruption of business
or system failures.
vii. Execution, delivery, and process management: Failed
transaction processing or process management, from relations with trade
counterparties and vendors.
[sbull] Operational risk exposure: An estimate of the potential
operational losses that the banking institution faces at a soundness
standard consistent with a 99.9 per cent confidence level over a one-
year period. The institution will multiply the exposure by 12.5 to
obtain risk-weighted assets for operational risk; this is added to the
risk-weighted assets for credit and market risk to arrive at the
denominator of the regulatory capital ratio.
[sbull] Business environment and internal control factor
assessments: The range of tools that provide a meaningful assessment of
the level and trends in operational risk across the institution. While
the institution may use multiple tools in an AMA framework, they must
all have the same objective of identifying key risks. There are a
number of existing tools, such as audit scores and performance
indicators that may be acceptable under this definition.
IV. Banking Activities and Operational Risk
The above definition of operational risk gives a sense of the
breadth of exposure to operational risk that exists in banking today as
well as the many interdependencies among risk factors that may result
in an operational risk loss. Indeed, operational risk can occur in any
activity, function, or unit of the institution.
The definition of operational risk incorporates the risks stemming
from people, processes, systems and external events. People risk refers
to the risk of management failure, organizational structure or other
human resource failures. These risks may be exacerbated by poor
training, inadequate controls, poor staffing resources, or other
factors. The risk from processes stem from breakdowns in established
processes, failure to follow processes, or inadequate process mapping
within business lines. System risk covers instances of both disruption
and outright system failures in both internal and outsourced
operations. Finally, external events can include natural disasters,
terrorism, and vandalism.
There are a number of areas where operational risks are emerging.
These include:
[sbull] Greater use of automated technology has the potential to
transform risks from manual processing errors to system failure risks,
as greater reliance is placed on globally integrated systems;
[sbull] Proliferation of new and highly complex products;
[sbull] Growth of e-banking transactions and related business
applications expose an institution to potential new risks (e.g.,
internal and external fraud and system security issues);
[sbull] Large-scale acquisitions, mergers, and consolidations test
the viability of new or newly integrated systems;
[sbull] Emergence of institutions acting as large-volume service
providers create the need for continual maintenance of high-grade
internal controls and back-up systems;
[sbull] Development and use of risk mitigation techniques (e.g.,
collateral, insurance, credit derivatives, netting arrangements and
asset securitizations) optimize an institution's exposure to market
risk and credit risk, but potentially create other forms of risk (e.g.,
legal risk); and
[sbull] Greater use of outsourcing arrangements and participation
in clearing and settlement systems mitigate some risks while increasing
others.
The range of banking activities and areas affected by operational
risk must be fully identified and considered in the development of the
institution's risk management and measurement plans. Since operational
risk is not confined to particular business lines \9\, product types,
or organizational units, it should be managed in a consistent and
comprehensive manner across the institution. Consequently, risk
management mechanisms must encompass the full range of risks, as well
as strategies that help to identify, measure, monitor and control those
risks.
---------------------------------------------------------------------------
\9\ Throughout this guidance, terms such as ``business units''
and ``business lines'' are used interchangeably and refer not only
to an institution's revenue-generating businesses, but also to
corporate staff functions such as human resources or information
technology.
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V. Corporate Governance
Supervisory Standards
S 1. The institution's operational risk framework must include an
independent firm-wide operational risk management function, line of
business management oversight, and independent testing and verification
functions.
The management structure underlying an AMA operational risk
framework may vary between institutions. However, within all AMA
institutions, there are three key components that must be evident--the
firm-wide operational risk management function, lines of business
management, and the testing and verification function. These three
elements are functionally independent \10\ organizational components,
but should work in cooperation to ensure a robust operational risk
framework.
---------------------------------------------------------------------------
\10\ For the purposes of AMA, ``functional independence'' is
defined as the ability to carry out work freely and objectively and
render impartial and unbiased judgments. There should be appropriate
independence between the firm-wide operational risk management
functions, line of business management and staff and the testing/
verification functions. Supervisory assessments of independence
issues will rely upon existing regulatory guidance (e.g. audit,
internal control systems, board of directors/management, etc.)
---------------------------------------------------------------------------
A. Board and Management Oversight
Supervisory Standards
S 2. The board of directors must oversee the development of the
firm-wide operational risk framework, as
[[Page 45980]]
well as major changes to the framework. Management roles and
accountability must be clearly established.
S 3. The board of directors and management must ensure that
appropriate resources are allocated to support the operational risk
framework.
The board is responsible for overseeing the establishment of the
operational risk framework, but may delegate the responsibility for
implementing the framework to management with the authority necessary
to allow for its effective implementation. Other key responsibilities
of the board include:
[sbull] Ensuring appropriate management responsibility,
accountability and reporting;
[sbull] Understanding the major aspects of the institution's
operational risk as a distinct risk category that should be managed;
[sbull] Reviewing periodic high-level reports on the institution's
overall operational risk profile, which identify material risks and
strategic implications for the institution;
[sbull] Overseeing significant changes to the operational risk
framework; and
[sbull] Ensuring compliance with regulatory disclosure
requirements.
Effective board and management oversight forms the cornerstone of
an effective operational risk management process. The board and
management have several broad responsibilities with respect to
operational risk:
[sbull] To establish a framework for assessing operational risk
exposure and identify the institution's tolerance for operational risk;
[sbull] To identify the senior managers who have the authority for
managing operational risk;
[sbull] To monitor the institution's performance and overall
operational risk profile, ensuring that it is maintained at prudent
levels and is supported by adequate capital;
[sbull] To implement sound fundamental risk governance principles
that facilitate the identification, measurement, monitoring, and
control of operational risk;
[sbull] To devote adequate human and technical resources to
operational risk management; and
[sbull] To institute remuneration policies that are consistent with
the institution's appetite for risk and are sufficient to attract
qualified operational risk management and staff.
Management should translate the operational risk management
framework into specific policies, processes and procedures that can be
implemented and verified within the institution's different business
units. Communication of these elements will be essential to the
understanding and consistent treatment of operational risk across the
institution. While each level of management is responsible for
effectively implementing the policies and procedures within its
purview, senior management should clearly assign authority,
responsibilities, and reporting relationships to encourage and maintain
this accountability and ensure that the necessary resources are
available to manage operational risk. Moreover, management should
assess the appropriateness of the operational risk management oversight
process in light of the risks inherent in a business unit's activities.
The testing and verification function is responsible for completing
timely and comprehensive assessments of the effectiveness of
implementation of the institution's operational risk framework at the
line of business and firm-wide levels.
Management collectively is also responsible for ensuring that the
institution has qualified staff and sufficient resources to carry out
the operational risk functions outlined in the operational risk
framework. Additionally, management must communicate operational risk
issues to appropriate staff that may not be directly involved in its
management. Key management responsibilities include ensuring that:
[sbull] Operational risk management activities are conducted by
qualified staff with the necessary experience, technical capabilities
and access to adequate resources;
[sbull] Sufficient resources have been allocated to operational
risk management, in the business lines as well as the independent firm-
wide operational risk management function and verification areas, so as
to sufficiently monitor and enforce compliance with the institution's
operational risk policy and procedures; and
[sbull] Operational risk issues are effectively communicated with
staff responsible for managing credit, market and other risks, as well
as those responsible for purchasing insurance and managing third-party
outsourcing arrangements.
B. Independent Firm-Wide Risk Management Function
Supervisory Standards
S 4. The institution must have an independent operational risk
management function that is responsible for overseeing the operational
risk framework at the firm level to ensure the development and
consistent application of operational risk policies, processes, and
procedures throughout the institution.
S 5. The firm-wide operational risk management function must ensure
appropriate reporting of operational risk exposures and loss data to
the board of directors and senior management.
The institution must have an independent firm-wide operational risk
management function. The roles and responsibilities of the function
will vary between institutions, but must be clearly documented. The
independent firm-wide operational risk function should have
organizational stature commensurate with the institution's operational
risk profile, while remaining independent of the lines of business and
the testing and verification function. At a minimum, the institution's
independent firm-wide operational risk management function should
ensure the development of policies, processes, and procedures that
explicitly manage operational risk as a distinct risk to the
institution's safety and soundness. These policies, processes and
procedures should include principles for how operational risk is to be
identified, measured, monitored, and controlled across the
organization. Additionally, they should provide for the collection of
the data needed to calculate the institution's operational risk
exposure.
Additional responsibilities of the independent firm-wide
operational risk management function include:
[sbull] Assisting in the implementation of the overall firm-wide
operational risk framework;
[sbull] Reviewing the institution's progress towards stated
operational risk objectives, goals and risk tolerances;
[sbull] Periodically reviewing the institution's operational risk
framework to consider the loss experience, effects of external market
changes, other environmental factors, and the potential for new or
changing operational risks associated with new products, activities or
systems. This review process should include an assessment of industry
best practices for the institution's activities, systems and processes;
[sbull] Reviewing and analyzing operational risk data and reports;
and
[sbull] Ensuring appropriate reporting to senior management and the
board.
C. Line of Business Management
Supervisory Standards
S 6. Line of business management is responsible for the day-to-day
management of operational risk within each business unit.
S 7. Line of business management must ensure that internal controls
and
[[Page 45981]]
practices within their line of business are consistent with firm-wide
policies and procedures to support the management and measurement of
the institution's operational risk.
Line of business management is responsible for both managing
operational risk within the business lines and ensuring that policies
and procedures are consistent with and support the firm-wide
operational risk framework. Management should ensure that business-
specific policies, processes, procedures and staff are in place to
manage operational risk for all material products, activities, and
processes. Implementation of the operational risk framework within each
line of business should reflect the scope of that business and its
inherent operational complexity and operational risk profile. Line of
business management must be independent of both the firm-wide
operational risk management and the testing and verification functions.
VI. Operational Risk Management Elements
The operational risk management framework provides the overall
operational risk strategic direction and ensures that an effective
operational risk management and measurement process is adopted
throughout the institution. The framework should provide for the
consistent application of operational risk policies and procedures
throughout the institution and address the roles of both the
independent firm-wide operational risk management function and the
lines of business. The framework should also provide for the consistent
and comprehensive capture of data elements needed to measure and verify
the institution's operational risk exposure, as well as appropriate
operational risk analytical frameworks, reporting systems, and
mitigation strategies. The framework must also include independent
testing and verification to assess the effectiveness of implementation
of the institution's operational risk framework, including compliance
with policies, processes, and procedures.
In practice, an institution's operational risk framework must
reflect the scope and complexity of business lines, as well as the
corporate organizational structure. Each institution's operational risk
profile is unique and requires a tailored risk management approach
appropriate for the scale and materiality of the risks present, and the
size of the institution. There is no single framework that would suit
every institution; different approaches will be needed for different
institutions. In fact, many operational risk management techniques
continue to evolve rapidly to keep pace with new technologies, business
models and applications.
The key elements in the operational risk management process
include:
[sbull] Appropriate policies and procedures;
[sbull] Efforts to identify and measure operational risk;
[sbull] Effective monitoring and reporting;
[sbull] A sound system of internal controls; and
[sbull] Appropriate testing and verification of the operational
risk framework.
A. Operational Risk Policies and Procedures
Supervisory Standards
S 8. The institution must have policies and procedures that clearly
describe the major elements of the operational risk management
framework, including identifying, measuring, monitoring, and
controlling operational risk.
Operational risk management policies, processes, and procedures
should be documented and communicated to appropriate staff. The
policies and procedures should outline all aspects of the institution's
operational risk management framework, including:
[sbull] The roles and responsibilities of the independent firm-wide
operational risk management function and line of business management;
[sbull] A definition for operational risk, including the loss event
types that will be monitored;
[sbull] The capture and use of internal and external operational
risk loss data, including large potential events (including the use of
scenario analysis);
[sbull] The development and incorporation of business environment
and internal control factor assessments into the operational risk
framework;
[sbull] A description of the internally derived analytical
framework that quantifies the operational risk exposure of the
institution;
[sbull] An outline of the reporting framework and the type of data/
information to be included in line of business and firm-wide reporting;
[sbull] A discussion of qualitative factors and risk mitigants and
how they are incorporated into the operational risk framework;
[sbull] A discussion of the testing and verification processes and
procedures;
[sbull] A discussion of other factors that affect the measurement
of operational risk; and
[sbull] Provisions for the review and approval of significant
policy and procedural exceptions.
B. Identification and Measurement of Operational Risk
The result of a comprehensive program to identify and measure
operational risk is an assessment of the institution's operational risk
exposure. Management must establish a process that identifies the
nature and types of operational risk and their causes and resulting
effects on the institution. Proper operational risk identification
supports the reporting and maintenance of capital for operational risk
exposure and events, facilitates the establishment of mechanisms to
mitigate or control the risks, and ensures that management is fully
aware of the sources of emerging operational risk loss events.
C. Monitoring and Reporting
Supervisory Standards
S 9. Operational risk management reports must address both firm-
wide and line of business results. These reports must summarize
operational risk exposure, loss experience, relevant business
environment and internal control assessments, and must be produced no
less often than quarterly.
S 10. Operational risk reports must also be provided periodically
to senior management and the board of directors, summarizing relevant
firm-wide operational risk information.
Ongoing monitoring of operational risk exposures is a key aspect of
an effective operational risk framework. To facilitate monitoring of
operational risk, results from the measurement system should be
summarized in reports that can be used by the firm-wide operational
risk and line of business management functions to understand, manage,
and control operational risk and losses. These reports should serve as
a basis for assessing operational risk and related mitigation
strategies and creating incentives to improve operational risk
management throughout the institution.
Operational risk management reports should summarize:
[sbull] Operational risk loss experience on an institution, line of
business, and event-type basis;
[sbull] Operational risk exposure;
[sbull] Changes in relevant risk and control assessments;
[sbull] Management assessment of early warning factors signaling an
increased risk of future losses;
[sbull] Trend analysis, allowing line of business and independent
firm-wide operational risk management to assess
[[Page 45982]]
and manage operational risk exposures, systemic line of business risk
issues, and other corporate risk issues;
[sbull] Exception reporting; and
[sbull] To the extent developed, operational risk causal factors.
High-level operational risk reports must also be produced
periodically for the board and senior management. These reports must
provide information regarding the operational risk profile of the
institution, including the sources of material risk both from a firm-
wide and line of business perspective, versus established management
expectations.
D. Internal Control Environment
Supervisory Standards
S 11. An institution's internal control structure must meet or
exceed minimum regulatory standards established by the Agencies.
Sound internal controls are essential to an institution's
management of operational risk and are one of the foundations of safe
and sound banking. When properly designed and consistently enforced, a
sound system of internal controls will help management safeguard the
institution's resources, produce reliable financial reports, and comply
with laws and regulations. Sound internal controls will also reduce the
possibility of significant human errors and irregularities in internal
processes and systems, and will assist in their timely detection when
they do occur.
The Agencies are not introducing any new internal control
standards, but rather emphasizing the importance of meeting existing
standards. There is a recognition that internal control systems will
differ among institutions due to the nature and complexity of an
institution's products and services, organizational structure, and risk
management culture. The AMA standards allows for these differences,
while also establishing a baseline standard for the quality of the
internal control structure. Institutions will be expected to at least
meet the minimum interagency standards\11\ relating to internal
controls as a criterion for AMA qualification.
---------------------------------------------------------------------------
\11\ There are a number of interagency standards that cover
topics relevant to the internal control structure. These include,
for example, the Interagency Policy Statement on the Internal Audit
Function and Its Outsourcing (March 2003), the Federal Financial
Institution's Examination Council's (FFIEC's) Business Continuity
Planning Booklet (May 2003), the FFIEC's Information Security
Booklet (January 2003). In addition, each Agency has extensive
guidance on corporate governance, internal controls, and monitoring
and reporting in its respective examination policies and procedures.
---------------------------------------------------------------------------
The extent to which an institution meets or exceeds the minimum
standards will primarily be assessed through current and ongoing
supervisory processes. As noted earlier, the Agencies will leverage off
existing examination processes, to avoid duplication in assessing an
institution's implementation of an AMA framework. Assessing the
internal control environment is clearly an area where the supervisory
authorities already focus considerable attention.
VII. Elements of an AMA Framework
Supervisory Standards
S 12. The institution must demonstrate that it has appropriate
internal loss event data, relevant external loss event data,
assessments of business environment and internal controls factors, and
results from scenario analysis to support its operational risk
management and measurement framework.
S 13. The institution must include the regulatory definition of
operational risk as the baseline for capturing the elements of the AMA
framework and determining its operational risk exposure.
S 14. The institution must have clear standards for the collection
and modification of the elements of the operational risk AMA framework.
Operational risk inputs play a significant role in both the
management and measurement of operational risk. Necessary elements of
an institution's AMA framework include internal loss event data,
relevant external loss event data, results of scenario analysis, and
assessments of the institution's business environment and internal
controls. Operational risk inputs aid the institution in identifying
the level and trend of operational risk, determining the effectiveness
of risk management and control efforts, highlighting opportunities to
better mitigate operational risk, and assessing operational risk on a
forward-looking basis.
To use its AMA framework, an institution must demonstrate that it
has established a consistent and comprehensive process for the capture
of all elements of the AMA framework. The institution must also
demonstrate that it has clear standards for the collection and
modification of all AMA inputs. While the analytical framework will
generally combine these inputs to develop the operational risk
exposure, supervisors must have the capacity to review the individual
inputs as well; specifically, supervisors will need to review the loss
information that is being provided to the analytical framework that
stems from internal loss event data, versus the loss event information
provided by external loss event data capture, scenario analysis, or the
assessments of the business environment and internal control factors.
The capture systems must cover all material business lines,
business activities and corporate functions that could generate
operational risk. The institution must have a defined process that
establishes responsibilities over the systems developed to capture the
AMA elements. In particular, the issue of overriding the data capture
systems must be addressed. Any overrides should be tracked separately
and documented. Tracking overrides separately allows management and
supervisors to identify the nature and rationale, including whether
they stem from simple input errors or, more importantly, from exclusion
because a loss event was not pertinent for the quantitative
measurement. Management should have clear standards for addressing
overrides and should clearly delineate who has authority to override
the data systems and under what circumstances.
As noted earlier, for AMA qualification purposes, an institution's
operational risk framework must, at a minimum, use the definition of
operational risk that is provided in paragraph 10 when capturing the
elements of the AMA framework. Institutions may use an expanded
definition if considered more appropriate for risk management and
measurement efforts. However, for the quantification of operational
risk exposure for regulatory capital purposes, an institution must
demonstrate that the AMA elements are captured so as to meet the
baseline definition.
A. Internal Operational Risk Loss Event Data
Supervisory Standards
S 15. The institution must have at least five years of internal
operational risk loss data \12\ captured across all material business
lines, events, product types, and geographic locations.
---------------------------------------------------------------------------
\12\ With supervisory approval, a shorter initial historical
observation period is acceptable for banks newly authorized to use
an AMA methodology.
---------------------------------------------------------------------------
S 16. The institution must be able to map internal operational risk
losses to the seven loss-event type categories.
S 17. The institution must have a policy that identifies when an
operational risk loss becomes a loss event and must be added to the
loss
[[Page 45983]]
event database. The policy must provide for consistent treatment across
the institution.
S 18. The institution must establish appropriate operational risk
data thresholds.
S 19. Losses that have any characteristics of credit risk,
including fraud-related credit losses, must be treated as credit risk
for regulatory capital purposes. The institution must have a clear
policy that allows for the consistent treatment of loss event
classifications (e.g., credit, market, or operational risk) across the
organization.
The key to internal data integrity is the consistency and
completeness with which loss event data capture processes are
implemented across the institution. Management must ensure that
operational risk loss event information captured is consistent across
the business lines and incorporates any corporate functions that may
also experience operational risk events. Policies and procedures should
be addressed to the appropriate staff to ensure that there is
satisfactory understanding of operational risk and the data capture
requirements under the operational risk framework. Further, the
independent operational risk management function must ensure that the
loss data is captured across all material business lines, products
types, event types, and from all significant geographic locations. The
institution must be able to capture and aggregate internal losses that
cross multiple business lines or event types. If data is not captured
across all business lines or from all geographic locations, the
institution must document and explain the exceptions.
AMA institutions must be able to map operational risk losses into
the seven loss event categories defined in paragraph 10. Institutions
will not be required to produce reports or perform analysis for
internal purposes on the basis of the loss event categories, but will
be expected to use the information about the event-type categories as a
check on the comprehensiveness of the institution's data set.
The institution must have five years of internal loss data,
although a shorter range of historical data may be allowed, subject to
supervisory approval. The extent to which an institution collects
operational risk loss event data will, in part, be dependent upon the
data thresholds that the institution establishes. There are a number of
standards that an institution may use to establish the thresholds. They
may be based on product types, business lines, geographic location, or
other appropriate factors. The Agencies will allow flexibility in this
area, provided the institution can demonstrate that the thresholds are
reasonable, do not exclude important loss events, and capture a
significant proportion of the institution's operational risk losses.
The institution must capture comprehensive data on all loss events
above its established threshold level. Aside from information on the
gross loss amount, the institution should collect information about the
date of the event, any recoveries, and descriptive information about
the drivers or causes of the loss event. The level of detail of any
descriptive information should be commensurate with the size of the
gross loss amount. Examples of the type of information collected
include:
[sbull] Loss amount;
[sbull] Description of loss event;
[sbull] Where the loss is reported and expensed;
[sbull] Loss event type category;
[sbull] Date of the loss;
[sbull] Discovery date of the loss;
[sbull] Event end date;
[sbull] Management actions;
[sbull] Insurance recoveries;
[sbull] Other recoveries; and
[sbull] Adjustments to the loss estimate.
There are a number of additional data elements that may be
captured. It may be appropriate, for example, to capture data on ``near
miss'' events, where no financial loss was incurred. These near misses
will not factor into the regulatory capital calculation, but may be
useful for the operational risk management process.
Institutions will also be permitted and encouraged to capture loss
events in their operational risk databases that are treated as credit
risk for regulatory capital purposes, but have an underlying element of
operational risk failure. These types of events, while not incorporated
into the regulatory capital calculation, may have implications for
operational risk management. It will be essential for institutions that
capture loss events that are treated differently for regulatory capital
and management purposes to demonstrate that (1) loss events are being
captured consistently across the institution; (2) the data systems are
sufficiently advanced to allow for this differential treatment of loss
events; and (3) credit, market, and operational risk losses are being
appropriated in the correct manner for regulatory capital purposes.
The Agencies have established a clear boundary between credit and
operational risks for regulatory capital purposes. If a loss event has
any element of credit risk, it must be treated as credit risk for
regulatory capital purposes. This would include all credit-related
fraud losses. In addition, operational risk losses with credit risk
characteristics that have historically been included in institutions'
credit risk databases will continue to be treated as credit risk for
the purposes of calculating minimum regulatory capital.
The accounting guidance for credit losses provides that creditors
recognize credit losses when it is probable that they will be unable to
collect all amounts due according to the contractual terms of a loan
agreement. Credit losses may result from the creditor's own
underwriting, processing, servicing or administrative activities along
with the borrower's failure to pay according to the terms of the loan
agreement. While the creditor's personnel, systems, policies or
procedures may affect the timing or magnitude of a credit loss, they do
not change its character from credit to operational risk loss for
regulatory capital purposes. Losses that arise from a contractual
relationship between a creditor and a borrower are credit losses
whereas losses that arise outside of a relationship between a creditor
and a borrower are operational losses.
B. External Data
Supervisory Standards
S 20. The institution must have policies and procedures that
provide for the use of external loss data in the operational risk
framework.
S 21. Management must systematically review external data to ensure
an understanding of industry experience.
External data may serve a number of different purposes in the
operational risk framework. Where internal loss data is limited,
external data may be a useful input in determining the institution's
level of operational risk exposure. Even where external loss data is
not an explicit input to an institution's data set, such data provides
a means for the institution to understand industry experience, and in
turn, provides a means for assessing the adequacy of its internal data.
External data may also prove useful to inform scenario analysis, fit
severity distributions, or benchmark the overall operational risk
exposure results.
To incorporate external loss information into an institution's
framework, the institution should collect the following information:
[sbull] External loss amount;
[sbull] External loss description;
[sbull] Loss event type category;
[sbull] External loss event date;
[sbull] Adjustments to the loss amount (i.e., recoveries, insurance
settlements,
[[Page 45984]]
etc) to the extent that they are known; and
[sbull] Sufficient information about the reporting institution to
facilitate comparison to its own organization.
Institutions may obtain external loss data in any reasonable
manner. There are many ways to do so; some institutions are using data
acquired through membership with industry consortia while other
institutions are using data obtained from vendor databases or public
sources such as court records or media reports. In all cases,
management will need to carefully evaluate the data source to ensure
that they are comfortable that the information being reported is
relevant and reasonably accurate.
C. Business Environment and Internal Control Factor Assessments
Supervisory Standards
S 22. The institution must have a system to identify and assess
business environment and internal control factors.
S 23. Management must periodically compare the results of their
business environment and internal control factor assessments against
actual operational risk loss experience.
While internal and external loss data provide a historical
perspective on operational risk, it is also important that institutions
incorporate a forward-looking element to the operational risk measure.
In principle, an institution with strong internal controls in a stable
business environment will have less exposure to operational risk than
an institution with internal control weaknesses that is growing rapidly
or introducing new products. In this regard, institutions will be
required to identify the level and trends in operational risk in the
institution. These assessments must be current, comprehensive across
the institution, and identify the critical operational risks facing the
institution.
The business environment and internal control factor assessments
should reflect both the positive and negative trends in risk management
within the institution as well as changes in an institution's business
activities that increase or decrease risk. Because the results of the
risk assessment are part of the capital methodology, management must
ensure that the risk assessments are done appropriately and reflect the
risks of the institution. Periodic comparisons should be made between
actual loss exposure and the assessment results.
The framework established to maintain the risk assessments must be
sufficiently flexible to encompass an institution's increased
complexity of activities, new activities, changes in internal control
systems, or an increased volume of information.
D. Scenario Analysis
Supervisory Standards
S 24. Management must have policies and procedures that identify
how scenario analysis will be incorporated into the operational risk
framework.
Scenario analysis is a systematic process of obtaining expert
opinions from business managers and risk management experts to derive
reasoned assessments of the likelihood and impact of plausible
operational losses consistent with the regulatory soundness standard.
Within an institution's operational risk framework, scenario analysis
may be used as an input or may, as discussed below, form the basis of
an operational risk analytical framework.
As an input to the institution's framework, scenario analysis is
especially relevant for business lines or loss event types where
internal data, external data, and assessments of the business
environment and internal control factors do not provide a sufficiently
robust estimate of the institution's exposure to operational risk. In
some cases, an institution's internal loss history may be sufficient to
provide a reasonable estimate of exposure to future operational losses.
In other cases, the use of well-reasoned, scaled external data may
itself be a form of scenario analysis.
The institution must have policies and procedures that define
scenario analysis and identify its role in the operational risk
framework. The policy should cover key elements of scenario analysis,
such as the manner in which the scenarios are generated, the frequency
with which they are updated, and the scope and coverage of operational
loss events they are intended to reflect.
VIII. Risk Quantification
A. Analytical Framework
Supervisory Standards
S 25. The institution must have a comprehensive operational risk
analytical framework that provides an estimate of the institution's
operational risk exposure, which is the aggregate operational loss that
it faces over a one-year period at a soundness standard consistent with
a 99.9 per cent confidence level.
S 26. Management must document the rationale for all assumptions
underpinning its chosen analytical framework, including the choice of
inputs, distributional assumptions, and the weighting across
qualitative and quantitative elements. Management must also document
and justify any subsequent changes to these assumptions.
S 27. The institution's operational risk analytical framework must
use a combination of internal operational loss event data, relevant
external operational loss event data, business environment and internal
control factor assessments, and scenario analysis. The institution must
combine these elements in a manner that most effectively enables it to
quantify its operational risk exposure. The institution can choose the
analytical framework that is most appropriate to its business model.
S 28. The institution's capital requirement for operational risk
will be the sum of expected and unexpected losses unless the
institution can demonstrate, consistent with supervisory standards, the
expected loss offset.
The industry has made significant progress in recent years in
developing analytical frameworks to quantify operational risk. The
analytical frameworks, which are a part of the overall operational risk
framework, are based on various combinations of an institution's own
operational loss experience, the industry's operational loss
experience, the size and scope of the institution's activities, the
quality of the institution's control environment, and management's
expert judgment. Because these models capture specific characteristics
of each institution, such models yield unique risk-sensitive estimates
of the institutions' operational risk exposures.
While the Agencies are not specifying the exact methodology that an
institution should use to determine its operational risk exposure,
minimum supervisory standards for acceptable approaches have been
developed. These standards have been set so as to assure that the
regulation can accommodate continued evolution of operational risk
quantification techniques, yet remain amenable to consistent
application and enforcement across institutions. The Agencies will
require that the institution have a comprehensive analytical framework
that provides an estimate of the aggregate operational loss that it
faces over a one-year period at a soundness standard consistent with a
99.9 percent confidence level, referred to as the institution's
operational risk exposure. The institution will multiply the exposure
estimate by 12.5 to obtain risk weighted assets for operational risk,
[[Page 45985]]
and add this figure to risk-weighted assets for credit and market risk
to obtain total risk-weighted assets. The final minimum regulatory
capital number will be 8 percent of total risk-weighted assets.
The Agencies expect that there will be significant variation in
analytical frameworks across institutions, with each institution
tailoring its framework to leverage existing technology platforms and
risk management procedures. These approaches may only be used, provided
they meet the supervisory standards and include, as inputs, internal
operational loss event data, relevant external operational loss event
data, assessments of business environment and internal control factors,
and scenario analysis. The Agencies do expect that there will be some
uncertainty and potential error in the analytical frameworks because of
the evolving nature of operational risk measurement and data capture.
Therefore, a degree of conservatism will need to be built into the
analytical frameworks to reflect the evolutionary status of operational
risk and its impact on data capture and analytical modeling.
A diversity of analytical approaches is emerging in the industry,
combining and weighting these inputs in different ways. Most current
approaches seek to estimate loss frequency and loss severity to arrive
at an aggregate loss distribution. Institutions then use the aggregate
loss distribution to determine the appropriate amount of capital to
hold for a given soundness standard. Scenario analysis is also being
used by many institutions, albeit to significantly varying degrees.
Some institutions are using scenario analysis as the basis for their
analytical framework, while others are incorporating scenarios as a
means for considering the possible impact of significant operational
losses on their overall operational risk exposure.
The primary differences among approaches being used today relate to
the weight that institutions place on each input. For example,
institutions with comprehensive internal data may place less emphasis
on external data or scenario analysis. Another example is that some
institutions estimate a unique loss distribution for each business
line/loss type combination (bottom-up approach) while others estimate a
loss distribution on a firm-wide basis and then use an allocation
methodology to assign capital to business lines (top-down approach).
The Agencies expect internal loss event data to play an important
role in the institution's analytical framework, hence the requirement
for five years of internal operational risk loss data. However, as
footnote 5 makes clear, five years of data is not always required for
the analytical framework. For example, if a bank exited a business
line, the institution would not be expected to make use of that
business unit's loss experience unless it had relevance for other
activities of the institution. Another example would be where a bank
has made a recent acquisition where the acquired firm does not have
internal loss event data. In these cases, the Agencies expect the
institution to make use of the loss data available at the acquired
institution and any internal loss data from operations similar to that
of the acquired firm, but the institution will likely have to place
more weight relevant external loss event data, results from scenario
analysis, and factors reflecting assessments of the business
environment and internal controls.
Whatever analytical approach an institution chooses, it must
document and provide the rationale for all assumptions embedded in its
chosen analytical framework, including the choice of inputs,
distributional assumptions, and the weighting of qualitative and
quantitative elements. Management must also document and justify any
subsequent changes to these assumptions. This documentation should:
[sbull] Clearly identify how the different inputs are combined and
weighted to arrive at the overall operational risk exposure so that the
analytical framework is transparent. The documentation should
demonstrate that the analytical framework is comprehensive and
internally consistent. Comprehensiveness means that all required inputs
are incorporated and appropriately weighted. At the same time, there
should not be overlaps or double counting.
[sbull] Clearly identify the quantitative assumptions embedded in
the methodology and provide explanation for the choice of these
assumptions. Examples of quantitative assumptions include
distributional assumptions about frequency and severity, the
methodology for combining frequency and severity to arrive at the
overall loss distribution, and dependence assumptions between
operational losses across and within business lines.
[sbull] Clearly identify the qualitative assumptions embedded in
the methodology and provide explanations for the choice of these
assumptions. Examples of qualitative assumptions include the use of
business environment and control factors as well as scenario analysis
in the approach.
[sbull] Where feasible, provide results based purely on
quantitative methods separately from results that incorporate
qualitative factors. This will provide a transparent means of
determining the relative importance of quantitative versus qualitative
inputs.
[sbull] Where feasible, provide results based on alternative
quantitative and qualitative assumptions to gauge the overall model's
sensitivity to these assumptions.
[sbull] Provide a comparison of the operational risk exposure
estimate generated by the analytical framework with actual loss
experience over time, to assess the reasonable of the framework's
outputs.
[sbull] Clearly identify all changes to assumptions, and provide
explanations for such changes.
[sbull] Clearly identify the results of an independent verification
of the analytical framework.
The regulatory capital charge for operational risk will include
both expected losses (EL) and unexpected losses (UL). The Agencies have
considered two approaches that might allow for some recognition of EL;
these approaches are reserving and budgeting. However, both approaches
raise questions about their ability to act as an EL offset for
regulatory capital purposes. The current U.S. GAAP treatment for
reserves (or liabilities) is based on an incurred-loss (liability)
model. Given that EL is looking beyond current losses to losses that
will be incurred in the future, establishing a reserve for operational
risk EL is not likely to meet U.S. accounting standards. While reserves
are specific allocations for incurred losses, budgeting is a process of
generally allocating future income for loss contingencies, including
losses resulting from operational risk. Institutions will be required
to demonstrate that budgeted funds are sufficiently capital-like and
remain available to cover EL over the next year. In addition, an
institution will not be permitted to recognize EL offsets on budgeted
loss contingencies that fall below the established data thresholds;
this is relevant as many institutions currently budget for low
severity, high frequency events that are more likely to fall below most
institutions' thresholds.
An institution's analytical framework complements but does not
substitute for prudent controls. Rather, with improved risk
measurement, institutions are finding that they can make better-
informed strategic decisions regarding enhancements to controls and
processes, the desired scale and scope of the operations, and how
insurance and
[[Page 45986]]
other risk mitigation tools can be used to offset operational risk
exposure.
B. Accounting for Dependence
Supervisory Standards
S 29. Management must document how its chosen analytical framework
accounts for dependence (e.g., correlations) among operational losses
across and within business lines. The institution must demonstrate that
its explicit and embedded dependence assumptions are appropriate, and
where dependence assumptions are uncertain, the institution must use
conservative estimates.
Management must document how its chosen analytical framework
accounts for dependence (e.g., correlation) between operational losses
across and within business lines. The issue of dependence is closely
related to the choice between a bottom-up or a top-down modeling
approach. Under a bottom-up approach, explicit assumptions regarding
cross-event dependence are required to estimate operational risk
exposure at the firm-wide level. Management must demonstrate that these
assumptions are appropriate and reflect the institution's current
environment. If the dependence assumptions are uncertain, the
institution must choose conservative estimates. In so doing, the
institution should consider the possibility that cross-event dependence
may not be constant, and may increase during stress environments.
Under a top-down approach, an explicit assumption regarding
dependence is not required. However, a parametric distribution for loss
severity may be more difficult to specify under the top-down approach,
as it is a statistical mixture of (potentially) heterogeneous business
line and event type distributions. Institutions must carefully consider
the conditions necessary for the validity of top-down approaches, and
whether these conditions are met in their particular circumstances.
Similar to bottom-up approaches, institutions using top-down approaches
must ensure that implicit dependence assumptions are appropriate and
reflect the institution's current environment. If historic dependence
assumptions embedded in top-down approaches are uncertain, the
institution must be conservative and implement a qualitative adjustment
to the analysis.
IX. Risk Mitigation
Supervisory Standards
S 30. Institutions may reduce their operational risk exposure
results by no more than 20% to reflect the impact of risk mitigants.
Institutions must demonstrate that mitigation products are sufficiently
capital-like to warrant inclusion in the adjustment to the operational
risk exposure.
There are many mechanisms to manage operational risk, including
risk transfer through risk mitigation products. Because risk mitigation
can be an important element in limiting or reducing operational risk
exposure in an institution, an adjustment is being permitted that will
directly impact the amount of regulatory capital that is held for
operational risk. The adjustment is limited to 20% of the overall
operational risk exposure result determined by the institution using
its loss data, qualitative factors, and quantitative framework.
Currently, the primary risk mitigant used for operational risk is
insurance. There has been discussion that some securities products may
be developed to provide risk mitigation benefits; however, to date, no
specific products have emerged that have characteristics sufficient to
be considered capital-replacement for operational risk. As a result,
securities products and other capital market instruments may not be
factored in to the regulatory capital risk mitigation adjustment at
this time.
For an institution that wishes to adjust its regulatory capital
requirement as a result of the risk mitigating impact of insurance,
management must demonstrate that the insurance policy is sufficiently
capital-like to provide the cushion that is necessary. A product that
would fall in this category must have the following characteristics:
[sbull] The policy is provided through a third party \13\ that has
a minimum claims paying ability rating of A; \14\
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\13\ Where operational risk is transferred to a captive or an
affiliated insurer such that risk is retained within the group
structure, recognition of such risk transfer will only be allowed
for regulatory capital purposes where the risk has been transferred
to a third party (e.g., an unaffiliated reinsurer) that meets the
standards set forth in this section.
\14\ Rating agencies may use slightly different rating
scales.For the purpose of this supervisory guidance, the insurer
must have a rating that is at least the equivalent of A under
Standard and Poor's Insurer Financial Strength Ratings or an A2
under Moody's Insurance Financial Strength Ratings.
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[sbull] The policy has an initial term of one year; \15\
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\15\ Institutions must decrease the amount of the adjustment if
the remaining term is less than one year. The institution must have
a clear policy in place that links the remaining term to the
adjustment factor.
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[sbull] The policy has no exclusions or limitations based upon
regulatory action or for the receiver or liquidator of a failed bank;
[sbull] The policy has clear cancellation and non-renewal notice
periods; and
[sbull] The policy coverage has been explicitly mapped to actual
operational risk exposure of the institution.
Insurance policies that meet these standards may be incorporated
into an institution's adjustment for risk mitigation. An institution
should be conservative in its recognition of such policies, for
example, the institution must also demonstrate that insurance policies
used as the basis for the adjustment have a history of timely payouts.
If claims have not been paid on a timely basis, the institution must
exclude that policy from the operational risk capital adjustment. In
addition, the institution must be able to show that the policy would
actually be used in the event of a loss situation; that is, the
deductible may not be set so high that no loss would ever conceivably
exceed the deductible threshold.
The Agencies will not specify how institutions should calculate the
risk mitigation adjustment. Nevertheless, institutions are expected to
use conservative assumptions when calculating adjustments. An
institution should discount (i.e., apply its own estimates of haircuts)
the impact of insurance coverage to take into account factors, which
may limit the likelihood or size of claims payouts. Among these factors
are the remaining terms of a policy, especially when it is less than a
year, the willingness and ability of the insurer to pay on a claim in a
timely manner, the legal risk that a claim may be disputed, and the
possibility that a policy can be cancelled before the contractual
expiration.
X. Data Maintenance
Supervisory Standards
S 31. Institutions using the AMA approach for regulatory capital
purposes must use advanced data management practices to produce
credible and reliable operational risk estimates.
Data maintenance is a critical factor in an institution's
operational risk framework. Institutions with advanced data management
practices should be able to track operational risk loss events from
initial discovery through final resolution. These institutions should
also be able to make appropriate adjustments to the data and use the
data to identify trends, track problem areas, and identify areas of
future risk. Such data should include not only operational risk loss
event information, but also information on risk assessments, which are
factored into the operational risk exposure calculation. In general,
institutions using the AMA
[[Page 45987]]
should have the same data maintenance standards for operational risk as
those set forth for A-IRB institutions under the credit risk guidance.
Operational risk data elements captured by the institution must be
of sufficient depth, scope, and reliability to:
[sbull] Track and identify operational risk loss events across all
business lines, including when a loss event impacts multiple business
lines.
[sbull] Calculate capital ratios based on operational risk exposure
results. The institution must also be able to factor in adjustments
related to risk mitigation, correlations, and risk assessments.
[sbull] Produce internal and public reports on operational risk
measurement and management results, including trends revealed by loss
data and/or risk assessments. The institution must also have sufficient
data to produce exception reports for management.
[sbull] Support risk management activities.
The data warehouse \16\ 16 must contain the key data elements
needed for operational risk measurement, management, and verification.
The precise data elements may vary by institution and also among
business lines within an institution. An important element of ensuring
consistent reporting of the data elements is to develop comprehensive
definitions for each data element used by the institution for reporting
operational risk loss events or for the risk assessment inputs. The
data must be stored in an electronic format to allow for timely
retrieval for analysis, verification and testing of the operational
risk framework, and required disclosures.
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\16\ In this document, the terms ``database'' and ``data
warehouse'' are used interchangeably to refer to a collection of
data arranged for easy retrieval using computer technology.
---------------------------------------------------------------------------
Management will need to identify those responsible for maintaining
the data warehouse. In particular, policies and processes will need to
be developed for delivering, storing, retaining, and updating the data
warehouse. Policies and procedures must also cover the edit checks for
data input functions, as well as the requirements for the testing and
verification function to verify data integrity. Like other areas of the
operational risk framework, it is critical that management ensure
accountability for ongoing data maintenance, as this will impact
operational risk management and measurement efforts.
XI. Testing and Verification
Supervisory Standards
S 32. The institution must test and verify the accuracy and
appropriateness of the operational risk framework and results.
S 33. Testing and verification must be done independently of the
firm-wide operational risk management function and the institution's
lines of business.
The operational risk framework must provide for regular and
independent testing and verification of operational risk management
policies, processes and measurement systems, as well as operational
risk data capture systems. For most institutions, operational risk
verification and testing will primarily be done by the audit function.
Internal and external audits can provide an independent assessment of
the quality and effectiveness of the control systems' design and
performance. However, institutions may use other independent internal
units (e.g. quality assurance) or third parties. The testing and
verification function, whether internally or externally performed,
should be staffed by qualified individuals who are independent from the
firm-wide operational risk management function and the institution's
lines of business.
The verification of the operational risk measurement system should
include the testing of:
[sbull] Key operational risk processes and systems;
[sbull] Data feeds and processes associated with the operational
risk measurement system;
[sbull] Adjustments to empirical operational risk capital
estimates, including operational risk exposure;
[sbull] Periodic certification of operational risk models used and
their underlying assumptions; and
[sbull] Assumptions underlying operational risk exposure, data
decision models, and operational risk capital charge.
The operational risk reporting processes should be periodically
reviewed for scope and effectiveness. The institution should have
independent verification processes to ensure the timeliness, accuracy,
and comprehensiveness of operational risk reporting systems, both at
the firm-wide and the line of business levels.
Independent verification and testing should be done to ensure the
integrity and applicability of the operational risk framework,
operational risk exposure/loss data, and the underlying assumptions
driving the regulatory capital measurement process. Appropriate
reports, summarizing operational risk verification and testing findings
for both the independent firm-wide risk management function and lines
of business should be provided to appropriate management and the board
of directors or a designated board committee.
Appendix A: Supervisory Standards for the AMA
S 1. The institution's operational risk framework must include
an independent firm-wide operational risk management function, line
of business management oversight, and independent testing and
verification functions.
S 2. The board of directors must oversee the development of the
firm-wide operational risk framework, as well as major changes to
the framework. Management roles and accountability must be clearly
established.
S 3. The board of directors and management must ensure that
appropriate resources are allocated to support the operational risk
framework.
S 4. The institution must have an independent operational risk
management function that is responsible for overseeing the
operational risk framework at the firm level to ensure the
development and consistent application of operational risk policies,
processes, and procedures throughout the institution.
S 5. The firm-wide operational risk management function must
ensure appropriate reporting of operational risk exposures and loss
data to the board of directors and senior management.
S 6. Line of business management is responsible for the day-to-
day management of operational risk within each business unit.
S 7. Line of business management must ensure that internal
controls and practices within their line of business are consistent
with firm-wide policies and procedures to support the management and
measurement of the institution's operational risk.
S 8. The institution must have policies and procedures that
clearly describe the major elements of the operational risk
management framework, including identifying, measuring, monitoring,
and controlling operational risk.
S 9. Operational risk management reports must address both firm-
wide and line of business results. These reports must summarize
operational risk exposure, loss experience, relevant business
environment and internal control assessments, and must be produced
no less often than quarterly.
S 10. Operational risk reports must also be provided
periodically to senior management and the board of directors,
summarizing relevant firm-wide operational risk information.
S 11. An institution's internal control structure must meet or
exceed minimum regulatory standards established by the Agencies.
S 12. The institution must demonstrate that it has appropriate
internal loss event data, relevant external loss event data,
assessments of business environment and internal controls factors,
and results from scenario analysis to support its operational risk
management and measurement framework.
S 13. The institution must include the regulatory definition of
operational risk as the baseline for capturing the elements of the
[[Page 45988]]
AMA framework and determining its operational risk exposure.
S 14. The institution must have clear standards for the
collection and modification of the elements of the operational risk
AMA framework.
S 15. The institution must have at least five years of internal
operational risk loss data \17\ captured across all material
business lines, events, product types, and geographic locations.
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\17\ With supervisory approval, a shorter initial historical
observation period is acceptable for banks newly authorized to use
an AMA methodology.
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S 16. The institution must be able to map internal operational
risk losses to the seven loss-event type categories.
S 17. The institution must have a policy that identifies when an
operational risk loss becomes a loss event and must be added to the
loss event database. The policy must provide for consistent
treatment across the institution.
S 18. The institution must establish appropriate operational
risk data thresholds.
S 19. Losses that have any characteristics of credit risk,
including fraud-related credit losses, must be treated as credit
risk for regulatory capital purposes. The institution must have a
clear policy that allows for the consistent treatment of loss event
classifications (e.g., credit, market, or operational risk) across
the organization.
S 20. The institution must have policies and procedures that
provide for the use of external loss data in the operational risk
framework.
S 21. Management must systematically review external data to
ensure an understanding of industry experience.
S 22. The institution must have a system to identify and assess
business environment and internal control factors.
S 23. Management must periodically compare the results of their
business environment and internal control factor assessments against
actual operational risk loss experience.
S 24. Management must have policies and procedures that identify
how scenario analysis will be incorporated into the operational risk
framework.
S 25. The institution must have a comprehensive operational risk
analytical framework that provides an estimate of the institution's
operational risk exposure, which is the aggregate operational loss
that it faces over a one-year period at a soundness standard
consistent with a 99.9 per cent confidence level.
S 26. Management must document the rationale for all assumptions
underpinning its chosen analytical framework, including the choice
of inputs, distributional assumptions, and the weighting across
qualitative and quantitative elements. Management must also document
and justify any subsequent changes to these assumptions.
S 27. The institution's operational risk analytical framework
must use a combination of internal operational loss event data,
relevant external operational loss event data, business environment
and internal control factor assessments, and scenario analysis. The
institution must combine these elements in a manner that most
effectively enables it to quantify its operational risk exposure.
The institution can choose the analytical framework that is most
appropriate to its business model.
S 28. The institution's capital requirement for operational risk
will be the sum of expected and unexpected losses unless the
institution can demonstrate, consistent with supervisory standards,
the expected loss offset.
S 29. Management must document how its chosen analytical
framework accounts for dependence (e.g., correlations) among
operational losses across and within business lines. The institution
must demonstrate that its explicit and embedded dependence
assumptions are appropriate, and where dependence assumptions are
uncertain, the institution must use conservative estimates.
S 30. Institutions may reduce their operational risk exposure
results by no more than 20% to reflect the impact of risk mitigants.
Institutions must demonstrate that mitigation products are
sufficiently capital-like to warrant inclusion in the adjustment to
the operational risk exposure.
S 31. Institutions using the AMA approach for regulatory capital
purposes must use advanced data management practices to produce
credible and reliable operational risk estimates.
S 32. The institution must test and verify the accuracy and
appropriateness of the operational risk framework and results.
S 33. Testing and verification must be done independently of the
firm-wide operational risk management function and the institution's
lines of business.
Dated: July 17, 2003.
John D. Hawke, Jr.,
Comptroller of the Currency.
By order of the Board of Governors of the Federal Reserve
System, July 21, 2003.
Jennifer J. Johnson,
Secretary of the Board.
Dated at Washington, DC, this 11th day of July, 2003.
By order of the Board of Directors.
Federal Deposit Insurance Corporation.
Robert E. Feldman,
Executive Secretary.
Dated: July 18, 2003.
By the Office of Thrift Supervision.
James E. Gilleran,
Director.
[FR Doc. 03-18976 Filed 8-1-03; 8:45 am]
BILLING CODE 4810-33-P
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