Strengthening Financial Risk Management at the FDIC
Improving Financial Reporting Ė Horizon 1
The FDICís Financial Risk Committee is responsible for determining the FDICís
reserves for failing depository institutions. The primary financial reporting outputs
of the FRC are the Contingent Loss Reserve and the 2-year Projection of failed bank
assets. The CLR is the FRCís estimate of the FDICís probable losses
attributable to failures of FDIC-insured institutions in the coming 12 months while
the 2-year Projection is an estimate of the assets of all FDIC-insured institutions
whose failure is reasonably possible in the coming 24 months. The FRC reports
these estimates at the end of each quarter, and they are incorporated into the FDICís
annual report at the end of each calendar year. The CLR appears as a distinct entry
in the financial statements; the 2-year Projection is used in a footnote to those
statements and in FDIC deliberations regarding the level of semi-annual premiums
necessary to maintain the funds at or above the reserve ratio.
There are three primary ways in which the FRCís estimates and processes can be
improved. First, the CLR methodology should be refined to: 1) place bounds on
the subjectivity exercised by the FRC in establishing failure rate estimates, and
2) incorporate balance sheet composition into loss rate estimates. These
refinements will enhance the accuracy, robustness, and transparency of the CLR
estimate. Second, the 2-year Projection should be replaced with: 1) a confidence
interval around the CLR; and 2) a 2-year loss figure based on the CLR
methodology. These substitute methodologies will provide a better assessment of
ďreasonably possibleĒ losses, the primary role of the 2-year Projection today.
Lastly, a new set of FRC organizational practices should be adopted to ensure that
the participants have the best available information to answer the most-important
questions that the FRC faces in setting reserves for its financial reports.
FRC should pursue these enhancements aggressively over the next 90 days.
Effectively implemented, these improvements will measurably increase the
accuracy of the CLR, provide additional, meaningful measures of risk for the FDIC,
and focus resources on the most important issues for the FRC. More broadly,
implementing these changes will build organizational momentum for continuous
migration to more sophisticated risk management. Each of the recommendations is
described in more detail below.
RECOMMENDATION 1.1: REFINE THE CONTINGENT LOSS
The CLR formula produces an output that is subject to review and alteration by the
FRC, at its discretion. The accuracy, robustness, and transparency of the CLR stem
from both the formula and any subjective deviation from it. To improve the
performance of the CLR along these dimensions, the FRC should adopt changes to
the CLR formula and bounds to the FRCís subjectivity. This is explained in greater
detail below, beginning with a description of how the CLR is calculated.
How the CLR is calculated
The CLR is the FDICís reserve for ďprobable and estimableĒ losses from the failure
of FDIC-insured institutions over the following 12 months. In any given year,
typically only a few of the roughly 8,500 insured institutions fail, and in most cases
the FDICís losses are only a fraction of the insured deposits held by the failed
institution. As such, the CLR hinges on FRCís estimates of the probability that
institutions will fail and the losses the FDIC will incur if they do. In particular, the
CLR is based on a three-step process outlined in Exhibit 1-1.
THE THREE-STEP CLR METHODOLOGY
Calculate expected failed assets
For institutions with
CAMELS ratings 4-5,
institution's assets by
an estimate of its
probability of failure
Calculate expected losses
failed assets by an
estimated loss rate on
Use judgment to adjust expected losses
For each institution, if
the assumed failure
probability or loss rate
alter the reserved
Source: FDIC; McKinsey analysis
The first step in the CLR calculation is to calculate expected failed assets. This
calculation begins with a list of institutions that are at heightened risk of failure,
Confidential which the FRC measures using the institutionsí composite CAMELS ratings.4 For
institutions with CAMELS ratings 4-5 (the lowest ratings), the FRC calculates
2-year historical failure rates for institutions with similar ratings and capital
adequacy (described below). For institutions whose failure is imminent,5 called theď100-percent failureĒ group, the formula uses a 100-percent failure probability. The
FRC multiplies these failure probabilities by the institutionsí assets to yield
expected failed assets.
The second step in the CLR calculation is to translate expected failed assets into the
FDICís expected losses. The FRC multiplies the expected failed assets of each
institution by an estimated loss rate on those assets, where loss rates are derived
from historical experience, 1987 through the present, for institutions of similar
The final step in the CLR calculations is to alter the result of the formula according
to the judgment of the FRC. The FRC may adjust the failure probabilities used in
Step 1, the loss rates used in Step 2, or the individual reserve for any given
institution. In determining whether to deviate from the CLR formula, the FRC
generally considers the current economic climate, input from the DSC on likely
failure probabilities, an alternative loss-rate model, and loss estimates prepared by
the DRR based on asset valuation reviews or cost-test analyses.
Performance of the CLR
The ideal CLR would be accurate, robust in reflecting changes in economic and
financial conditions, and based on a methodology that was transparent to all
stakeholders. Accuracy is an objective criterion that can be measured
arithmetically; e.g., by calculating the mean squared error between the CLR and the
FDICís actual losses over time.7 Robustness refers to the ability of the methodology to respond to changing future conditions, rather than merely explaining what has happened before. Transparency is a function of both the method itself and how that method is applied. Simple methods applied without
exception will generally be more transparent than complex methods with wide
latitude for exceptions. The CLR can be improved on each dimension: accuracy,
robustness, and transparency.
1. Each of the three steps in the CLR methodology (Exhibit 1-1) introduces
errors that decrease the accuracy of the CLR.
In Step 1, the FDIC does not reserve for ďunanticipatedĒ failures, i.e., for failures of
institutions with CAMELS ratings of 1-3, so any failure by such an institution leads
to inaccuracy in the CLR. In 1990-2000, 18 percent of the FDICís losses from
failed institutions were unanticipated, and the average annual loss from such
failures was $238 million. Reserving nothing for these failures tends to make the
CLR underestimate actual losses in most years, although other types of errors
described below may have offsetting effects.8
There are also errors in estimated failure probabilities for those institutions that are
included in the calculations. The FRC forms estimates of failure probabilities for
each of four risk groups based on CAMELS ratings and capital adequacy. The
institutions are first divided into those with CAMELS ratings 4 or 5. These two
groups are further divided into those projected to have capital above or below
2 percent of assets in 12 monthsí time.9
The FRCís estimated failure probability for each of these four groups is the average
annual failure rate for institutions in that group over the prior two years, weighted
by each institutionís assets. This methodology is subject to two types of
inaccuracy: differences in failure probabilities between institutions within each risk
group and changes in failure probabilities over time.
Institutions within each risk group may have different failure probabilities. For
example, in the 1990-2000 interval, the annual failure rate for CAMELS 4
institutions with less than 2 percent capital (the ď4-minusĒ group) was 0.051
percent, but within that group, the failure rate for the largest half of institutions, by assets, was 0.090 percent, while that for the smallest half was 0.046 percent Ė a
statistically significant difference. If these historical failure rates were accurate,
rather than being random and anomalous, then the CLR method introduced
inaccuracies by assuming the average failure rate for both halves of the 4-minus
group. Specifically, 0.051 percent was too small a failure probability for the largest
institutions and too large a failure probability for the smallest institutions. While
such within-group differences no doubt exist and contribute to errors in the current
CLR methodology, the magnitudes of such errors are difficult to quantify.
The second potential source of inaccuracy in the FRCís historical failure-probability
estimates stems from changes in failure rates over time. Such changes can result
from pure randomness (e.g., failure from fraud or poor corporate governance) or
from changes in the characteristics of institutions within each risk group (e.g.,
capital degradation of every institution in the 4-minus group due to a change in the
economic climate). These two effects combine to cause failure rates to differ
substantially from year to year. For example, the failure rate of the 4-minus group
was 0.052 percent in 1990-1994, but it fell to 0.045 percent in 1995-2000.
Accordingly, using historical failure rates in 1995 would likely have led to
overestimates of failures.
In general, any historical moving average, or ďlook-back period,Ē for failure rates
will underestimate failure probabilities during a period of unusually high failure
rates, since the look-back will estimate failure probabilities based on earlier
experience, and similarly overestimate failure probabilities after the temporarily
high failure rates have subsided, since the look-back will incorporate high failure
rates that will have since passed.
There are additional costs to accuracy in Step 2 of the CLR calculation, when
converting estimated failed assets into estimated losses. As described above, FRC
uses losses from 1987 onwards to form estimates of loss rates for institutions based
on their size, by assets, for five different size ranges. As with failure rates, there are
two primary sources of error in these historical estimates of loss rates: differences
in loss rates within any given size group and changes in loss rates over time.
Obviously, not all institutions of a given size will cause the same loss if they fail.
For example, the asset composition of an institution will influence the FDICís loss
rates, with almost no losses realized on liquid assets like securities and substantial
losses on less-liquid assets like commercial loans. This means that institutions of
the same size often have predictably different loss rates, whereas the current CLR
methodology assumes that those loss rates are the same.
The other source of error in loss rates stems from changes in loss rates over time.
Loss rates depend on a number of factors that are likely to change from one year to the next, such as prevailing market conditions, the prevailing cause of failures, and
the credit quality of the assets being liquated. For example, the average loss rate for
institutions with assets $100 million to $500 million was 17 percent for 1990-1994,
but it was 27 percent for 1995-2000. Since the CLR uses average loss rates from
1987 to the present (23 percent in this size band), the CLRís loss estimates in the
late 1990s incorporated the lower loss rates from the early 1990s, leading to
reserves that were erroneously low for institutions in this size group.
The desire to have accurate and up-to-date loss rates must be traded off against the
statistical challenges of estimating loss rates based on very few failures. In the
example above, if the loss rate had been estimated with more-recent failures rather
than those in the early 1990s, then the loss-rate estimate would have been based on
just a handful of failures, so it would have been vulnerable to unusual failures that
did not reflect prevailing conditions.
The final source of inaccuracy in the CLR comes in Step 3, when the FRC exercises
its judgment in determining whether to deviate from the historical failure- and loss rate
estimates. In practice, this has led to wide departures from the historical
estimates for failure rates (Exhibit 1-2). Loss rates are rarely overridden by the
COMPARISON OF FAILURE RATES
D * Average failure rate over the 5 .buckets. (e.g., CAMELS 4 with more than 2% capital), weighted by the number
of institutions in each bucket in the given quarter
Source: FDIC; McKinsey analysis
In all but two quarters from 1997 to 2001, the 2-year historical failure rate was
closer to the actual failure rate than was the failure probability used by the FRC. As
such, the subjectivity exercised by the FRC in setting failure probabilities has in
general decreased the accuracy of the CLR.
2. Each of the three steps in the CLR methodology (Exhibit 1-1) affects the
robustness of the CLR.
In Step 1 of the CLR methodology, the groupings for failure rates are based on
CAMELS ratings and capital forecasts. Both of these criteria are fairly robust to
changes in conditions. CAMELS ratings represent the best and most current
knowledge of supervisors and are flexible enough to reflect new and emerging
concerns. For example, if a new and risky lending practice emerged in the banking
industry, then supervisors can incorporate the impact of that practice in the
CAMELS ratings, especially as it affects asset quality and capital.
Likewise, capital is likely to remain a fundamental component of an institutionís
soundness, irrespective of innovations in the industry, so it is a robust measure of probability of failure, even on a lagged accounting basis. In this area, the only lack
of robustness may come from how capital is forecast, since the dynamics of balance
sheets can change with industry practices, and the FDICís Pro Forma Model may
miss such changes.
In Step 2, the FRCís current loss-rate methodology is not robust, because the typical
asset profile of the banking industry varies over time. For example, the 1990s saw
rapid increases in the prevalence of syndicated loans, subprime lending, and credit
derivatives. Since different assets have different loss rates, a robust CLR would
adapt to introductions of new asset classes. The current CLR methodology does
not, since it assumes that loss rates vary based only on the size of an institution, not
on its asset composition.
Finally, in Step 3, the FRC may use its judgment to override the historical failure
and loss rates used in the CLR calculations. Failure rates are overridden routinely,
loss rates only infrequently. In theory this flexibility enhances the robustness of the
CLR, since the FRC is composed of representatives from four divisions of the
FDIC, with extensive experience and access to the most accurate and current
information about possible losses from failures. As such, the FRC participants as a
group are in a better position than anyone else to know whether the historical failure
and loss rates may be inappropriate for the coming year. However, as described
above, the robustness derived from this discretion comes at the expense of accuracy,
since subjective deviations from historical failure and loss rates have usually
enlarged CLR errors.
3. The formulaic part of the CLR methodology (Steps 1 and 2) is transparent,
but subjective deviations from the formula (Step 3) are not.
The most transparent methodology would be simple and applied uniformly. The
FDICís current methodology is quite simple: it reserves for failing institutions and
those with CAMELS ratings 4 and 5, based on the failure rates for the past 2 years
and average loss rates for similar-sized institutions since 1987, unless the FRC
judges that such historical estimates would be inappropriate going forward.
On the other hand, the discretion that FRC exercises to override historical failure
and loss rates detracts from the transparency of the CLR. The FRC can deviate
from the historical estimates for a variety of reasons.10 This latitude leaves room for outsiders to speculate about whether the CLR has been modified or adjusted for
other than purely reporting purposes.
Specifics of Recommendation 1.1 (Refining the CLR
The FRC can improve the accuracy, robustness, and transparency of its CLR
methodology by adopting the following eight recommendations. The first three
relate to bounding the FRCís subjectivity in estimating failure probabilities, while
the remaining four explain how the FRC should use more information from
institutionsí balance sheets in estimating loss rates. The FRC and DIR should
implement all of the recommendations during the next 3 months.
1.1.a. The FRC should develop explicit guidelines about when it will deviate from
historical failure rates. The current CLR methodology uses a 2-year look-back for
estimating failure probabilities. This period was chosen to minimize the mean squared
error of the CLR relative to other possible look-back windows, enhancing
the overall accuracy of the CLR. Nevertheless, the FRC has often used its broad
discretion to depart from the 2-year failure rates and substitute its own estimates of
expected failure rates not strictly grounded in historical experience. Although well intended,
these deviations typically have increased the error in the CLR.
Developing explicit guidelines governing when to deviate from the 2-year lookback
will help the FRC adhere more often to historical failure rates and guide DIRís
ongoing research into anticipating future failure rates. Preliminary analysis
suggests that historical failure rates may be too low when high-yield bond default
rates have recently increased, vacancy rates in commercial real estate are
increasing, or subprime lending makes up a larger-than-average fraction of assets
for institutions on the reserve list. Making this research and FRCís deviation
guidelines public would enhance the transparency of the CLR while maintaining its
1.1.b. When estimating failure probabilities, the FRC should constrain itself to a
90-percent confidence interval of the 2-year historical average.11 This
recommendation would allow the FRC to apply judgment while limiting its scope,
and thereby likely increase both the accuracy and robustness of the CLR. As
Exhibit 1-3 shows, restricting the FRCís discretion to the 90 percent confidence
interval would have improved the accuracy of the FRCís failure-probability estimates in all but one quarter since 1997. The net impact is depicted in Exhibit 1-4, which shows the improved accuracy (approximately 23 percent over the 5-year period shown) that bounding subjectivity confers to the CLR.12
BOUNDING THE FRCíS SUBJECTIVITY
D * Average failure rate over the 5 .buckets. (e.g., CAMELS 4 with more than 2% capital), weighted by the number
of institutions in each bucket in the given quarter
Note: Actual failure rate and 2-year moving average failure rate are not weighted for institution size.
Source: FDIC; McKinsey analysis
NET IMPROVEMENT TO THE CLR
D * Lighter shading starting in 01Q4 indicates probable improvements
** Before any subjective override the FRC may make to account for new information about specific institutions
Source: McKinsey analysis
1.1.c. The FRC should continue not to reserve for unanticipated failures.
Although institutions with composite CAMELS ratings 1-3 represent a substantial
fraction of failures, losses from these failures do not appear to be ďprobable and
reasonably estimableĒ in any given year, since the annual loss is usually either zero
or a large amount. Reserving based on an historical moving average of past
unanticipated losses, regardless of the length of the look-back, would decrease the
accuracy of the CLR13 because it would result in under-reserving at the time of an unanticipated loss and over-reserving after the loss has occurred (Exhibit 1-5).
However, this topic warrants further study, especially if the FDIC begins to
experience more than a couple of failures of institutions rated 1-3 in each year.
THE CHALLENGE OF RESERVING FOR UNANTICIPATED FAILURES Actual versus estimated* loss in coming year, for institutions rated CAMELS 1-3, $M
D * One-year moving average of actual quarterly losses
Source: FDIC RIS data; McKinsey analysis
1.1.d. The FRC should use the FDICís Research Model for loss-rate estimates.
FRCís current CLR methodology takes no explicit account of liability structure in
estimating loss rates. Any loss in asset values is considered a loss to the FDIC. In
reality, however, the shareholders and creditors of the failed institution share in
these losses, in order of their seniority. Because the Research Model specifically
accounts for institutional liability structures, it is a better model of the FDICís
expected losses given failure.
1.1.e. DIR should update the Research Model with more recent data. In current
form, the Research Model does not offer a material improvement in accuracy over
the existing approach to estimating loss rates, but its accuracy can be improved.
The research model was last updated based on data for 1990-97. More recent data
almost certainly better reflect current conditions, so additional years of data should
be included. Furthermore, DIR should investigate whether loss rates in the early
1990s are still applicable, and if not, DIR should drop those earlier observations
from its loss-rate estimates.
1.1.f. DIR should expand the Research Model to incorporate institution size.
The current CLR methodology recognizes a strong inverse relationship between
institution size and loss rate given failure, assigning a loss rate to the smallest
institutions that is three times the rate of very large institutions (24 percent versus
8 percent). Asset size is likely to retain predictive power even when controlling for
asset composition, because larger institutions tend to have better internal controls,
and because the FDIC often enjoys economies of scale in disposing of larger institutions. It follows that the overall predictive power of the asset-composition based
Research Model is likely to be enhanced with the addition of a variable
representing institution size, and the FRC should explore this possibility. For
reasons of sample size, it may be advisable to restrict the analysis to two size
categories, large and small.
1.1.g. DIR should expand the Research Model to account for dispositions other
than liquidation. The FDIC often is able to dispose of an institution, or part of it,
via a Purchase and Assumption (P&A) transaction that captures some of the
franchise value and avoids some liquidation costs. By ignoring these kinds of
transactions, the current Research Model is biased toward overstating actual losses
from failures. Although issues of availability and scope of historical data present
analytical obstacles, DIR should thoroughly study the feasibility of expanding the
Research Model to account for dispositions other than liquidation.
4 A CAMELS rating is a supervisory estimate of the safety and soundness of a depository institution. Each letter of CAMELS corresponds to a dimension considered by the supervisor. C is for capital adequacy; A is for asset quality; M is for management capabilities; E is for earnings quality and quantity; L is for liquidity; and S is for the sensitivity
to market risk. Each dimension is scored one to five, with one being the best rating. The supervisor determines the overall, or composite, rating based on the six component ratings, and composite ratings also range from one to five; 1-rated institutions are deemed to be the safest, and they have in fact historically been much less likely to fail.
5 This determination is based on either the Division of Resolution and Receivershipsí (DRR) scheduled closing date for the institution, the classification of the institution as ďcritically undercapitalized,Ē or the Division of Supervision and Consumer Protection (DSC) identification of the institution as an imminent failure.
6 For each of five size groups, the FRC estimates the loss rate as the dollar amount of the FDICís losses from failures in that size group divided by the sum of all the assets of institutions in that size group. This amounts to an average loss rate, weighted by each institutionís size by assets.
7 Mean squared error (MSE) is the predominant measure of goodness of fit in statistics. When the MSE of a set of estimates is low, then the estimates are accurate, and an MSE of zero corresponds to a perfect fit. To calculate MSE, square the difference (error) between each estimate and its corresponding historical value, and then average these squares. By squaring the errors in this way, all errors are stated as positive numbers (the square of a negative is a positive), and large errors are penalized disproportionately (42=16 is more than twice as large as 22=4).
8 As discussed later in this report, no analytically sound solution exists in Horizon 1 for correcting the inaccuracy introduced by not reserving for CAMELS 1, 2, and 3 institutions.
9 DIR forecasts an institutionís capital using the FDICís Pro Forma Model (Pro Forma), which simulates the future financial condition of the institution based on its current financial condition. For example, if the institutionís current income is not sufficient to cover its expenses, then it must be depleting capital to make up the difference, so Pro Forma forecasts that its capital will decline.
10 The FRC must document and explain any failure rate deviations in a written memorandum to the FDICís CFO and the Division of Financeís Deputy Director responsible for the FDICís financial statements.
11 Within each risk bucket (e.g., 4-minus), there were a given number of failures (X) and a given number of institutions (Y) over the prior 2 years. The historical failure rate (F) is the ratio of the two (X/Y). This ratio is a binomial random variable with well-understood statistical properties, making it possible to construct a confidence interval for the true 2-year failure rate. Specifically, Y is almost always greater than 30, so the distribution of the true 2-year
failure rate can be closely approximated by a normal random variable with mean F and variance S=[(F)(1-F)/Y]Ĺ . The 90-percent confidence interval for the true failure rate is then F Ī (1.96)(S).
12 We recommend using unweighted averages in calculating historical failure rates, and we have done so in our analysis. The current FRC methodology determines historical failure rates using an asset-weighted average.
13 For example, the mean squared error would be higher.