Chapter 13

Off-Site Sur Off-Site Surveillance veillance 

Systems Systems

Introduction

Bank regulators have used computerized off-site surveillance systems since 1975, yet

during the banking crises of the 1980s and 1990s, bank supervisors seemed surprised as

each new bank crisis erupted. This chapter examines why, even with computerized off-site

systems, it is difficult to anticipate which banks will fail many years in advance of the failure and what tools bank regulators can use to identify banks in the various stages of financial distress. 

A brief history of off-site monitoring and a discussion of the advantages and disadvantages of off-site systems are followed by a section in which the goals of a forecasting

system are discussed, two sample approaches to achieving those goals are explored, and the

conclusion is drawn that the best way to predict long-term failure rates is to measure risk

characteristics. The next section focuses on the obstacles to predicting failures in the real

world (the life cycle of failing banks, the role of the economic environment, and the nonlinear nature of banks financial process), and the following section develops and tests an

analysis of risk groups. Then the systems currently in use at the three bank supervisory

agencies are described, with special attention to the FDICs systems for monitoring growth

and tracking changes in bank financial condition that may warrant added supervisory attention. (Also included is a discussion of several proposed improvements in the FDICs

Growth Monitoring System.) A brief concluding section sums up the lessons learned, given

the history of banking in the 1980s and early 1990s and the strengths and limitations of current computerized off-site surveillance systems.

History of Off-Site Surveillance Systems

The advent of computerized off-site monitoring of banks in 1975 significantly affected bank examination and enforcement in the 1980s and 1990s. Computerized systems

allowed regulators to analyze rapidly and systematically the enormous amounts of data that

banks report on their Call Reports. Back in the 1960s, when computers and computer time
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1 For example, the System/360 Model 30 IBM mainframe computer, introduced in 1964, had a price-per-instruction-persecond cost of $25.02 in 1992 dollars. In 1971 IBM released the System/370 Model 135, after which the price per instruction dropped to $8.91 (1992 dollars), a 65 percent decrease. Throughout the 1970s the rate of price decreases accelerated.

In 1979 IBM released the 4341 mainframe, with which the price per instruction fell to $0.64 (1992 dollars), less than 10 percent of the 1971 price (Emerson W. Pugh, Building IBM: Shaping an Industry and Its Technology [1995], 329). 2 Eugene N. White, The Comptroller and the Transformation of American Banking, 1960-1990 (1992), 27, 3839. 3 In 1976 there were 5,426 examinations in 4,737 national banks; in 1977 there were 2,886 examinations in 5,665 national

banks, a 47 percent decline (White, Comptroller, 38). For a more detailed description of these changes, see Chapter 12. 4 Before this time the Call Report itself was the principal off-site monitoring tool. Examiners would look at their particular institutions Call Report to see if there were any significant changes from the previous examination or the previous Call Report.

5 Edward I. Altman, Predicting Performance in the Savings and Loan Association Industry, Journal of Monetary Economics 3 (October 1977): 44366; and Joseph Sinkey, A Multivariate Statistical Analysis of the Characteristics of Problem

Banks, Journal of Finance 30 (March 1975): 2136. The National Bank Surveillance System eventually became the Uniform Bank Surveillance System (UBSS), and the Bank Performance Report became the Uniform Bank Performance Report

(UBPR). Currently (1997), the UBPR is the major tool used by banks and bank regulators to compare an individual banks

performance with the performance of its peers. Information on peer groups is given below, in the section entitled Modifying the Peer Groups.

6 White, Comptroller, 61; and Linda W. McCormick, Comptroller Begins Major Revamp, American Banker 147 (April 29,

1982), 15. See Chapter 12.

were very expensive, there were no off-site monitoring systems as we understand them today. But from the early 1960s onward the price of computer time kept dropping,1 and dramatic price drops in the early 1970s coincided with a crisis at the Office of the Comptroller

of the Currency (OCC). Two large national banks failed, United States National Bank

(USNB) in 1973 and Franklin National Bank in 1974.

In response to the USNB failure, the OCC commissioned a study by the accounting

firm of Haskins & Sells to recommend changes in the OCCs examination system. The report, issued in 1975, recommended putting less reliance on comprehensive reviews of assets in the OCCs banks, increasing the reporting by banks, and establishing a computerized

off-site system.2 It also recommended making vast changes in examination procedures, and

implementation in 1976 resulted in a sharp drop in the annual number of on-site examinations, mainly by extending the time between examinations from 12 months to 18 months.3

In 1975 the OCC did institute an off-site system, the National Bank Surveillance System, in

which the primary tool was the Bank Performance Report (BPR).4 The surveillance system

drew on early economic research into the causes of bank failure and on the OCCs own

analysis, and the BPR used various financial ratios and benchmarks of financial performance for different peer groups to identify banks that could develop problems.5

The Haskins & Sells recommendations were designed to make the OCC examination

system more efficient, but in the early 1980s the computerized ability to analyze Call Report data was used to help justify reducing the frequency of on-site bank examinations and

therefore the number of bank examiners.

6 In fact, between 1975 and 1983 the OCC became
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History of the EightiesLessons for the Future 479

7 Andrew Albert, Comptrollers Office Throws a Bash, American Banker 148 (November 4, 1983), 16. 8 Barron H. Putnam, Early-Warning Systems and Financial Analysis in Bank Monitoring, Federal Reserve Bank of Atlanta

Economic Review 68 (November 1983): 612. 9 Drew Dahl, Gerald A. Hanweck, and John OKeefe, The Influence of Auditors and Examinations on Accounting Discretion in the Banking Industry (paper presented at the Academy of Financial Services conference, October 1995).

so identified with computerized off-site monitoring that the cake at the OCCs 120thanniversary celebration was in the shape of a computer.

7

During this same period the Federal Reserve Board (FRB) and the FDIC developed

their own off-site systems similar to the OCCs.8 However, as the number of bank failures

dramatically increased through the early 1980s, it became obvious that off-site monitoring

was not a substitute for frequent, periodic on-site examinations but was instead a valuable

complement to the examination process and could be used to target examination resources.

Examinations provide a scrutiny of management practices that no Call Report can capture,

and makes it possible for loans to be reviewed in detail. Moreover, studies have shown that

examinations affect the integrity of Call Reporting by encouraging banks to recognize loan

losses in a timely manner. And unless Call Report data are accurate, an off-site system will

not be effective.9

To make surveillance systems more useful, changes were introduced in the early

1990s. As a result, contemporary bank surveillance systems are designed to take Call Report data and build indicators of the condition of a bank so that regulators can determine

whether additional supervisory attention is warranted before the next regularly scheduled

on-site examination. Regulators have also developed various failure models that predict

how many banks have a high probability of failure within the next two years. These models

are used to plan for the FDICs future cash needs and to alert examiners to the impending

failures.

Advantages and Disadvantages of Off-Site Monitoring 

The best way for supervisors to track the condition of banks is to conduct frequent, periodic on-site examinations of banks. But examiners cannot be perpetually on-site at all

banksthat would be prohibitively expensive and, for most banks, unnecessary. Even in

1988, the worst year of the bank crisis, only approximately 2 percent of U.S. banks failed.

Therefore, regulators now help bridge the time between regularly scheduled examinations

by combining off-site monitoring systems and additional examinations so that they have

up-to-date evaluations of the financial condition of banks.

Off-site systems currently being used by bank regulators have several strengths. First,

they are current. That is, they are updated every quarter with new Call Report information. Second, they are far less intrusive than on-site examinations. This is very important.
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10 David Holland, Don Inscoe, Ross Waldrop, and William Kuta, Interstate BankingThe Past, Present, and Future, FDIC

Banking Review 9, no. 1 (1996): 117.

To achieve the same level of surveillance without these systems would require more on-site

examinations and more staff. Third, these systems help regulators target examination resources efficiently. Institutions that show signs of financial distress can have their examination dates moved forward, or an institution can be contacted and asked to explain the

changes observed. This also means that well-run and highly rated institutions will generally

not be examined outside of the regular examination schedule. Fourth, todays off-site systems enable the failure models to be modified and updated with relatively few staff resources. Finally, whereas examinations focus on the current condition of the bank, off-site

systemswhich are current in terms of informationhave the potential to identify highrisk characteristics that may increase the probability that a bank will fail.

Although the systems now in use function reasonably well, they have some weaknesses that generally stem from their complete dependence on Call Report data. For example, Call Reports do not note either the quality of management or management practices, as

on-site examinations do, so the evaluation of management remains outside the realm of offsite systems. Likewise, under current methods, only on-site examinations look at individual

loan files. A less-serious example of the problem with relying solely on Call Report data is

that the accuracy of any of the models data depends on on-site examinations (accordingly,

the predictive power of the models decreases as the time between examinations increases).

In addition, because of increased industry consolidation, only on-site examinations can determine the geographic loan concentrations of some banks.10 Finally, because contemporary

off-site models are used to assist in the examination process, they are current condition oriented, which is their first strength, but for that very reason they do not measure the longterm risk in a bankyet key aspects of changes in a banks operations may take place as

much as four or five years before a banks crisis. 

Discovering What a Forecasting System Can Do

To see why todays surveillance models work well in identifying a banks current condition but not the risks a bank may face well into the future, researchers at the FDIC examined the characteristics of banks that failed and banks that survived over a five-year period.

To examine how banks condition changed over time, they constructed a data set consisting

of all banks that existed in 1982 and either were still in existence in 1987 or had failed in

1986 or 1987 (banks that failed after 1987 or between 1983 and 1985 were excluded). The

set of banks examined therefore contained two clear types: those that existed over the entire

five-year period and never experienced failure, and those that existed at the beginning of the

five-year period and failed during the fourth or fifth year. 
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History of the EightiesLessons for the Future 481

Four indicators of bank condition were examined: (A) equity ratio, (B) coverage ratio

(equity plus reserves less delinquent loans, to total assets), (C) return on assets, and (D)

nonperforming loans (see figure 13.1). In 1982, banks that would not fail during the next

five years had an average equity ratio of 8.84 percent, while banks that would fail had a ratio 55 basis points lower (8.29 percent). This lower ratio is above the level that, under the

risk-based system now in effect, is considered well capitalized. The coverage ratio, of

course, was also lower for future failures: 6.57 percent versus 7.90 percent; so was the return on assets: 86 basis points versus 101 basis points. Nonperforming loans were slightly

higher in the future failures: 2.3 percent of assets, versus 1.44 percent of assets in nonfailed

banks. For all of the indicators, the average was worse for the future failures than for the

survivors. However, these ratios would not in themselves be considered typical, or predictive, of banks that would fail, for the future failures also had good capital levels, decent

earnings, and a low percentage of nonperforming loans.

With each passing year, the divergence between the healthy banks and the failed banks

grew. By 1984, three years before failure, the equity ratios of the failing banks were 179 basis points lower than those of the nonfailed banks (6.85 percent versus 8.64 percent). The

healthy banks had maintained a return on assets of 84 basis points, whereas the failures had

fallen to -77 basis points. The future failures also showed the beginnings of large increases

in their nonperforming loans, which had risen from 2.3 percent in 1982 to 5.05 percent in

1984. 

The data from 1985 demonstrate the wide differences that had developed between the

two groups of banks. Equity at the healthy banks was virtually unchanged at 8.63 percent

(compared with 8.64 percent in 1984), whereas at the future failed banks it had dropped 199

basis points to 4.86 percent. The failed banks coverage ratio had fallen below zero (-2.06

percent); losses were accumulating rapidly, bringing the return on assets down to -2.71 percent; and the level of nonperforming loans had increased 76 percent to 8.87 percent of assets, above the average equity of three years earlier.

At the end of 1985, just before their failure, the failing banks are easy to identify. Their

average equity was a very low 1.54 percent (healthy banks had 8.54 percent) and they were

suffering enormous losses, with an average return on assets of -5.44 percent; nonperformingloan ratios exceeded 12 percent. These data clearly show, therefore, that standard indicators

of condition can identify banks that are already in financial distress but do not indicate

which banks may become distressed.

Instead of looking at indicators of condition, if we look at the risk characteristics of the

same banks over the same five-year period, we find a somewhat different pattern. Whereas

the condition indicators for failed and surviving banks were very similar many years before

failure, some of the risk indicators show wide differences several years prior to failure. The
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482 History of the EightiesLessons for the Future

1982 1983 1984 1985 1986 1982 1983 1984 1985 1986

C. Return on Assets*

*Net income/assets

1982 1983 1984 1985 1986

-6

-4

-2

0

2

Percent

B. Coverage Ratio*

*(Equity + reserves nonperforming loans) /assets –

-10

-5

5

10

Percent

Note: “Failed” means banks that existed in 1982 and failed in 1986 or 1987; “nonfailed” means banks that existed during

the entire period and never failed.

D. Nonperforming Loans*

*As a percentage of assets

1982 1983 1984 1985 1986

0

4

8

12

Percent

A. Equity Ratio*

*Equity/assets

0

2

4

6

8

10

Percent

0

Banks That Subsequently Failed Banks That Did Not Fail

Figure 13.1

Bank Condition Ratios for Failed and Nonfailed Banks, 19821986
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 483

11 The definitions of these risk ratios and explanations of what they measure are presented in table 13.1 (in the subsection entitled Developing a Procedure).

four ratios used to measure risk in a bank were (A) the loans-to-assets ratio, (B) the asset

growth rate, (C) the interest-and-fees-to-loans ratio, and (D) the salary-to-employee ratio

(see figure 13.2).11

In 1982, in all four risk categories the surviving banks had lower average ratios than

the failed banks. The surviving banks had a loans-to-assets ratio of 49.6 percent, a full 10

percent below the 59.8 percent ratio of the failed banks. Failed banks had an interestincome-and-fees-to-loans ratio that was almost 200 basis points above the ratio of the surviving banks (8.91 percent versus 6.97 percent). Failed banks were also growing slightly

faster than the survivors: 13.9 percent per year versus 12.1 percent. And failed banks had

salary-to-employee ratios that were 5.7 percent above those of surviving banks: $20,364 per

employee for failed banks and $19,272 for survivors.

The pattern that developed over time for the risk indicators was very unlike the pattern for the condition indicators. For three out of four of the risk indicators, the difference

between failed and surviving banks hardly changed at all. By the end of 1986 the failed

banks had an average loans-to-assets ratio 12 percent higher than that of surviving banks (in

1982 the difference was 10 percent). The interest-and-fee-income ratio was still 200 basis

points higher for failed banks than for survivors; and the failed banks salary ratiowhich

in 1982 had been 5.7 percent higher than that of the surviving bankswas 4.8 percent

higher ($24,637 for failed banks, $23,500 for survivors). The only ratio that demonstrated

a dramatic difference over time was the asset growth rate. Over the entire period the asset

growth rate for failed banks plummeted, going from a high of 13.9 percent in 1982 to 9.88

percent in 1984 and then to -5.5 percent in 1986, but the asset growth for surviving banks

never fell below 8.8 percent. 

The condition indicators and risk indicators behave in such dissimilar ways (except

for asset growth) that they are obviously measuring different aspects of banks. The current

condition of a bank, as measured by the four condition indicators discussed above, can be

viewed as the result of the risks the bank has accepted over a number of years. Exposure to

excess risk can ultimately produce the conditions that cause failure. Exposure to risk involves the types of loans the bank issues or the type of business it chooses to enter, and in

their day-to-day operations banks are continuously changing their risk exposure. Eventually such changes are reflected in the condition statements of the banks. If risk can be measured, it might be possible to see if banks that engaged in riskier practices failed at a higher

rate than less-risky banks.
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A. Loans to Assets

Percent

B. Asset Growth Rate

Percent

1982 1986

D. Average Employee Salary

$Thousands

C. Interest Income and Fees Ratio*

*Total interest and fees on loans and leases/total loans and leases

Percent

Note: “Failed” means banks that existed in 1982 and failed in 1986 or 1987; “nonfailed” means banks that existed during

the entire period and never failed.

Banks That Subsequently Failed Banks That Did Not Fail

1982 1983 1984 1985 1986

0

20

40

60

1982 1983 1984 1985 1986

-10

0

10

1982 1983 1984 1985 1986

0

10

20

1982 1983 1984 1985 1986

0

4

8

Figure 13.2

Bank Risk Ratios for Failed and Nonfailed Banks, 19821986
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History of the EightiesLessons for the Future 485

12 George J. Vojta, Bank Capital Adequacy (1973). 13 Business Week (October 21, 1982): 82.

Banks earn profits by accepting and managing risk. For example, when a bank issues

a loan, the banks management is making a conscious decision to accept the risk that the

borrower will default. By issuing a large number of loans the bank can spread the risk of default over an entire portfolio. Borrower default is just one of the risks that bank management facesand an important aspect of managements responsibilities is to establish the

levels and types of risks the bank can accept, given managements ability to manage risk

and the banks ability to absorb the losses that may result. If the bank accepts too little risk,

earnings will suffer, but if it accepts too much, it might face losses that would consume the

institutions capital.

The types of risk a bank faces include credit risk, interest-rate risk, concentration risk,

liquidity risk, and operating risk. Credit risk is the risk of default by a borrower. Interestrate risk refers to the risk that an asset will lose value as interest rates rise or fall, or the risk

that interest-rate changes will adversely affect income. Concentration risk refers to a situation in which a large percentage of assets are concentrated in one product or in one geographic area. This type of risk can flow from the very nature of the banks business. For

instance, small banks in agricultural communities are highly exposed to the risks of the

agricultural economy. Likewise, specialized mortgage lenders are highly exposed to extreme changes in mortgage markets. Concentration risk can also occur when an institution

undergoes rapid growth: the rapid growth results in the banks having a high concentration

of unseasoned loans, probably approved in a boom economy, or at least a benign one, but

this high concentration of recent loans puts the institution at considerable risk when the economic environment worsens. Liquidity risk refers to potential difficulties in meeting cash

demands from liability holders out of current assets. Operating risk is the risk of loss from

mistakes and inefficiencies in the operation of the bank. A bank can fail from any one of

these risks or from a combination of them.12

These risks may be magnified when bank management changes the institutions goals.

For example, one particularly well-documented case is that of Continental Illinois (see

Chapter 7). In 1976, acting on a report by the management consultants McKinsey & Co.,

the bank made very significant changes in its operating philosophy and decided to concentrate its lending in high-growth segments of the economy. In addition, to implement this

strategy fully the bank decentralized its lending function and made loan approvals much

easier to obtain.13 In other words, the bank made a conscious decision to increase its risk

profile. By concentrating lending in high-growth areasthat is, by lending into a boom

sectormanagement increased the risk that loan defaults would result when the bust occurred. By reducing management controls for loan approvals, the bank also made it more
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14 Ibid., 83. 15 Richard W. Madsen and Melvin L. Moeschberger, Statistical Concepts with Applications to Business and Economics

(1986), 36065. 16 In a civil case, the standard is the less-exacting a preponderance of the evidence.

likely that loans would go to financially weak firms. Not long after initiating these changes,

Continentals senior management established a goal of growing to be one of the three

largest commercial lenders in the nation.14 Within two years after changing its goals, Continental had markedly increased its risk exposure.

Though it is difficult to detect differences in the financial condition of failing and surviving banks many years in advance of the failure, it may be possible to determine if failed

and surviving banks have different risk characteristics. But even if it is possible to identify

risk characteristics and therefore to identify a large percentage of eventual failures, it is

nonetheless true that among banks with the same risk characteristics, a very high percentage may survive.

Thus, both accuracy and comprehensiveness are required if a system or model is to be

judged effective. A failed-bank model might be calibrated so that a high percentage of its

predicted bank failures actually fail, with a correspondingly low percentage of predicted

bank failures that actually survive. This high accuracy, however, may not mean that the

model identifies all, or even a majority, of the problem institutions. Alternatively, the model

can flag a large percentage of the total number of banks as potential problems or failures,

and although the probability that any individual bank will actually fail is low, a large percentage of failing institutions will nonetheless eventually be captured. 

In statistics one quantifies these trade-offs by deciding what type of error one is willing to acceptType I or Type II. AType I error is an error one makes by rejecting a null hypothesis when the null hypothesis is in fact true, and a Type II error is an error one makes

by accepting a null hypothesis when the alternative hypothesis is in fact true.15 The tradeoff between Type I and Type II errors is exemplified by the U.S. criminal justice system, in

which a person is presumed innocent until proven guilty. In a criminal trial, the null hypothesis is that a defendant is not guilty. A Type I error occurs when an innocent person is

found guilty (convicting the innocent). A Type II error occurs when a person who is guilty

is incorrectly acquitted (acquitting the guilty). There is an obvious trade-off between the

two types of errors. If one wants to have a very low Type I error (few innocents wrongly

convicted), one usually accepts the fact that there will be a large Type II error (a large percentage of acquitted people will in fact be guilty). To minimize the occurrence of the Type

I error, the courts require that there be evidence beyond a reasonable doubt in order to

convict someone.16 Likewise, if a small Type II error is desired (so that few people who are
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History of the EightiesLessons for the Future 487

actually guilty are acquitted), then there is likely to be a very large Type I error (many innocent people will be judged guilty).

These trade-offs are inherent not only in statistical models but also in the bank examination system. All banks are examined within 18 months of the previous examination

whether or not there is any evidence of a negative change in the banks financial condition.

The examinations are performed to capture the relatively few banks that have significant

changes. Thus, in contrast to the criminal justice system, the bank examination process has

a large Type I error: many healthy banks are examined so the regulators can find the few

that had negative changes. These trade-offs are important to keep in mind when one considers the various surveillance systems. 

Real-World Obstacles to Forecasting

For several reasons, it is difficult to identify future problem banks even when the effort is made to identify risk factors. The life cycle of problem banks is such that in its early

years, future problem banks cannot yet be clearly distinguished from other banks. In addition, both the economic environment and the financial process are dynamic and not easily

modeled by the forecasting tools available. 

The Life Cycle of a Bank Failure

In interviews with bank and thrift regulators, rapid loan growth was identified again

and again as a precursor to failure. Whether or not loan growth is the primary risk in which

banks engage, one regulators description of a three-phase process by which rapid loan

growth evolves into a major problem does a good job of laying out the long-term nature of

the development of a banks financial distress.

In the first stage, there is rapid loan growth; loan concentrations emerge, and lending

is aggressive (internal controls in the growth areas are weak, and underwriting standards are

lenient). The increased lending may be, but is not always, funded by a volatile lending

source. This growth could occur throughout the entire institution or within a specific asset

type. If the growth is in a specific asset type, the increase could stem either from growth in

concentration in a loan category or from a shift into a new activity, with subsequent growth.

If the rapid growth draws the attention of the relevant regulator, management usually points

to the excellent earnings and contribution to capital that the growth has provided. This stage

of the development of the problem can take up to two years.

In the second stage, the institution has rising loan-quality problems. Associated expenses may far exceed industry averages. Nonrecurrent sources of income are used to maintain the same level of profits that existed during the growth phase. Eventually profits begin

to decline, and inadequate reserve levels become apparent. At this point the bank may be
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loaned up (that is, have a high loans-to-assets ratio). Management may still believe that

the problem is manageable. This stage may take an additional one to two years.

In the final stage, deteriorating asset quality is a serious problem. The institution is incurring large loan losses, and charge-offs have increased. If the institution is large, the capital markets have recognized that the institution has inadequate loan-loss reserves and are

unwilling to provide fresh capital. At this point, major changes in the banks operations are

necessary. Dividends may be cut, expenses (mostly personnel) are slashed, and assets are

sold to cover charge-offs and operating expenses (especially in larger institutions). This crisis phase may last up to a year and results either in the failure of the bank or, if dramatic and

fundamental changes are made, in its eventual recovery.

As this account of the life cycle of failure makes clear, only in the course of years do

changed behavior and the acceptance of greater risk lead to financial distress or failure. After all, neither growth itself nor most other risk taking is necessarily bad for a financial institution. Banks earn their income by assuming risk; to increase risk through growth can

therefore be a sound strategy. Such a strategy would ideally be accompanied by increases in

capital as a buffer against higher losses, maintenance of high underwriting standards, and

attention to proper risk managementin other words, by prudent management of the institutions growth. Moreover, regardless of whether the increased lending is prudent, ill timed,

or very risky, the growth will generate added revenue from increased loan fees and interest

income. In addition, because these are all new loans, initially there are no delinquencies and

no loss charge-offs, so that the growth is almost always accompanied by growth in income

and capital (assuming retained earnings). Only over time do the effects of growth or other

risk takingwhether these effects are good or badbecome apparent. This long lead time

before problems appear makes it difficult to identify future problem banks accurately.

The Dynamics of the Economic Environment

Long lead times are not the only problem encountered in forecasting failures. There

are two others.

One is that economic conditions, both regional and national, change over time, but the

changing nature of economic conditions is not built into failure forecasts. All failure forecasts are based on financial profiles of banks, indicating whether a bank has the characteristics of other banks that have failed. This seems relatively straightforward. If it is found

that failed banks have low capital levels, high percentages of nonperforming assets, and

poor earnings, then nonfailed banks with similar financial profiles should be considered

probable failures. Embedded in this type of analysis, however, is the underlying assumption

that the set of economic conditions under which the failures occurred will not change. Without explicit economic variables in a model, the forecasts for future failures assume the same
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 489

17 NIH Pub. No. 93-2724, rev. October 1992, National Heart, Lung and Blood Institute, National Institutes of Health.

economic environment as the one in which the actual failures occurred: the then-current

interest-rate environment, the particular real estate market, and the same general nationwide economic health. But if economic conditions change, as they always do (for example,

there may be a recession or a dramatic interest-rate change), the number of actual failures

(or CAMEL rating downgrades) can substantially diverge from the forecasts.

The Dynamics of the Financial Process

Finally, forecasting is difficult because normal economic models assume linearity, but

as the three-stage life cycle shows, the financial process that leads to failure is inherently

nonlinear. Failure is a rare event, and only extreme behavior eventually causes a bank to

fail. For an analogy, consider the situation of people who are overweight (assuming that excess weight is bad for a persons health): if overweight people continue to gain weight their

health will worsen, and if they lose weight their health will improvebut if they lose too

much weight, their health will again suffer. Many aspects of bank risk taking can be thought

of in the same way: too much growth can result in financial distress, but too little may

threaten the banks long-term financial viability. This too much or too little phenomenon

makes the financial process nonlinear; hence, both very high growth and very low growth

may be risky. For that reason, economic models that attempt to capture the specific dynamics of the financial process are unstable and lumpy, and do not isolate the risks of failure.

Analysis by Risk Groups

To isolate these risks, contingency table analysis is needed in which the specific dynamics of the process are ignored and one looks at levels of risk or risk groups to classify

banks or people (the underlying dynamics of the process, nevertheless, are always present).

Analysis by risk groups is most common in epidemiological studies. For example, a person

who smokes has twice the risk of having a heart attack compared with a person who does

not smoke. The risk of a heart attack is also double for a person who has high blood pressure or high blood-cholesterol levels. In addition, these risk factors are multiplicative: if a

person has two factors, the risk of a heart attack increases four times; if all three factors are

present, the risk increases eightfold.17 For banks it may be possible to determine risk factors

in a similar mannerin other words, to develop nonlinear models. The two subsections that

follow give details of an attempt to do that. 
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Developing a Procedure

In connection with heart attacks, the levels for high blood pressure or high cholesterol have already been determined. In contrast, for banks the levels for risk factors have

not yet been identified. We assume, however, that risk increases when the risk measure increases. The goal in analyzing risk measures is to find the set of variables that has the greatest predictive power for determining which banks will fail. 

A group of researchers at the FDIC chose nine measures of risk to study and eventually used eight of them (see table 13.1). To determine how these measures of risk predict

failure individually and as a set, the researchers divided each measure into five risk groups

(quintiles) from high to low, using the data for the years 1980, 1982, 1984, 1986, and 1988.

For each year studied, banks that never failed were separated from banks that failed four or

five years later (all other banks that existed for only part of the five-year period were excluded from the study, as is explained in more detail below). Both groups of banks in each

period were then analyzed to determine which risk measures were the best long-range predictors of failure (the details of the analysis also appear below).

A brief summary of the results of the analysis appears here (a fuller presentation appears in the next subsection). Among this group of variables, the best long-range predictor

Table 13.1

Ratio Measures of Bank Performance

Identification of Variable What the Variable Measures

Loans-to-assets ratio Liquidity and risk. The higher the ratio, the greater the amount

of the banks total portfolio that is subject to default risk.

Deposits over $100,000 (large deposits) The use of larger deposits to fund assets. These deposits may

to total liabilities* be more volatile than fully insured deposits.

Return on assets The banks profitability. Low ROA may encourage risk taking

by the bank. High ROA may indicate high-risk lending to increase profits.

Asset growth from previous year Risk of growth.

Loan growth from previous year Risk of growth.

Operating expenses to total expenses Managements control of expenses. Higher expenses are assumed to be an indicator of loose controls.

Salary expenses per employee Managements control of expenses.

Interest on loans and leases to total loans and leases The average income of loans. High yields might indicate that

(interest yield) the bank is originating high-risk loans.

Interest and fee income to total loans and leases Income. The addition of fees to the variables may catch firms

(interest and fees to loans) that are loading up on fee income.

* This variable was eventually dropped (see the discussion below about banks in Texas).
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 491

of failure is a banks loans-to-assets ratio. This result appears to be consistent across all

years and all regions. In all five years studied, approximately 50 percent or more of the failures come from the top loans-to-assets quintile. In the last three periods (1984 through

1988), if banks in that quintile are excluded, then the banks in the highest return on assets

(ROA) risk group are the best predictor of failure.

The evidence is strong that the basic pattern of bank distress and failure as set forth by

the regulators and presented above is valid. Banks that eventually become troubled do undertake risky business strategies several years before their financial condition deteriorates.

But even if it turns out to be possible to identify these risky strategies, it may still be very

difficult to identify which banks within a risk group will fail and which will survive. In addition, the predictions have a large Type II error: although the procedure identifies the quintile that contains a very large percentage of the failures, more than 95 percent of all the

banks in the quintile never fail.

Contingency Table Analysis: Methodology and Results

The data for the study were constructed from all BIF-insured institutions (banks and

savings banks) that existed in the beginning year and either did not ever fail (then or later)

or failed four or five years from the beginning date. Thus, the study excludes banks that existed in the beginning year and (a) failed before the fourth year, (b) were merged out of existence during the period, or (c) failed subsequently; and it also excludes all de novo banks

created during the period. The reasons for the exclusions were that banks that failed or

merged in the interim period were not in the sample long enough to be studied, nor were de

novo banks, and banks that failed subsequent to the period under study were excluded to ensure that each sample had clearly defined groups of survivors and failures. 

So that an epidemiological approach could be used, a contingency table analysis was

performed on each years data. First, a logit regression was performed on each variable,

where the dependent variable was whether the bank failed or did not fail (1 or 0). The variable with the highest predictive power for failure was determined by a Chi-Square test score

for each regression. The coefficients for each quintile grouping of the variable were then

compared, and a Chi-Square test was performed to determine which quintile or group of

quintiles was the best predictor of failure. The split of the quintiles created a high-risk

group and a low-risk group. The analysis was then repeated on both of the two groups to

determine the next-best predictor of failure in each group. This procedure was repeated for

each subgroup until the cells became too sparse (the number of failures was too low) to analyze (see figure 13.3).18

18 The procedure used was not complicated but was very time-consuming. Thus, it was important to keep the number of analyzed variables at a reasonable level.
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492 History of the EightiesLessons for the Future

ALL BANKS

1988

1.24% Failed

n=10,707

LOW RISK

Loans-to-Assets

Quintiles 1-4

0.71% Failed

n=8,564

Return on Assets

Quintiles 1-4

0.38% Failed

n=6,914

Return on Assets

Quintile 5

2.12% Failed

n=1,650

HIGH RISK

Loans-to-Assets

Quintile 5

3.36% Failed

n=2,143

Average Salary

Quintiles 1-4

2.06% Failed

n=1,651

Average Salary

Quintile 5

7.72% Failed

n=492

Figure 13.3

Procedure Used in Contingency Table Analysis

The five study periods began in 1980 and spanned ten years of failures, from 1984 to

1993. Included were 1,193 failures. Not included were 300 failures that occurred during the

period but were excluded from the study because they fell into one of the following groups:

(1) banks that did not exist for at least four years, (2) banks that were taken over under a

cross-guarantee subsequent to the passage of the Financial Institutions Reform, Recovery, and Enforcement Act of 1989, (3) banks that were closed primarily because of fraud,

and (4) subsidiary banks of First Republic and First City that had composite CAMEL ratings of 1 or 2 as of closing (similar to cross-guarantees).

An examination of the relationship between the nine variables and the failures over

the five different periods reveals that banks in the highest loans-to-assets quintile had the

highest probability of failure for the periods beginning in 1980, 1982, and 1988 and the second-best high-risk probability in 1984 and 1986. In those two years the best predictor was

the large-deposit ratio. 

However, because the large-deposit ratio did not show up as either a primary or a secondary indicator in 1980 or 1982, there was concern that it might not be an indicator of
Table 13.2

Probability of Failure When a Bank Appears in the Highest-Risk Category

Increased Probability Highest of Failure from Total

Aggregate Failures Loans-to-Assets Quintile Population to Banks in

Probability Probability Highest Loans-toBeginning of Failure Number of of Failure Number of Assets Quintile

Year (Percent) Failures (Percent) Failures (Percent)

1980 1.51 184 3.62 88 140

1982 2.45 291 6.75 160 175

1984 2.89 332 8.20 188 184

1986 2.25 253 6.46 145 187

1988 1.24 133 3.36 72 171

Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 493

19 FDIC, Statistics on Banking: A Statistical History of the United States Banking Industry, 19341994, vol. 2 (1995), E545.

volatile funding. This issue arose apropos of banks in Texasthe predominant state in the

Southwestwhich was a unit-bank state. The lack of branching might have forced Texas

banks to rely more heavily on large deposits than did banks in states with branching. In periods of high growth, the inability to produce deposits through a branch system might also

produce high ratios of large CDs. It was hypothesized that the large-deposit ratio might be

a function of the particular region rather than an actual risk measure. To test the theory, the

researchers examined the distribution of Texas banks large-deposit ratio and found that

Texas banks were extraordinarily concentrated in the high quintiles of large deposits. In

1980, 63 percent of Texas banks were in the two highest quintiles. During the next four

years, assets in Texas banks grew 66 percent (from $118 billion to $198 billion),19 and by

year-end 1984, 84 percent of Texas banks were in the two highest quintiles of the largedeposit ratio (58 percent were in the highest quintile). In 1986 the comparable figures were

89 percent and 68 percent. It appears that Texas started the 1980s with a higher-thanaverage number of banks with a high percentage of large deposits, and banks in that state

disproportionately used large deposits to fund asset growth. Thus, large deposits indicated

a high probability of being a Texas bank rather than being an indicator of risk, so large

deposits were dropped from the list of variables.

Once large deposits were excluded, the loans-to-assets ratio was always the best predictor of future failure. Being in the highest loans-to-assets quintile more than doubled a

banks probability of failure (see table 13.2). More important, after 1980 more than 50 per-
Table 13.3

Probability of Failure When a Bank

Appears in the Highest- and Second-Highest Risk Categories

Highest Loans-to-Assets Quintile Subset of Loans-to-Assets Quintile

Probability Percent of Probability Percent of

Beginning of Failure Total Second-Level of Failure Total Failures

Year (Percent) Failures High-Risk Ratio (Percent)

1980 3.6 47.8 Interest and loan fees 7.2 31.5

1982 6.8 55.0 Interest and loan fees 11.6 35.4

1984 8.2 56.6 Asset growth 12.6 28.0

1986 6.5 57.3 Return on assets 12.0 27.3

1988 3.4 54.1 Average salary 7.7 26.3

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494 History of the EightiesLessons for the Future

20 After the first high-risk group was identified, the remaining banks were not redistributed into new, equal quintiles.

Rather, they were left in the original quintile distribution, with the already identified high-risk banks removed.

cent of the total failures for each cohort of banks came from the highest loans-to-assets

quintile. 

As noted, in the contingency table analysis the banks were split into two groups, the

high-risk group (in this case, the banks in the highest loans-to-assets quintile) and the

low-risk group (all other banks), and the calculations described above were repeated so

that the next-greatest indicators of risk could be found.20 This second-level analysis for the

high-risk group did not yield a consistent pattern for second-level predictors. In 1980 and

1982, the interest-and-fee-income ratio was the best second-level predictor; in 1984, 1986,

and 1988 the second-level predictors were, respectively, asset growth, return on assets, and

average salaries. This result is discouraging, for it indicates that the relationship between

the second-level risk indicators and failure is unstable (see table 13.3). 

If a bank was not in the highest-risk quintile, that did not mean the bank had no risk of

failure. A little under half of all banks that failed were not in the high loans-to-assets quintile, so it may be useful to see if the remaining banks that failed had any identifiable risk

characteristics. For banks that were not in the high-risk loans-to-assets quintile, the best

predictors of failures were loan growth in 1980, interest yield in 1982, and ROA in 1984,

1986, and 1988. The risk indicators for the so-called low-risk groups (that is, all groups except the highest-risk quintile) performed quite well. They identified a very large percentage

of the remaining failures, particularly in 1986 and 1988, when being in the highest ROA

quintile identified 57 percent of the remaining failures. If the high-risk and low-risk groups
Table 13.4

Probability of Failure in Low-Risk Banks

(Banks Not in the Highest-Risk Group)

Highest Loans-to-Assets Quintile Low-Risk Failure Indicator

Probability Percent of Probability Percent of

of Failure Total High-Risk Indicator of Faillure* Remaining

Year (Percent) Failures for Low-Risk Group (Percent) Failures 

1980 3.62 47.8 Loan growth 2.32 41.7

1982 6.75 55.0 Interest yield 3.76 40.4

1984 8.20 56.6 Return on assets 3.96 45.1

1986 6.46 57.3 Return on assets 3.74 57.4

1988 3.36 54.1 Return on assets 2.12 57.4

* This is the probability of failure in the remaining 80 percent of banks that are not in the high-risk loans-to-assets quintile.

 Excludes failures in the high-risk loans-to-assets quintile.

Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 495

were taken together in 1988, they would contain more than 80 percent of the study group

failures (see table 13.4).

From these results, one can infer that it may be possible to identify groups or populations of banks with a high probability of containing a high proportion of future failures, or

that it may be possible to identify large populations of banks with a very low probability of

failing in the future. Thus, the analysis described above has limitations that must be considered. First, to identify 80 percent of the failures, the contingency analysis flagged 35

percent of the entire study population for 1988: 2,143 banks in the loans-to-assets quintile

and 1,650 banks in the ROA quintile, or a total of 3,793. The entire study population for that

year consisted of 10,707 banks, 133 of which failed, and 107 of the failures (80 percent of

133) were in two identified risk groups. The two identified risk groups also contained 3,686

banks that did not fail, or approximately 97 percent. In addition, in 1988 there was no way

to identify which 3 percent would fail in 1992 or 1993. Nor would identification have been

much easier if only the highest-risk loans-to-assets banks had been identified. In the 1988

cohort approximately 96 percent of the high-risk loans-to-assets quintile survived, and in

the 1984 cohort (the one with the highest number of failures), 92 percent survived. Second,

to differentiate clearly between failures and survivors, the analysis was performed on a subset of all banks, but the exclusion of some banks from the analysis might have introduced

measurement errors. Third, the lack of consistency in the secondary risk factors may mean

that the industry changes so rapidly that supervisory attention could be diverted to monitoring diminishing risks instead of identifying emerging risks. 
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496 History of the EightiesLessons for the Future

21 Volatile liabilities are defined as the sum of the following: time deposits of $100,000 or more, deposits in foreign offices,

federal funds purchased and repurchase agreements, demand notes issued to the U.S. Treasury, and other liabilities for borrowed money.

The FDICs Growth-Monitoring System (GMS)

The contingency analysisattempting to identify the interactions within a set of risk

groups in order to find a way to predict future failuresfeeds into and seeks to improve the

FDICs growth-monitoring system (GMS). GMS was developed during the mid-1980s and

was designed to detect the initial stage in the life cycle of failing banksthe rapid-growth

stage. The systems premise is that rapid growth in total assets (or loans) represents a risky

activity of which bank supervisors should be aware. Growth-related risk can come in at least

two areas, loans and bank management: there may be increased loan concentrations in risky

areas, and there may be management lapses such as lowered underwriting standards, increased reliance upon volatile funding, or a general weakening of internal controls in order

to facilitate rapid growth. Banks that GMS identifies as rapid-growth institutions in these

two areas are flagged for off-site review and may receive increased supervisory attention. 

The system is based upon the levels and quarterly trends of five summary measures.

These include two growth rates (for total assets and for loans and leases) along with three

financial ratios (as percentages of assets): loans and leases, plus securities with maturities

of five years or more; volatile liabilities; and equity capital.21 The system measures both the

levels and the trends (growth) of the three financial ratios in addition to asset growth and

loan growth, for a total of eight terms. Banks percentile rankings are computed quarterly

for each of the eight terms; all percentile rankings are relative to a banks Uniform Bank

Performance Report (UBPR) peer group (see appendix B). There are 25 UBPR peer groups

based on asset size, location in a metropolitan area or a nonmetro area, and number of

branch offices. These eight percentile rankings are subsequently weighted in a two-step

process, and the weighted percentile rankings of the eight terms are then summed to give a

GMS score (see table 13.5).

Composite GMS scores are evaluated separately for two groups of banks. The first

group is composed of banks whose quarterly asset and loan growth rates were 5 percent or

more (high-growth banks). For all high-growth banks, composite GMS score percentile

rankings are computed. Banks in the highest composite GMS score percentilescurrently

the 95th to 99th percentilesare flagged for off-site review. Supervisors may also review

banks beneath the 95th percentile, particularly those with poor CAMEL ratings. The second

group is composed of banks with quarterly asset and/or loan growth under 5 percent (lowgrowth banks). These low-growth banksGMS scores and related information are available

for review by regional office examiners in the GMS system. 
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 497

The next subsection contains a detailed review of GMSs predictive abilities as many

as five years before deterioration in banksfinancial condition. The findings can be summed

up as follows: GMS appears to perform the function for which it was designed. The system

identifies a group of banks that have a higher-than-average risk of failure, and may do so up

to four years before failure. When a standard failure estimation technique is used, the GMS

score has also been found to be a significant long-term predictor of failure in three out of

four time periods. In addition to predicting failure moderately well, GMS has been a

better-than-average predictor of CAMEL downgrades two to three years in advance of the

event. 

No significant changes have yet been made to the system. However, marked and significant improvements have been suggested for each stage of the process (these suggested

changes are also detailed in a later subsection). Major proposed improvements include a

new weighting scheme for the GMS score, new variables for inclusion in the score, better

methods of constructing growth variables, and use of peer groups not based on the UBPR

groupings. If all of these suggested changes were made, they could increase the percentage

of banks accurately identified as future problems and could decrease the percentage of

banks incorrectly identified as future problems (in other words, the changes should decrease Type II errors).

Effectiveness

If GMS is effective, risk detection should occur well before there are adverse changes

in banksfinancial condition. Therefore, researchers evaluated GMSs predictive abilities as

much as five years before deterioration by comparing (1) GMS composite scores with future bank-failure rates, and (2) GMS score percentile rankings with changes in banks composite safety-and-soundness (CAMEL) ratings. 

Table 13.5

Hypothetical GMS Score Computation Example

Trend Trend Ratio Ratio Raw

Weight Percentile Weight Percentile Score Weight Score

Asset growth 0.60 x 98 + - x - = 58.9 x 0.67 = 39.4

Loan growth 0.60 x 99 + - x - = 59.4 x 0.00 = 0.0

Loans and

securities/assets 0.60 x 98 + 0.40 x 82 = 91.6 x 0.11 = 10.1

Volatile 

liabilities/assets 0.60 x 96 + 0.40 x 86 = 92.0 x 0.11 = 10.1

Equity/assets 0.60 x 98 + 0.40 x 85 = 92.8 x 0.11 = 10.2

Composite GMS Score 69.8
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498 History of the EightiesLessons for the Future

22 These results are consistent with those reported in Chapter 12.

Early detection of failure. To review the first relationship (between GMS composite

scores and future bank-failure rates), the researchers began by computing GMS composite

scores for the last calendar quarter of each year between 1984 and 1994. Next they ranked

banks GMS scores into deciles, and compared bank failures occurring in subsequent years

(between one and five years after scores were assigned) across GMS score deciles. For

computational simplicity, open-bank assistance transactions were excluded from the group

of failed banks. The analysis showed that banks in the lowest GMS score decile usually

failed at the highest rates during the two years immediately after scores were measured. The

situation was reversed, however, for failures occurring between three and five years after

scores were computed: the long-term failure rates were generally higher for banks in the

highest GMS decile. These results are in agreement with the life-cycle profile for failing

banks.

For example, among banks ranked by GMS score deciles for December 1984, approximately 39 percent of the banks that failed during the next year were in the lowest

(first) GMS score decile (see table 13.6). The proportion of 1986 failures in the lowest GMS

decile was also high, at approximately 19 percent.22 Failures in subsequent years were more

frequent for the highest (tenth) GMS score decile: banks in the tenth GMS decile accounted

for approximately 21 percent of 1987 failures, 20 percent of 1988 failures, 22 percent of

1989 failures, and 20 percent of all failures occurring between 1990 and 1995. Similar results (not presented here) were obtained for other GMS score ranking years. 

Exam rating changes. The reason for targeting banks for inclusion on an off-site review list is that they may be undergoing rapid changes in condition. Thus, the second test

of the usefulness of GMS was the relationship between GMS score percentile rankings and

subsequent changes in banks composite safety-and-soundness (CAMEL) ratingsmore

specifically, changes in CAMEL ratings measured between banksmost recent CAMEL rating as of the date of the GMS ranking and the examination subsequent to the ranking date

(the analysis used examination ratings over a period of two years before and two years after the date of the GMS score ranking). Those tests (not presented here) revealed no consistent relationship between banks GMS rankings and changes in CAMEL ratings, either

downgrades or upgrades. That result was not unexpected, however, given the previous results for failure rates. The evidence from failure rates and GMS rankings indicates that

rapid growth is related to failure three or more years afterward. To the extent that CAMEL

ratings are concurrent as opposed to leading indicators of condition, deterioration in

CAMEL ratings would not be expected in the near term. Rather, deterioration in CAMEL

ratings among banks in the highest GMS decile is expected three or more years after rapid

growth occurs. 
History of the EightiesLessons for the Future 499 

Table 13.6 Bank Failures by GMS Score Ranking and Failure Year (Number and Percent of Years Failures) December Failed Failed Failed Failed Failed Failed 1984 Never 1 year 2 Years 3 Years 4 Years 5 Years More Than 

GMS Decile Failed Later Later Later Later Later 5 Years Later 1 1,318 9.74% 42 38.89% 26 18.98% 24 13.48% 16 8.79% 12 6.52% 28 8.00% 

2 1,363 10.07 15 13.89 22 16.06 19 10.67 10 5.49 13 7.07 25 7.14 

3 1,364 10.08 6 5.56 18 13.14 16 8.99 15 8.24 22 11.96 26 7.43 

4 1,376 10.17 7 6.48 12 8.76 12 6.74 14 7.69 18 9.78 28 8.00 

5 1,389 10.27 5 4.63 6 4.38 13 7.30 16 8.79 10 5.43 28 8.00 

6 1,373 0.15 4 3.70 10 7.30 18 10.11 9 4.95 17 9.24 36 10.29 

7 1,367 10.10 6 5.56 14 10.22 10 5.62 15 8.24 21 11.41 34 9.71 

8 1,364 10.08 9 8.33 9 6.57 15 8.43 26 14.29 14 7.61 30 8.57 

9 1,347 9.96 5 4.63 14 10.22 14 7.87 24 13.19 17 9.24 46 13.14 

10 1,269 9.38 9 8.33 6 4.38 37 20.79 37 20.33 40 21.74 69 19.71 

Total 13,530 100.00% 108 100.00% 137 100.00% 178 100.00% 182 100.00% 184 100.00% 350 100.00% 
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500 History of the EightiesLessons for the Future

Table 13.7

Comparisons of Exam Ratings as Assigned in 1985 and 1987

(Number and Column Percent)

December CAMEL CAMEL No CAMEL

1984 Upgraded Downgraded Change Missing Because of

GMS Decile Next Exam Next Exam Next Exam Mergers, etc. Total

01 248 19.89% 93 8.73% 323 9.70% 802 8.88% 1,466 9.99%

02 159 12.75 81 7.61 357 10.72 870 9.64 1,467 10.00

03 130 10.43 100 9.39 351 10.54 886 9.81 1,467 10.00

04 121 9.70 81 7.61 361 10.84 904 10.01 1,467 10.00

05 117 9.38 96 9.01 333 10.00 921 10.20 1,467 10.00

06 114 9.14 106 9.95 361 10.84 886 9.81 1,467 10.00

07 97 7.78 98 9.20 333 10.00 939 10.40 1,467 10.00

08 95 7.62 111 10.42 295 8.86 966 10.70 1,467 10.00

09 96 7.70 129 12.11 296 8.89 946 10.48 1,467 10.00

10 70 5.61 170 15.96 319 9.58 908 10.06 1,467 10.00

Total 1,247 100.00% 1,065 100.00% 3,329 100.00% 9,028 100.00% 14,669 100.00%

To test the latter hypothesis, the researchers looked at the relationships between GMS

rankings at a given year-end and changes in CAMEL ratings assigned one and three years

laterfor example, the relationships between GMS rankings at year-end 1984 and changes

in CAMEL ratings for exams given in 1985 and 1987 (see table 13.7). Nearly 16 percent of

CAMEL downgrades occurred among the highest GMS decile group, a higher proportion

than for any other decile. Moreover, for most examination years considered between 1984

and 1995, the proportion of downgrades generally rose with decile rankings. These results

support the previous relationships between GMS rankings and future failure rates; they also

support GMSs use as a leading indicator of bank risk.

Statistical significance of the results. What the previous analysis does not test is the statistical significance of the relationships between GMS score rankings and subsequent changes

in banks condition. To test the statistical significance of GMS scores in measuring bank risk,

the researchers included GMS scores in standard models of bank-failure prediction.

Logit Model Methodology 

Logit estimations of the relationships between banksfinancial condition and the incidence of failure were obtained with the use of year-end financial data, actual failures, and

assistance transactions during the subsequent two years (see appendix A). Equation 1 is the

basic model:
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 501

23 Gerald A. Hanweck, Gary Fissel, and John OKeefe, A Comparative Analysis of Modeling Methodologies of Financially

Distressed Banking Firms (paper presented at the Financial Management Association Meetings, October 1995).

Equation 1

Likelihood of failure(i, next two years) = c0 

+ c1(Capital, loss reserves)(i) 

+ c2(Loans past due 3089 days)(i) 

+ c3(Loans past due 90 days or more, non-accrual loans, repossessed 

real state)(i) 

+ c4(3-year mean operating income)(i) 

+ c5(3-year standard deviation in operating income)(i) 

+ c6(Examination interval, normalized)(i) 

+ c7(Most recent capital rating)(i) 

+ c8(Most recent asset rating)(i) 

+ c9(Most recent management rating)(i) 

+ c10(Most recent earnings rating)(i) 

+ c11(Most recent liquidity rating)(i) 

+ c12(Average salary/employee)(i) 

+ c13(Multibank holding co. dummy)(i) 

+ c14(Log of bank assets)(i) 

+ c15(GMS score current year-end)(i) 

+ c16(GMS score prior year-end)(i) 

+ c17(GMS score 2 years prior)(i) 

+ c18(GMS score 3 years prior)(i) 

+ c19(GMS score 4 years prior)(i) 

+ e(i,t) 

Models in the form of equation 1 had previously been tested and were found to be

fairly accurate failure-prediction models.23 Results from this model show that the lower a

banks GMS score in the most recent period, the higher the probability of failure. That result is consistent with those found earlier. Moreover, the relationship between the most recent GMS score and failure was statistically significant for four of the five estimations. The

results for lagged GMS scores were not as consistent, however. The expectation had been

that high lagged GMS scores would be positively related to failures, as above, but in fact

lagged GMS scores were sometimesbut not consistentlysignificantly and positively related to the likelihood of failure. 
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502 History of the EightiesLessons for the Future

24 See UBPR peer definitions in appendix B, Table 13-A.6.

Proposals for Improvement

Several proposals have been made to refine GMS. These are described below.

Distinguishing types of growth. GMS does not distinguish between two important

types of bank growthincreases in assets through existing offices (internal growth) and

growth in assets through mergers, acquisitions, and consolidations (external growth). It

may be that the risk profiles of banks are different for the two types of growth. The researchers hypothesized that external growth is less risky than internal growth. Internal

growth may require more new business for the bank in terms of customers, markets, and

products/loan types, whereas mergers and consolidations are more likely to involve the acquisition of seasoned loans from target banks. Moreover, because of regulatory limits on

geographic expansion of banks, many mergers occur between former competitors in a single market. Rapid growth in new business or unseasoned loans may be considered risky

for several reasons. Information about new customers and new markets may be limited, and

underwriting standards may be loosened as a way to expand business in existing markets. 

To test for the importance of these factors in assessing the riskiness of growth, the researchers used a simplified version of the bank-failure prediction model (equation 1) and

included a control variable for merger-related growth. The variable of interest is a dummy

variable set equal to 1 if the bank was involved in a merger, acquisition, or consolidation

during the quarter its GMS growth score was measured, and zero otherwise. Logit estimations show that growth by mergers was negatively related to the likelihood of failure; however, the coefficients for the merger dummy were usually not statistically significant. This

negative relationship is consistent with the hypothesis about internal versus external

growth. 

Modifying the peer groups. Banksfinancial performance often differs systematically

across industry segments, so some form of peer ranking is needed in GMS. GMS puts banks

into peer groups based upon the UBPR standards. As mentioned above, the 25 UBPR (Uniform Bank Performance Report) peer groups distinguish banks on the basis of asset size, location in metropolitan area, and number of branch offices.24

Some form of asset size grouping would appear to be necessary. For example, small

banks that do not have easy access to direct financial markets for equity often rely on retained earnings for equity funding. This lack of flexibility in equity finance is a reason that

small banks hold large amounts of excess or buffer capital relative to regulatory capital requirements. Large banks, however, do not suffer from financial diseconomies and may rely

on new equity issues for additional capital. For those and other reasons, banks capital-toassets ratios generally decline as asset size increases. However, it is less clear whether lo-
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 503

cation in a metro or nonmetro area and number of branch offices have significance in assessing risk. With the advent of interstate branch banking in 1997, these latter two criteria

would seem to have become particularly irrelevant. 

Several alternative peer-group designations were tested, and the results of risk detection based upon revised GMS score rankings were compared with those based on the original 25 UBPR groups. One of the more promising alternatives tested was peers formed on

the basis of seven geographic regions and two asset-size ranges (assets over and under $1

billion). Preliminary results (not presented here) indicate that rankings of GMS scores using this peer grouping performed marginally better than the 25 UBPR peer groupings in detecting banks likely to have CAMEL rating downgrades. Although the relevant peer groups

depend in large measure upon the ratios used in scoring, it seems likely that a simplified

peer-group structure can be used without any loss in risk detection.

Modifying the ratio weighting structure. As explained above, GMS uses a two-step

weighting system to assign importance to the eight terms used to score banks. Since it

seems unlikely that the importance of any bank activity or growth in detecting risk is stable

over time, a periodic resetting of GMS term weights is necessary. The GMS User Manual

does not state how the present weighting structure was chosen. In this section we present a

means of determining GMS term weights on the basis of the importance of the eight growth

measures in a model forecasting CAMEL rating downgrades. Specifically, the eight GMS

terms were used as explanatory variables in a logit model relating the growth measures to

the incidence of CAMEL rating downgrades occurring three years after growth was measured (see table 13.8).

Table 13.8 

Relationship between GMS Weightings 

and Logit Estimations of CAMEL Downgrades

December 1988

GMS Term Initial Weight Final Weight Model Coefficient

Asset growth .60 0.67 0.0022 ns

Loan growth .60 0 0.0053 *

Loans and sec/assets growth .60 0.11 20.0043 *

Volatile liab/assets growth .60 0.11 20.0016 ns

Equity/assets growth .60 0.11 0.0005 ns

Loans and sec/assets ratio .40 0.11 0.0111 *

Volatile liab/assets ratio .40 0.11 0.0085 *

Equity/assets ratio .40 0.11 0.0063 *

Note: The asterisk denotes significance at the 1 percent confidence level, and ns denotes not significant at the 1 percent

confidence level.
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25 The HHI is a measure of product market concentration and measures concentration of market shares across competitors in

a market. The HHI is defined as the sum of squared market shares for all competitors in a well-defined geographic or product market. High HHI values indicate the concentration of market power (shares) among a few firms, while low HHI values indicate higher levels of competition.

The coefficient estimates from logit estimation of the CAMEL downgrade model

were fairly consistent over time. Comparisons of the GMS weights and logit coefficients

show that whereas GMS placed the greatest weight on asset growth and the least weight on

loan growth, the logit model argued in favor of doing the reverse. Logit estimations indicated that whereas loan growth was significantly related to changes in condition (CAMEL

downgrades), asset growth was not. In addition, the logit estimations argued for placing

much greater emphasis (weight) upon the three ratiosloans and securities to assets,

volatile liabilities to assets, and equity to assetsthan do the present GMS weightings. The

next subsection discusses tests made to see how GMS would be enhanced if the system

were reweighted. It also discusses tests made with the use of additional risk measures.

Adding to the variables. The focus of GMS can be broadened to consider potentially

risky changes in bank loan concentrations or shifts in business activity that may occur with

(or without) growth in total loans or assets. For example, during the 1980s and 1990s the

risks associated with rapid loan growth were often linked to increased portfolio concentrations in risky areas such as commercial real estatea type of growth that is presumably

riskier than growth in safe loan products such as residential mortgages. Yet GMS does not

distinguish between these or other types of growth. To GMS, growth in residential mortgages is no different from growth in unsecured loans or loans with questionable collateral

values. 

Shifts in business activity can be measured with summary measures of loan portfolio

concentration, analogous to the Herfindahl-Hirschman Index (HHI).25 The bank portfolio

concentration index proposed here is a summary measure of loan concentration for an individual bank. First, to measure overall loan concentration one computes the shares of total

loans for several well-defined categories of loans. Next, to form the loan concentration index one squares and sums the portfolio shares. 

Table 13.9 presents two hypothetical cases for Bank A. In Case 1, Bank A replaced

$20 of residential mortgages with construction loans, increasing its loan portfolio concentration in the process. The portfolio concentration index increases from 2,500 to 3,300 (+32

percent) between 1980 and 1981 in Case 1. In Case 2, the bank reduced long-term commercial real estate by $20 and increased construction loans by the same amount, and the

same concentration increase occurs. The portfolio shifts in Case 1 and Case 2, however, are

not equal in terms of overall risk exposures. Most observers would agree that long-term

commercial real estate and construction loans are riskier loan categories than residential
Chapter 13 Off-Site Surveillance Systems

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Table 13.9

Hypothetical Loan Portfolios for Bank A: Loan Shares Not Weighted

1981

1980 Case 1 Case 2

Residential mortgages $25 $ 5 $25

Commercial and industrial 25 25 25

Commercial real estate 25 25 5

Construction and land 

development 25 45 45

Loan concentration index 2,500 3,300 3,300

Percent change in loan concentration (unweighted) +32% +32%

mortgages. Consequently Case 1, where total commercial real estate and construction loan

exposures are higher, should be treated differently from Case 2. The way to distinguish

these cases is by weighting loan portfolio shares, giving greater weight to riskier loan categories. A weighted portfolio concentration index would show greater increases in

(weighted) concentration when overall risk exposures increase. Using the previous example

but giving commercial real estate and construction loans greater weight in the portfolio concentration index, one finds that in Case 1 the overall risk exposure is now greater than in

Case 2 (see table 13.10). 

To test the usefulness of the concentration index, the researchers devised a loan portfolio concentration index by dividing total loans into 15 loan categories and weighting

riskier loan shares more heavily than other loan categories. The loan categories and weights

used are presented in table 13.11.

Table 13.10

Hypothetical Loan Portfolios for Bank A: Loan Shares Weighted

1981

Weight 1980 Case 1 Case 2

Residential mortgages 0% $25 $ 5 $25

Commercial and industrial 100 25 25 25

Commercial real estate 200 25 25 5

Construction and land 

development 200 25 45 45

Loan concentration index 3,125 5,925 4,700

Percent change in loan concentration (weighted) +89.6% +50%
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506 History of the EightiesLessons for the Future

Table 13.11

Loan Portfolio Concentration Index

Weight

Loans Secured by Real Estate

Construction loans 5

Secured by farmland 0

1- to 4-Family residential 0

Multifamily (5+) dwellings 5

Other commercial properties 5

Other real estate loans 0

Loans Not Secured by Real Estate

Loans to banks 0

Agricultural loans 0

Commercial and industrial 1

Acceptances of U.S. banks 0

Consumer loans 1

Loans to foreign govts and orgs. 1

Municipal loans 0

Total leases 0

Unearned income 0

26 Concentration measures based upon income or revenue shares might also be better measures of risk. For example, the proportion of total revenue generated by each loan type might be used to form a concentration index normalized by revenue.

Such measures will be tested in the future.

These weights were chosen somewhat arbitrarily to reflect the riskiness of commercial real estate loans relative to other loan areas. More-precise weights could be based upon

loss experience by loan type.26

The researchers tested the relationships between revised GMS score rankings and

changes in CAMEL ratings three years after scores were computed. The GMS scores were

revised in two ways. First, the portfolio concentration index just described was included as

a scoring variable, as was the percentage change in the portfolio concentration index over

the growth quarter. Second, the percentile ranks of the ten terms in the revised GMS score

(the original eight terms [table 13.8] plus portfolio concentration and growth in portfolio

concentration) were weighted by use of the coefficients obtained from a logit model that related the ten terms to changes in CAMEL ratings, as discussed above. (See table 13.12.) To
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 507

weight the terms, estimated coefficients from 1988 were chosen arbitrarily. Terms that were

not statistically significant were given zero weight.

The analysis indicated that in each year the revised GMS score rankings were improved indicators of the likelihood of future CAMEL rating downgrades. For example, in

December 1984 the proportion of downgraded banks in the highest (original) GMS decile

was 15.96 percent (table 13.7), but with the revised GMS score the proportion increased to

18.45 percent. Similarly, with the revised GMS score the proportion of downgraded banks

in the top two deciles increased from 28.07 percent to 32.4 percent (see table 13.13). In

every year between 1984 and 1995 the highest revised GMS decile contained greater concentrations of downgraded banks than did the highest decile for the original GMS score.

Even greater improvement can be expected to follow from less-arbitrary loan weightings as

well as from adjustments in GMS term weightings over time.

The FDICs Examination Ratings Model (CAEL) 

Identifying factors that might affect bank performance several years in the future is

not the same thing as identifying banks with deteriorating financial condition between examinations so that examination resources can be efficiently targeted at the identified institutions. Identifying the institutions with deteriorating condition has been the general focus

of early-warning systems. And just as in a civil suit a preponderance of the evidence is required to reject the null hypothesis of innocence in favor of guilty, in a surveillance system

for problem-bank detection the equivalent of a preponderance of the evidence is a high

Table 13.12

12/1988 CAMEL Logit

GMS Term Model Coefficient

Portfolio concentration 0.0025*

Growth in portfolio conc. 0.0060*

Asset growth 0.0022 ns

Loan growth 0.0054*

Loans and sec/assets growth 20.0044*

Volatile liab/assets growth 20.0017 ns

Equity/assets growth 0.0005 ns

Loans and sec/assets ratio 0.0111*

Volatile liab/assets ratio 0.0068

Equity/assets ratio 0.0067*

Note: The asterisk denotes significance at the 1 percent confidence

level, and ns denotes not significant at the 1 percent level.
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measured probability that a flagged institution will turn out to be a problem bank (so that

few healthy banks are wrongly flagged as problem banks). Such systems should have a

small Type II error. 

Historical Development

In 1977, after the OCC developed the National Bank Surveillance System (NBSS),

the FDIC introduced the Integrated Monitoring System (IMS). IMS consisted of 12 tests:

a set of Call Report ratios and associated benchmarks performed on commercial banks

whose composite CAMEL ratings at their last examinations were 1 or 2. Institutions failing

these benchmarks were flagged for the analytic attention of regional and field office personnel. Over time, the IMS failed to achieve a small Type II error. For example, in an IMS

Failure Report for the first quarter of 1984, 2,758 institutions (more than 39 percent of the

7,400 institutions scored) were given top-priority flags. Included were 100 of the 168 (60

percent) institutions having assets of $1 billion or more. The inclusion of so many highasset institutions may have occurred for a number of reasons, but probably when the flag

cutoffs were established the system did not take into account the differences in operation

between banks of different sizes.

In response to these problems, the IMS off-site monitoring approach was given a major overhaul. This led to the FDICs CAEL model that, like the IMS, was based solely on

Table 13.13

Comparisons of Exam Ratings as Assigned in 1985 and 1987

Portfolio Concentration Model

(Number and Column Percent)

December

1984 CAMEL CAMEL No CAMEL Missing Because

GMS Decile Upgraded Downgraded Change of Mergers, etc. Total

1 203 16.29% 54 5.06% 384 11.47% 825 9.16% 1,466 9.99%

2 144 11.56 75 7.02 387 11.56 861 9.56 1,467 10.00

3 157 12.60 75 7.02 391 11.68 844 9.37 1,467 10.00

4 140 11.24 75 7.02 337 10.07 915 10.16 1,467 10.00

5 138 11.08 95 8.90 350 10.45 884 9.81 1,467 10.00

6 106 8.51 106 9.93 346 10.33 909 10.09 1,467 10.00

7 111 8.91 122 11.42 351 10.48 883 9.80 1,467 10.00

8 114 9.15 120 11.24 279 8.33 954 10.59 1,467 10.00

9 85 6.82 149 13.95 291 8.69 942 10.46 1,467 10.00

10 48 3.85 197 18.45 232 6.93 990 10.99 1,467 10.00

Total 1,246 100.00% 1,068 100.00% 3,348 100.00% 9,007 100.00% 14,669 100.00%
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 509

27 There are three commercial-bank peer groups based solely on asset size; a fourth group encompasses all FDIC-supervised

savings institutions. 28 During the study period (198794), CAEL encompassed only about 80 percent of all commercial banks. It excluded all

banks with over $1 billion in assets and all banks with a CAMEL rating of 1.

Call Report data. The model was introduced at the end of the December 1985 Call Report

processing period, and it remains the principal tool for the FDICs off-site monitoring system. The CAEL model is named after the first letter of four of the five examination rating

components: capital, asset quality, earnings, and liquidity (the M, management, is not

modeled). CAEL is used to perform a variety of tasks designed to help achieve and maintain efficient allocation of supervisory resources, primarily by early detection of banks that

appear to have a high probability of a rating downgrade. 

Description 

CAEL is an expert system, designed to replicate the financial analysis that an examiner would perform to assign an examination rating. As such, the system is a nonparametric, nonstatistical construct. CAEL is designed to predict examination ratings that would

have been assigned if an institution had been examined as of the date of a Call Report.

CAEL uses 19 financial ratios that are matched within peer groups.27

The FDIC periodically updates the CAEL model by having analysts subjectively determine new weights for each of the relevant CAEL ratio components. In addition, the

CAEL component rating tables are updated each quarter to mirror the proportionate distribution of peer-group examination ratings over the previous year. Through this process, the

CAEL rating distribution always approximates the previous years examination rating distribution. The final component ratings are multiplied by their respective weights and combined to generate a single CAEL composite rating for an institution. CAEL component and

composite ratings range from 0.50 (best) to 5.49 (worst), a range that corresponds to the examination CAMEL rating range of 1 to 5.

BanksCAEL ratings are compared with their most recent composite CAMEL ratings.

If the result is a large predicted downgrade from the current CAMEL rating, the appropriate

Division of Supervision regional office gives those banks increased supervisory attention.

Regional personnel are required to review these lists and, for each institution, determine

whether they agree or disagree with CAELs results. If they agree, appropriate supervisory

follow-up of the subject institution must take place (appropriateness is a function of the

severity of the CAEL rating and the gap between the CAEL rating and the CAMEL rating).

If regional staff disagree with CAELs results, the reason(s) for the disagreement must be

documented in writing and transmitted to the Washington, D.C., office. In volume the CAEL

Off-Site Review List has ranged from 1 percent to 3 percent of the institutions modeled.28 In

addition to following up on the Off-Site Review List, regional staff use CAELto help in such
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510 History of the EightiesLessons for the Future

29 Chain banking organizations refer to banks that are controlled by the same ownership group but are not associated with

a bank holding company. 30 Putnam, Early-Warning Systems.

supervisory activities as scheduling exams, doing preexamination planning, assessing affiliated holding company institutions and chain banking organizations,29 and reviewing riskrelated premium classifications.

Validation

CAEL has been in existence since 1985, but only since 1987 have sufficient examinations been conducted for there to be measurable results. The validation analysis, for the period from 1987 to 1994, focuses on how well the system performs its stated functions,

which are to identify deteriorating banks so that exam resources can be efficiently targeted

and to do so with a high probability that a flagged institution will in fact have deteriorated

(high probability of guilt).

From 1987 to 1994, only 16 percent of banks with composite CAMEL ratings of 2 or

lower experienced a rating downgrade. If there had been no off-site system and the bank

regulators had simply randomly chosen banks for accelerated examinations, 16 percent of

the examinations would have resulted in a rating downgrade. Over the same period, 52 percent of all CAEL-predicted downgrades were actually downgraded within six months. In

other words, CAEL was more than three times better than a random draw at predicting

downgrades. Note that a random draw would result in a Type I error rate of 84 percent (84

percent of institutions examined would not be downgraded), while the CAEL model has a

Type I error rate of 48 percent, a very large improvement.

Another way to analyze CAELs effectiveness is to see what percentage of total downgrades were identified. During the period there were 2,867 downgrades, 715 of whichor

25 percent of the relevant groupCAEL predicted. Although CAEL does not predict

downgrades for CAMEL 1-rated institutions, some of these institutions were downgraded,

and a large number by more than one rating, that is, CAMEL 1 to CAMEL 3. If these downgrades are included, CAEL correctly predicted only 14.3 percent of the total of 3,810 downgrades. By design, CAEL will miss a large number of actual downgrades in order to avoid

targeting banks that are, in fact, in sound condition.

The Federal Reserve Boards

Financial Institutions Monitoring System (FIMS)

During the years when the OCC and the FDIC were developing their first off-site systems, the Federal Reserve Board developed a similar system,30 a system of screens, which

it replaced in the mid-1980s with the Uniform Bank Surveillance System (UBSS), an out-
Chapter 13 Off-Site Surveillance Systems

History of the EightiesLessons for the Future 511

31 Rebel A. Cole, Barbara G. Cornyn, and Jeffery W. Gunther, FIMS: A New Monitoring System for Banking Institutions,

Federal Reserve Bulletin 81 (January 1995): 3. 32 Alst Demirguc-Kurt, Modeling Large Commercial-Bank Failures: A Simultaneous-Equations Analysis, working paper

8905, Federal Reserve Bank of Cleveland, May 1989; Alst Demirguc-Kurt, Deposit-Institution Failures: A Review of the

Empirical Literature, Federal Reserve Bank of Cleveland Economic Review 27 (March 1991); Gregory R. Gajewski, Assessing the Risk of Bank Failure, in Bank Structure and Competition, conference proceedings, Federal Reserve Bank of

Chicago (May 1989), 43256; and Gary Whalen and James Thomson, Using Financial Data to Identify Changes in Bank

Conditions, Federal Reserve Bank of Cleveland Economic Review (quarter 2, 1988): 1726. 33 Cole, Cornyn, and Gunther. FIMS: A New Monitoring System, 115. 34 Ibid.

growth of the OCCs early National Bank Surveillance System (NBSS).31 After the UBSS

and CAEL were developed, a substantial body of economic research focused on modeling

bank failures and financial distress.32 This research indicated that banksfinancial condition

could be successfully modeled with the use of standard Call Report data and that the models would probably use far fewer variables than CAEL. Taking note of the research, in 1993

the FRB replaced the UBSS with the Financial Institutions Monitoring System (FIMS).33

FIMS represented a major advance in surveillance systems by using sophisticated statistical models to predict CAMEL ratings (ordinal-level logit) and probabilities of commercial bank failures (binary-probit). These techniques allow the bank analyst to determine

statistically what bank condition ratios are significant determinants of CAMEL ratings or of

failure, and how important each ratio might be in the model. The techniques also help the

analyst discard ratios that do not have a statistically significant relationship with CAMEL

ratings or bank failures. These models can also be updated (reestimated) as often as four

times a year (when new Call Report data are received) to adapt to changes in examination

standards or in the banking environment.

The FIMS model lends itself to the same type of validation that was performed for

CAEL. The FIMS validation covers the period from December 1989 to March 1992, a much

shorter period than that used for validating the CAEL system.34 Over its validation period

FIMS correctly identified 61 percent of downgrades predicted, with a 39 percent Type II error rate. FIMS also identified 41.2 percent of the total downgrades. For purposes of comparison, a CAEL validation was performed for the same period. CAEL correctly identified

51 percent of downgrades (10 percent below FIMS), with a 49 percent Type II error rate.

CAEL also predicted a smaller percentage of total downgrades than FIMS, 34 percent (41.2

percent for FIMS). Thus, over the same study period, FIMS was more accurate than CAEL.

It should be noted that there are meaningful differences between an algorithmic

model like CAEL and a probabilistic model like FIMS. FIMS estimates the probability

of bank failure or of rating downgrade by using historical trends and relationships. CAEL

is not based on any statistical model and does not require assumptions about standard statistical problems, such as normality in dependent-term distribution. But CAELs nonpara-
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512 History of the EightiesLessons for the Future

metric approach is nonetheless dependent upon historical patterns and is therefore still open

to the same weaknesses as probabilistic models. In addition, the Type I and Type II error

trade-offs are inherent in all forecasts regardless of estimation method. 

After FIMS, the FRB went on to make many improvements to its entire surveillance

system. The bank-failure model described in FIMS is no longer in use; the model was reestimated quarterly using the previous two years data. But as the number of bank failures

dramatically decreased through the 1990s, the model became less and less reliable.35 The

FRB then developed a new failure model: a pooled time series model that uses failures for

the years 1985 through 1993 to estimate failure probabilities. Building on the original FIMS

work, researchers studied a large number of aggregate economic variables (Treasury bond

rates, changes in GDP, etc.) to see if they increase the predictive power of either the

CAMEL ratings model or the failure model. Although some of the variables were statistically significant, none of them improved the accuracy of the out-of-sample predictions of

the models. The FRB also continues to develop and use various financial-ratio screens that

highlight outlier banks. Currently the ratings model is updated quarterly, and the pooled

failed-bank model is updated every two years. All parts of the FRB surveillance system are

produced and distributed within three days of final Call Report data.

The OCCs Surveillance System

The OCCs current off-site surveillance system uses a variety of mainframe and PC applications based on Call Report and UBPR information. Two of the PC systems use artificial

intelligence and expert system technology. One of the two takes the UBPR for each national

and state-chartered bank and produces an English-language report based on expert financial

analysts experience. The other analyzes the interest-rate risk of each bank based on historical changes, and produces an English-language summary of the findings. Summary scores

from both systems provide for trend and systematic analysis across all banks. For national

banks, these reports are produced within a day after quarterly Call Report data are final.

Conclusion

The lessons of the banking crises of the 1980s and the use of off-site monitoring during that period are fairly clear. First, banks that either become financially distressed or fail

apparently exhibit identifiable risk characteristics several years in advance of the distress or

failure. Second, off-site surveillance systems like those now used by the FRB appear to be

reliable and valuable tools when used in conjunction with regular examinations. Finally, ongoing research at each of the federal bank regulatory agencies is warranted. Such research

would include regional economic data in the current off-site monitoring models, thereby

further enhancing our understanding of the causes of financial distress.

35 The model did not have enough failure events for the statistical procedure used to run to a solution.
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Appendix A

The tables in this appendix present a comparison of different factors in predicting bank

failures four and five years into the future for the years 1980, 1982, 1984, 1986, and 1988.

The banks studied consisted of all banks that were in existence on December 31 of the beginning year (for instance 1980) and either failed four or five years later or never failed. Excluded from the analysis were banks that failed before the fourth or fifth year, banks that

were chartered after the beginning period, and banks that failed subsequent to the fifth year.

There is a table for each beginning year of the study, and three groups of banks are

shown within each table. The first part of each table contains data from the entire set of

banks studied for the particular time period. This analysis (described below) identifies the

subset of banks that exhibit the highest risk of bank failure within the universe of banks

studied. The second part of the table then repeats the analysis using only the highest-risk

subset of banks. The third part of the table takes the remaining banks in the universe (those

not identified as the highest risk in the first part of the table) and again repeats the analysis. 

Table 13-A.1 compares the different risk factors as predictors of bank failures four and

five years forward from 1980. The eight financial ratios that were chosen as risk factors for

the analysis are listed across the top of the table. For each ratio, banks are sorted from lowest to highest and divided into five equal portions or quintiles, with banks in quintile 1 supposedly having the lowest risk and those in quintile 5 the highest. By reading across each

quintile, one can identify how many banks failed and how many banks never failed for each

risk factor. For instance, in the first quintile for Loans to Assets for 1980 (the 20 percent of

the banks with the lowest Loans-to-Assets ratio), 20 banks failed either in 1984 or 1985 and

2,415 never failed. The first quintile for Return on Assets (ROA) in 1980 had 39 failed

banks four or five years later and 2,396 banks that never failed. The Total row shows that

184 of the banks in existence in 1980 failed either in 1984 or 1985 and that 11,989 banks in

existence in 1980 never failed. 

Below the Total row are the Chi-Square statistics for the logit regression for each risk

factor. The higher the Chi-Square statistic, the better the risk factor is as a predictor of bank

failures. In 1980 the Loans-to-Assets ratio is the risk factor with the highest Chi-Square:

99.668. The risk factor with the next-highest score is Loan Growth, with a Chi-Square of

88.352.

The last row is labeled Best Grouping and identifies which set of banks within the highrisk ratio is the best predictor of failure. Obviously banks in quintile 5 had the highest failure rate (88 out of 2,434), but it may be that the best predictor of failure was being a bank

in either quintile 4 or quintile 5. To determine which grouping of banks yields the best prediction of failure, four groupings are analyzed: quintile 1 versus quintiles 25, quintiles 12

versus quintiles 35, and so forth. A Chi-Square statistic is calculated on the difference
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between each two groups. The largest Chi-Square indicates the largest difference between

the groups, and being in the group with the highest percentage of failures is the best predictor of failure. For the Loans-to-Assets factor, the smallest difference was between quintile 1 and quintiles 25, with a Chi-Square of 9.74. The largest difference was between the

grouping of quintiles 14 versus quintile 5, with a Chi-Square of 90.4. Of the 20 percent of

the banks in quintile 5, 3.6 percent failed, over three times the failure rate (1 percent) of the

other 80 percent of the banks. Thus, in 1980 a bank with a Loans-to-Assets ratio in the highest 20 percent of all banks was the best predictor of failure in 1984 or 1985. 

In the next part of the table, banks in the highest-risk grouping (that is, quintile 5 of

Loans to Assets) are analyzed to determine if there were additional risk factors that were related significantly to failure. Although the 2,434 highest-risk banks identified so far were all

in the top 20 percent of Loans to Assets, they are not uniformly distributed in the highestrisk quintiles of the other risk factors. For example, the 2,434 institutions (88 failures and

2,346 nonfailures) are spread relatively evenly through the Average Salary quintiles but

very unevenly through the Interest Yield quintiles, where a large percentage of banks were

concentrated in quintiles 4 and 5. Interest Yield would appear to be an excellent predictor of

risk, as 78 of the 88 failures (89 percent) are in the fourth and fifth quintiles. However, a

very large percentage of all of the high-risk banks (65.3) are in those quintiles, so the prediction may not be as good as it first appears. In fact, the Chi-Square for the best predictor

for the Interest Yield is 36.05, the second-highest for the High-Risk Group. The best predictor turned out to be Interest and Fees on Loans and Leases (Chi-Square of 52.22). For

this predictor, the best grouping was between quintiles 13 and quintiles 45. A bank in

both the top 20 percent of Loans to Assets and the top 40 percent of Interest and Fees to

Loans and Leases would have a 7.2 percent probability of failure, twice the rate of being

only in the top quintile of Loans to Assets.

The final part of the table is the risk analysis of the banks that were in the lower 80

percent of Loans to Assets. The same procedure that was used for the high-risk banks was

followed, and the results are shown in the Low-Risk Group section of table 13-A.1. The

variable with the highest Chi-Square statistic (42.61) is Loan Growth. The best grouping

was between quintiles 14 and quintile 5 (Chi-Square of 36.26). In quintile 5, 2.3 percent

of banks failed, versus 0.7 percent of the remaining banks.

To summarize, on December 31, 1980 there were 12,173 banks that either failed in

1984 or 1985 or never failed. Of that group, 184 failed four or five years later. Of the risk

factors studied, the banks with the highest probability of failure were those in the highest

quintile for Loans to Assets and in the highest 40 percent of banks for Interest and Fees on

Loans and Leases. For the 80 percent of banks that were not in the High-Risk Group, being

in the highest Loan Growth quintile was the best predictor of failure.
Table 13-A.1

Comparison of Different Factors in Predicting Bank Failures Four and Five Years 

Loans to Asset Operating Interest Average In

Assets ROA Growth Expenses Yield Salary 

Quintile Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Faile

All Banks

1 20 2,415 39 2,396 36 2,398 25 2,409 23 2,412 45 2,390 34 

2 13 2,421 31 2,404 16 2,419 20 2,414 17 2,418 19 2,416 14 

3 25 2,410 24 2,410 32 2,403 36 2,399 18 2,416 38 2,397 30 

4 38 2,397 36 2,399 34 2,400 36 2,399 44 2,390 30 2,403 52 

5 88 2,346* 54 2,380 66 2,369 67 2,368 82 2,353 52 2,383 54 

Total 184 11,989 184 11,989 184 11,989 184 11,989 184 11,989 184 11,989 184 

Chi-Square 99.668 13.769 36.323 36.803 83.604 18.274 3Best Grouping (1 vs 25) 9.740 (12 vs 35) 37.895 (13 vs 45) 63.140 

High-Risk Group

1 0 0 28 356 20 557 11 449 3 135 16 435 7 

2 0 0 15 374 9 560 11 401 4 279 9 490 5 

3 0 0 11 462 14 476 14 452 3 438 17 488 18 

4 0 0 16 486 18 410 18 497 19 620 13 478 30 

5 88 2,346 18 668 27 343 34 547 59 874 33 455 28 

Total 88 2,346 88 2,346 88 2,346 88 2,346 88 2,346 88 2,346 88 

Chi-Square 19.415 22.420 11.896 36.057 19.984 5

Best Grouping (1 vs 25) 17.189 (12 vs 35) 42.167 (13 vs 45) 44.473 

Low-Risk Group

1 20 2,415 11 2,040 16 1,841 14 1,960 20 2,277 29 1,955 27 

2 13 2,421 16 2,030 7 1,859 9 2,013 13 2,139 10 1,926 9 

3 25 2,410 13 1,948 18 1,927 22 1,947 15 1,978 21 1,909 12 

4 38 2,397 20 1,913 16 1,990 18 1,902 25 1,770 17 1,925 22 

5 0 0 36 1,712 39 2,026 33 1,821 23 1,479 19 1,928 26 

Total 96 9,643 96 9,643 96 9,643 96 9,643 96 9,643 96 9,643 96 

Chi-Square 27.905 25.469 19.978 12.260 9.423 1

Best Grouping (1 vs 25) 1.146 (12 vs 35) 13.71 (13 vs 45) 23.62 *Quintile that is best predictor of failure.

History of the EightiesLessons for the Future 515
516 History of the EightiesLessons for the Future Table 13-A.2 Comparison of Different Factors in Predicting Bank Failures Four and Five Years F

Loans to Asset Operating Interest Average Int 

Assets ROA Growth Expenses Yield Salary t

Quintile Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Faile

All Banks 1 8 2,363 54 2,318 63 2,306 37 2,333 24 2,345 54 2,317 29 

2 25 2,347 55 2,316 47 2,325 49 2,322 20 2,353 45 2,326 27 

3 36 2,333 50 2,323 40 2,331 45 2,326 33 2,338 45 2,328 52 

4 62 2,311 53 2,319 56 2,317 62 2,311 58 2,314 63 2,307 76 

5 160 2,212* 79 2,290 85 2,287 98 2,274 156 2,216 84 2,288 107 

Total 291 11,566 291 11,566 291 11,566 291 11,566 291 11,566 291 11,566 291 

Chi-Square 255.143 9.838 21.183 40.572 225.857 18.58 8

Best Grouping (1 vs 25) 55.472 (12 vs 35) 102.109 (13 vs 45) 163.495 

High-Risk Group 1 0 0 31 359 33 582 18 381 7 161 26 391 9 

2 0 0 29 369 26 445 21 333 7 275 25 439 18 

3 0 0 32 391 19 398 28 393 13 376 24 501 30 

4 0 0 24 395 39 352 42 460 30 542 39 436 50 

5 160 2,212 44 698 43 435 51 645 103 858 46 445 53 

Total 160 2,212 160 2,212 160 2,212 160 2,212 160 2,212 160 2,212 160 

Chi-Square 3.009 16.491 6.016 43.245 12.469 6

Best Grouping (1 vs 25) 27.947 (12 vs 35) 46.228 (13 vs 45) 52.778 

Low-Risk Group 1 8 2,363 23 1,724 30 1,724 19 1,952 17 2,184 28 1926 20 

2 25 2,347 26 1,880 21 1,880 28 1,989 13 2,078 20 1887 9 

3 36 2,333 18 1,933 21 1,933 17 1,933 20 1,962 21 1827 22 

4 62 2,311 29 1,965 17 1,965 20 1,851 28 1,772 24 1871 26 

5 0 0 35 1,852 42 1,852 47 1,629 53 1,358* 38 1843 54 

Total 131 9,354 131 9,354 131 9,354 131 9,354 131 9,354 131 9354 131 

Chi-Square 11.009 17.524 32.494 75.695 8.221 3

Best Grouping (1 vs 25) 7.7978 (12 vs 35) 26.783 (13 vs 45) 46.436 *Quintile that is best predictor of failure.
History of the EightiesLessons for the Future 517 Table 13-A.3 Comparison of Different Factors in Predicting Bank Failures Four and Five Years 

Loans to Asset Operating Interest Average In

Assets ROA Growth Expenses Yield Salary 

Quintile Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Faile

All Banks 1 11 2287 44 2,255 90 2,200 34 2,260 42 2,254 45 2,250 61 

2 22 2276 34 2,263 36 2,260 33 2,264 34 2,263 55 2,241 53 

3 47 2249 42 2,257 35 2,264 60 2,239 47 2,251 62 2,233 58 

4 64 2231 75 2,222 39 2,258 88 2,209 71 2,222 84 2,210 66 

5 188 2104* 137 2,150 132 2,165 117 2,175 138 2,157 86 2,213 94 

Total 332 11147 332 11,147 332 11,147 332 11,147 332 11,147 332 11,147 332 

Chi-Square 314.339 112.728 116.781 81.377 111.339 20.134 1

Best Grouping (1 vs 25) 59.593 (12 vs 35) 129.011 (13 vs 45) 184.104 

High-Risk Group 1 0 0 26 298 42 376 16 302 21 230 24 405 34 

2 0 0 19 351 14 317 20 323 13 249 31 441 33 

3 0 0 25 417 17 369 35 366 23 324 35 401 38 

4 0 0 46 466 22 399 49 476 47 480 45 428 42 

5 188 2,104 72 572 93 643* 68 637 84 821 53 429 41 

Total 188 2,104 188 2,104 188 2,104 188 2,104 188 2,104 188 2,104 188 

Chi-Square 16.442 40.392 9.797 6.564 11.643 

Best Grouping (1 vs 25) 2.312 (12 vs 35) 0.778 (1-3 vs 45) 9.363 

Low-Risk Group 1 11 2,287 18 1,957 48 1,824 18 1,958 21 2,024 21 1,845 27 

2 22 2,276 15 1,912 22 1,943 13 1,941 21 2,014 24 1,800 20 

3 47 2,249 17 1,840 18 1,895 25 1,873 24 1,927 27 1,832 20 

4 64 2,231 29 1,756 17 1,859 39 1,733 24 1,742 39 1,782 24 

5 0 0 65 1,578* 39 1,522 49 1,538 54 1,336 33 1,784 53 

Total 144 9,043 144 9,043 144 9,043 144 9,043 144 9,043 144 9,043 144 

Chi-Square 79.204 33.559 44.975 57.976 7.892 2

Best Grouping (1 vs 25) 73.962 (12 vs 35) 22.899 (13 vs 4-5) 48.909 *Quintile that is best predictor of failure.
518 History of the EightiesLessons for the Future Table 13-A.4 Comparison of Different Factors in Predicting Bank Failures Four and Five Years F

Loans to Asset Operating Interest Average In

Assets ROA Growth Expenses Yield Salary 

Quintile Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Faile

All Banks 1 7 2,247 18 2,237 66 2,171 32 2,220 53 2,195 40 2,208 72 

2 14 2,239 25 2,229 31 2,219 25 2,228 28 2,222 33 2,216 50 

3 26 2,221 29 2,225 27 2,225 29 2,219 38 2,216 55 2,191 43 

4 61 2,187 50 2,202 28 2,225 47 2,204 46 2,204 43 2,204 45 

5 145 2,099* 131 2,100 101 2,153 120 2,122 88 2,156 82 2,174 43 

Total 253 10,993 253 10,993 253 10,993 253 10,993 253 10,993 253 10,993 253 

Chi-Square 261.134 177.554 85.494 128.176 42.67 29.761 1Best Grouping (1 vs 25) 48.202 (12 vs 35) 108.822 (13 vs 45) 185.640 

High-Risk Group 1 0 0 10 311 38 360 16 299 22 305 21 421 50 

2 0 0 18 409 15 273 14 295 11 306 21 417 30 

3 0 0 18 440 15 296 21 374 30 318 29 450 24 

4 0 0 30 433 14 470 26 470 26 410 23 402 23 

5 145 2,099 69 506* 63 700 68 661 56 760 51 409 18 

Total 145 2,099 145 2,099 145 2,099 145 2,099 145 2,099 145 2,099 145 

Chi-Square 43.549 22.670 14.888 7.813 21.342 1

Best Grouping (1 vs 25) 6.940 (12 vs 35) 13.717 (13 vs 45) 30.233 

Low-Risk Group 1 7 2247 8 1,926 28 1,811 16 1,921 31 1,890 19 1,787 22 

2 14 2239 7 1,820 16 1,946 11 1,933 17 1,916 12 1,799 20 3 26 2221 11 1,785 12 1,929 8 1,845 8 1,898 26 1,741 19 

4 61 2187 20 1,769 14 1,755 21 1,734 20 1,794 20 1,802 22 

5 0 0 62 1,594* 38 1,453 52 1,461 32 1,396 31 1,765 25 

Total 108 8894 108 8,894 108 8,894 108 8,894 108 8,894 108 8,894 108 

Chi-Square 116.120 34.973 81.976 27.438 10.198 1

Best Grouping (1 vs 25) 12.841 (12 vs 35) 34.958 (13 vs 45) 65.614 *Quintile that is best predictor of failure.
History of the EightiesLessons for the Future 519 Table 13-A.5 Comparison of Different Factors in Predicting Bank Failures Four and Five Years 

Loans to Asset Operating Interest Average In

Assets ROA Growth Expenses Yield Salary 

Quintile Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Failed Nonfailed Faile

All Banks 1 6 2135 16 2,127 31 2,109 30 2,111 19 2,123 18 2,118 31 

2 8 2134 13 2,129 9 2,133 12 2,129 14 2,128 14 2,124 15 

3 17 2123 21 2,121 16 2,125 17 2,125 14 2,127 16 2,122 27 

4 30 2111 18 2,123 12 2,130 21 2,120 26 2,115 27 2,112 25 

5 72 2071* 65 2,074 65 2,077 53 2,089 60 2,081 58 2,098 35 

Total 133 10574 133 10,574 133 10,574 133 10,574 133 10,574 133 10,574 133 

Chi-Square 111.600 71.628 81.030 39.765 56.791 49.897 8Best Grouping (1 vs 25) 20.186 (12 vs 35) 48.751 (13 vs 45) 75.492 

High-Risk Group 1 0 0 8 396 14 203 22 412 11 162 7 430 18 

2 0 0 10 408 4 271 7 341 6 229 7 405 11 3 0 0 15 394 7 350 10 358 6 350 7 405 15 

4 0 0 9 414 8 528 10 437 14 527 13 377 10 

5 72 2,071 30 459 39 719 23 523 35 803 38 454* 18 

Total 72 2,071 72 2,071 72 2,071 72 2,071 72 2,071 72 2,071 72 

Chi-Square 17.269 24.812 9.279 11.051 40.015 7

Best Grouping (1 vs 25) 5.225 (12 vs 35) 12.674 (13 vs 45) 27.092 

Low-Risk Group 1 6 2,135 8 1,731 17 1,906 8 1,699 8 1,961 11 1,688 13 

2 8 2,134 3 1,721 5 1,862 5 1,788 8 1,899 7 1,719 4 

3 17 2,123 6 1,727 9 1,775 7 1,767 8 1,777 9 1,717 12 

4 30 2,111 9 1,709 4 1,602 11 1,683 12 1,588 14 1,735 15 5 0 0 35 1,615* 26 1,358 30 1,566 25 1,278 20 1,644 17 

Total 61 8,503 61 8,503 61 8,503 61 8,503 61 8,503 61 8,503 61 

Chi-Square 59.088 38.601 39.578 33.524 9.119 6

Best Grouping (1 vs 25) 1.963 (12 vs 35) 12.803 (13 vs 45) 27.707 *Quintile that is best predictor of failure.
520 History of the EightiesLessons for the Future Appendix B Table 13-A.6 UBPR Peer-Group Characteristics Peer Group Average Assets for Latest Quarter Number of Banking Offices Lo

1 In excess of $10 billion   2 Between $3 billion and $10 billion   3 Between $1 billion and $3 billion   4 Between $500 million and $1 billion   5 Between $300 million and $500 million 3 or more  6 Between $300 million and $500 million 2 or fewer  7 Between $100 million and $300 million 3 or more Metropo

8 Between $100 million and $300 million 3 or more Nonmetr

9 Between $100 million and $300 million 2 or fewer Metropo

10 Between $100 million and $300 million 2 or fewer Nonmetr11 Between $50 million and $100 million 3 or more Metropo12 Between $50 million and $100 million 3 or more Nonmetr13 Between $50 million and $100 million 2 or fewer Metropo14 Between $50 million and $100 million 2 or fewer Nonmetr15 Between $25 million and $50 million 2 or more Metropo16 Between $25 million and $50 million 2 or more Nonmetr17 Between $25 million and $50 million 1 Metropo18 Between $25 million and $50 million 1 Nonmetr19 Between $10 million and $25 million 2 or more Metropo20 Between $10 million and $25 million 2 or more Nonmetr21 Between $10 million and $25 million 1 Metropo22 Between $10 million and $25 million 1 Nonmetr23 Less than or equal to $10 million  Metropo24 Less than or equal to $10 million  Nonmetr

25 Were established within the last three years, and have assets less than or equal to $25 million