Banking Review 

2005 VOLUME 17, NO. 1 

A Survey of Current and Potential Uses of Market 

Data by the FDIC (page 1) 

by Steven Burton and Gary Seale 

Is market information useful to regulators? This paper discusses how supervisors of banks 

and thrifts currently use market information. It then explores possible applications of 

market information for FDIC activities, such as deposit insurance pricing and management 

of the insurance funds. 

Limited-Purpose Banks: Their Specialties, 

Performance, and Prospects (page 19) 

by Chiwon Yom 

Although credit card bank, subprime lenders, and internet primary banks make up only a 

small part of the financial services industry, their unique characteristics and distinctive 

business models have attracted considerable attention. This paper examines the special 

characteristics, performance, and future prospects of these limited-purpose banks. 

The views expressed are those of the authors and do not necessarily reflect official positions of the Federal Deposit Insurance 

Corporation. Articles may be reprinted or abstracted if the FDIC Banking Review and author(s) are credited. Please provide the 

FDIC’s Division of Insurance and Research with a copy of any publications containing reprinted material. 

Chairman Donald E. Powell 

Director, Division of Insurance Arthur J. Murton 

and Research 

Deputy Director Fred Carns 

Managing Editors Jack Reidhill 

Editorial Committee Christine E. Blair 

Valentine V. Craig 

Rose M. Kushmeider 

Publication Manager Geri Bonebrake 
A Survey of Current and Potential Uses of 

Market Data by the FDIC 

Steven Burton and Gary A. Seale* 

Our examiners are extremely good at what they do, 

but any good examiner recognizes that data should 

come from a variety of different sources, including the 

signals that come from the market. Therefore, market 

discipline can be an important adjunct to the supervisory process.—Roger W. Ferguson, Jr., Vice Chairman, Board of Governors of the Federal Reserve 

System 

I propose that a formal integration of selected market 

data into the regulatory agencies’ analytical systems 

could substantially improve the quality of the oversight 

they can provide.—Mark J. Flannery, Barnett 

Banks Professor of Finance, University of Florida 

Market data play an increasingly important role 

in the ongoing monitoring of insured institutions’ 

risks. In the eyes of the supervisory community, 

the essence of this role is captured by the two 

statements quoted above. First, supervisory 

processes benefit from consideration of a broad 

range of different sources of information, including objective signals offered by market participants. Second, the integration of market data 

* Both authors are in the Division of Insurance and Research at the Federal 

Deposit Insurance Corporation. Steven Burton is a senior financial analyst 

and Gary A. Seale is a financial economist. 

into off-site monitoring tools and models can 

improve supervisors’ responsiveness to emerging 

risks. The FDIC is also considering the possible 

benefits of integrating market data into insurance 

pricing and failure loss-prediction models. 

This article illustrates various ways in which the 

supervisors of depository institutions currently use 

market information; the article also highlights 

some potential applications of market data that 

the FDIC is considering in its insurance functions. The first section reviews the literature on 

the application of market data to supervisory risk 

assessments. The second section briefly reviews 

the supervisory process, setting the context for 

the current use of market information within that 

process. The third section illustrates how market 

information is currently applied in assessments of 

both industry risk trends and institution-specific 

risk conditions. The fourth section discusses 

research and other activities being conducted at 

the FDIC with a view to using market information more broadly. The final section summarizes 

and discusses a few of the challenges for wider 

incorporation of market information into the 

supervisory process. 

FDIC BANKING REVIEW 1 2005, VOLUME 17, NO. 1 
Current and Potential Uses of Market Data 

Market Data and the Literature on Links 

between Market Signals and Supervisory Risk 

Assessments 

The term “market discipline” assumes that the 

information provided by markets can signal that 

excessive risk levels are present in banks. From a 

public–policy standpoint, supervisors’ use of such 

signals is highly desirable. Market discipline has 

the potential to reduce the extent and frequency 

of burdensome regulatory oversight; and—because 

market signals call immediate attention to potential excessive risk taking—it allows regulators to 

take more timely corrective action. The inclusion of market discipline as Pillar III of the new 

Basel Capital Accord (Basel II) proposal underscores the important role regulators foresee market forces playing in encouraging banks to have 

adequate levels of capital. 

The market information presently available for 

publicly traded insured depositories is of three 

kinds:1 equity information (prices and trading 

volumes), debt information (debt ratings and subordinated debt prices), and analysts’ reports (see 

table 1). 

Data on daily and even intraday equity prices and 

trading volumes are widely available for U.S. public companies. As table 2 shows, just over onehalf of the 1,002 publicly held U.S. banking and 

Table 1 

thrift holding companies trade on the National 

Association of Securities Dealers Automated 

Quote System (NASDAQ). However, the largest 

banking organizations trade on the New York 

Stock Exchange (NYSE). Equity pricing information is also readily available for a number of large 

foreign banking organizations that own insured 

banking subsidiaries operating in the United 

States. 

Debt information is less widely available than 

equity information. As of year-end 2003, debt 

ratings from one of the three major rating agencies2 were available for 133 bank and thrift holding companies with roughly $6.4 trillion in 

insured assets. Subordinated debt prices, which 

have received a great deal of attention in recent 

academic research, are available for roughly 50 of 

the largest bank and thrift organizations with over 

$5 trillion in insured depository assets. Only 

about 30 of these companies have issues that are 

actively traded. 

The third kind of market information that is 

available is provided by the analyst community, 

1 Publicly traded insured depositories make up a relatively small percentage of 

all insured entities, yet as of September 30, 2004, they held over 85 percent 

of all the assets held by insured institutions. 

2Moody’s, Standard & Poor’s, and Fitch. 

Insured Subsidiaries of Publicly Traded U.S. and 

Foreign-based Companies by Primary Regulator 

Percentage of 

Number of Percentage of Assets All Insured 

Insured All Insured ($ B) Depository 

Institutionsa Depositories 9/30/04 Assets 

Comptroller of the Currency (OCC) 524 5.7 $4,580 46.3 

Federal Reserve (FR) 274 3.0 1,792 18.1 

Federal Deposit Insurance Corp. (FDIC) 828 9.1 1,109 11.2 

Office of Thrift Supervision (OTS) 227 2.5 999 10.1 

Total 1,853 20.3 $8,480 85.7 

Source: FDIC. 

a Includes stand-alone entities and subsidiaries of publicly traded bank and thrift companies. Excluded are insured 

institutions owned by industrial (nonfinancial) corporations (for example, Monogram Credit Card Bank, which is owned 

by General Electric). 

2005, VOLUME 17, NO. 1 2 FDIC BANKING REVIEW 
Current and Potential Uses of Market Data 

which widely monitors the performance of the 

largest 50 or so U.S. banking companies. Equity 

and bond analysts make investment recommendations and often prepare comprehensive analytical 

reports on the companies they follow. These recommendations and reports can be useful as confirmation of supervisory assessments of an 

institution’s risk profile. Table 3 shows the 

breadth of analysts’ coverage for equities of the 10 

largest U.S. banking and thrift organizations. 

Some people within the supervisory community 

have expressed doubts about the usefulness of 

these three kinds of market information. Much of 

the reluctance about using market information 

more regularly seems to center on doubts about 

whether these sources of market information can 

provide consistent, timely, and reliable indications of risk. In particular, the question is 

whether financial markets provide regulators with 

any information they do not already possess. 

Another way of asking this question is, “Can market participants detect deteriorating conditions in 

an institution before the institution’s supervisory 

rating deteriorates?” 

A number of studies have examined the extent to 

which equity holders and creditors are able to 

anticipate changes in the supervisory profile of 

regulated financial institutions. These studies 

generally incorporate one or more market-based 

measures into statistical models, which then 

Table 2 

Publicly Traded U.S. Banking and 

Thrift Companies: Exchanges, Number of 

Organizations, and Insured Subsidiary Assets 

Assets of 

Insured 

Number Subsidiaries 

of ($ B) 

Exchange Companies 9/30/04 

New York Stock Exchange (NYSE) 107 $6,431 

NASDAQ 507 1,095 

Other Over the Counter (OTC) 307 143 

American Stock Exchange (AMEX) 39 33 

Total 1,002 $7,752 

Source: SNL Datasource 

attempt to forecast supervisory ratings. For example, Gunther, Levonian, and Moore (2001) examined the ability of equity data to predict changes 

in the BOPEC ratings of bank holding 

companies.3 Using Moody’s KMV Corporation’s 

estimated default frequencies (EDFs), Gunther et 

al. concluded that equity prices provide incremental information to bank supervisors in periods 

between inspections. Hall et al. (2001), using 

separate equity measures, found similar results. 

Elmer and Fissel (2001) as well as Curry , Elmer, 

and Fissel (2001) related equity market variables 

directly to models of both CAMELS downgrades 

and bank failures.4 Their findings strengthen the 

argument that equity market variables add 

explanatory value to supervisory models. 

Similar studies have been performed using data 

from holders of bank debt. Gilbert , Meyer, and 

3 BOPEC is the acronym for the bank holding-company rating, assigned by the 

Federal Reserve Board, and stands for Banking subsidiaries, Other (nonbanking) subsidiaries, Parent company, consolidated Earnings, and Consolidated 

capital. A rating from 1 to 5 is assigned for each component, with 1 being 

the best and 5 being the worst. A composite rating from 1 to 5 is also 

assigned, reflecting the overall condition of the organization. 

4 The CAMELS rating is assigned by a bank’s primary regulator. The acronym 

stands for Capital, Assets, Management, Earnings, Liquidity, and Sensitivity to 

market risk. A rating from 1 (the best) to 5 (the worst) is assigned for each 

of these component elements, and an overall composite rating based on the 

component ratings is then assigned to the bank. 

Table 3 

Analyst Coverage of Major U.S. Banking and 

Thrift Companies 

Number 

of 

Company Analystsa 

J.P Morgan Chase 15 

Bank of America 21 

Citigroup 20 

Wells Fargo 23 

Wachovia 21 

Washington Mutual 15 

U.S. Bancorp 20 

National City 15 

SunTrust 16 

BB&T 15 

Source: Yahoo Finance. 

a Number of major brokerage firms providing buy, sell, hold recommendations on 

companies' equity. 

FDIC BANKING REVIEW 3 2005, VOLUME 17, NO. 1 
Current and Potential Uses of Market Data 

Vaughn (2001) found that risk premia on jumbo 

CDs do not predict CAMELS downgrades as well 

as early-warning models do. On the other hand, 

Evanoff and Wall (2001) examined the degree to 

which subordinated debt spreads provide supervisors with additional information. They found 

that subordinated debt spreads do at least as well 

as capital ratios in explaining changes in supervisory ratings. 

With support mounting for supervisors to use 

market discipline, Feldman and Levonian (2001) 

examined supervisory uses of market information 

and the reasons such data are not used more 

often. They point to several factors inhibiting 

the use of market data, including difficulty measuring market signals and the lack of specific direction from senior supervisory staff for using market 

data. They urge that multiple sources of market 

data be incorporated into three areas of the supervisory process: as an additional measure to augment supervisory risk assessments, as an element 

of statistical models used to forecast the future 

condition of banks, and as a measure to help 

assess banks’ loan quality and capital adequacy. 

Further, they advocated a combination of changes 

to supervisory policies and additional applied 

research as the next step toward putting market 

data to practical use. 

More recently, studies by Krainer and Lopez 

(2003) and Curry, Elmer, and Fissel (2003) further strengthen the case that market variables 

improve predictions of changes in supervisory ratings. Krainer and Lopez examined whether both 

equity and debt variables are significant in 

explaining BOPEC rating assignments, even after 

a large number of supervisory variables have been 

included in statistical models. They concluded 

that supervisors could benefit from incorporating 

market variables into their off-site monitoring 

models. Curry, Fissel, and Hanweck (2003) 

investigated the direction of causality, from 

changes in equity variables to changes in BOPEC 

ratings and the reverse. They find that, while 

market variables add value in predicting BOPEC 

rating changes, the reverse is only moderately successful, indicating that market variables may be 

more predictive of BOPEC rating changes than 

vice versa. They conclude that the market is able 

to obtain independent information about bank 

holding company risk exposure beyond the information available from public reporting resources 

and that therefore the market ought to be able to 

provide some degree of independent oversight. 

However, it should be noted that although both 

of these studies include in-sample and out-of-sample tests, results tend to be much weaker for outof-sample prediction. 

The Context for Supervisory Use of Market 

Information 

Ideally, financial markets would provide continuous monitoring of bank performance in the periods between on-site examinations. Although 

on-site examinations allow the most extensive 

review of a bank’s financial position, the information obtained during the examination becomes 

outdated over time, especially for rapidly growing 

institutions. However, market investors evaluate 

bank performance continually, even if they do not 

have access to as much detailed information as 

on-site examiners. Consequently, market signals 

could be effective in alerting supervisory agencies 

to a change in a bank’s risk profile, and the 

change might in turn prompt a supervisory 

response from the primary regulator.5 A supervisory response necessarily involves the reallocation 

of supervisory resources since it entails a shift in 

the current supervisory strategy. 

Market information and market signals rarely in 

and of themselves influence the priorities and 

strategies of supervisors of U.S. financial institutions. Rather, when supervisors are evaluating 

risk trends, they consider market data in the context of a number of different sources of information. In other words, market indicators are just 

one of many considerations that affect strategic 

decisions in response to perceived risk and emerg5 The term “market signal” is used to indicate when a change in investor sentiment about a company’s prospects and risk profile is significant enough to 

produce a substantive change in a given market indicator. 

2005, VOLUME 17, NO. 1 4 FDIC BANKING REVIEW 
No Change in More Frequent Acceleration or

Current and Potential Uses of Market Data 

ing risk trends. Figure 1 is a stylized representation of the role that market indicators might play 

in influencing supervisory responses to risk. 

Aside from influencing supervisory responses (and 

therefore, possibly, the reallocation of supervisory 

resources), market data are also routinely considered in supervisory risk determinations. All 

examination activities and all the information 

and trends analyzed through these activities are 

used to support supervisory risk determinations, 

but these determinations do not directly involve a 

reallocation of resources. Rather, they involve 

the assignment of institutions to certain risk categories for monitoring purposes. Supervisory risk 

determinations are commonly summarized by the 

assignment of numeric or alphanumeric risk 

grades to individual institutions.6 These determinations are critical for purposes of strategic and 

resource planning. For the FDIC, supervisory risk 

determinations are also one of the main factors 

influencing the level of deposit insurance premiums that insured institutions pay.7 This subject is 

discussed below in the section “Potential Uses of 

Market Data.” 

Figure 1 

It is hard to generalize about the importance of 

market indicators in relation to other sources of 

information when supervisory risk determinations 

are prepared. The difficulty stems partly from the 

fact that risk surveillance systems are fundamentally judgment-based processes. In evaluating 

market signals, for example, FDIC examiners and 

analysts do not apply a formulaic approach. 

Rather, they use their best judgment in determining what market data to consider and how to 

interpret and respond to the information. It is 

probably fair to say that the examiners and analysts responsible for preparing supervisory risk 

determinations do not view market information 

as a substitute for other sources of information. 

Rather, they tend to view market data as a supplemental source of information that helps confirm 

the risk perceptions they formed by looking first 

6 The CAMELS rating is one example of a supervisory risk determination. 

7 The current risk-related premium system is based on a nine-cell pricing 

matrix. Institutions are assigned to cells in this matrix depending on their 

capital levels (the capital subgroup) and their CAMELS ratings (the supervisory subgroup). Deposit insurance reform legislation currently pending before 

Congress would expand the ability of the FDIC to consider other factors, 

including market indicators, when setting insurance fund premiums. 

Negative 

Direction 

Supervisory 

Strategy 

Market Signal 

Positive 

Direction 

Economic 

Indicators 

Other Market 

Indicators 

Financial 

Analysis 

Examination 

Information 

Determination of Appropriate Supervisory Response 

Degree of Supervisory Concern 

Low High 

Further 

Evaluation 

Off-site Analysis/ 

Visitations 

No Change in 

Supervisory 

Strategy 

No Change in More Frequent 

Scope Adjustment of 

On-Site Examination 

Acceleration or 

Market Indicators’ Possible Influence on Supervisory Actions 

FDIC BANKING REVIEW 5 2005, VOLUME 17, NO. 1 
Current and Potential Uses of Market Data 

at supervisory information and financial performance measures. 

Before reviewing the current uses of market data, 

we briefly summarize the three broad types of 

U.S. supervisory programs—those for large, midsize, and small institutions—and the role of market data in each. The distinguishing 

characteristics are the depth and scope of on-site 

reviews, the degree of interaction between examiners and management, and the extent to which 

the emphasis is on risk-management information 

systems and controls as opposed to transaction 

testing and asset valuation. Table 4 gives the 

approximate number of institutions and insured 

depository assets covered by each of these three 

kinds of program. The table also distinguishes 

between institutions that are affiliated with a 

publicly traded entity and those that are not. 

Large-Institution Supervisory Programs. Largeinstitution supervision programs are by far the 

most intensive of the three types, subjecting institutions to more frequent and more in-depth onsite reviews and providing supervisors with a vast 

amount of nonpublic risk information more or 

less continuously. The Office of the Comptroller 

of the Currency (OCC), for example, uses teams 

of resident examiners to supervise the 23 largest 

nationally chartered banks. The Federal Reserve 

System uses designated supervisory teams, supplemented by teams of specialists in areas such as 

credit risk modeling and capital market activities, 

to oversee the largest complex banking organizations.8 The FDIC, like the OCC, uses dedicated 

staff in its Large-Bank Program, which encompasses the six largest state-chartered nonmember 

institutions that the FDIC directly supervises.9 

Table 4 

Although the design and structure of large-institution programs vary by primary regulator, all 

have the same goal: to provide real-time and continuous evaluations of the risks posed by large 

institutions. These programs differ from the more 

traditional point-in-time examination process in 

that examiners interact with bank personnel continually throughout the year. Large-institution 

programs also place far greater emphasis on evaluating internal risk-management systems and controls as opposed to performing the transaction 

testing and asset valuations (e.g., loan reviews) 

that take place during more traditional examinations. 

Continuous access to management and to riskmanagement information allows supervisors to 

respond more quickly to emerging problems than 

would be possible with an annual examination 

approach. Because of their ongoing interaction 

with the large institutions, supervisors generally 

learn the nature of negative announcements, 

shifts in risk profile, or shifts in strategic direction 

well in advance of market investors. Table 5 provides a more detailed breakdown of large-institution programs administered by the three federal 

banking agencies in terms of covered insured sub8 These institutions are covered by the Federal Reserve System’s Large Complex Banking Organization (LCBO) program. 

9 Commensurate with its role as insurer and back-up supervisor to nationally 

chartered banks and thrift institutions and state-chartered institutions that are 

members of the Federal Reserve System, the FDIC has established two additional surveillance programs for large banks and thrifts. In each of the two, 

staff coordinate their work with their primary-supervisor counterparts to monitor and independently assess risks in large organizations. One of the two programs is the Dedicated Examiner program, which assigns dedicated examiners 

to monitor the activities of the six largest bank and thrift organizations. The 

other is the Large Insured Depository Institution (LIDI) program, which covers 

all remaining insured organizations with $10 billion or more in assets. 

Distribution of Insured Institutions by Supervisory Program 

Large Midsizea Small 

Public Data Are Available 172 institutions 288 institutions 1,393 institutions 

$5,656 billion in assets $2,077 billion in assets $747 billion in assets 

Public Data Are Not Available 0 institutions 83 institutions 7,190 institutions 

$198 billion in assets $1,215 billion in assets 

a Approximation uses asset size > $5 billion as the criterion, excluding institutions explicitly included in one of the agencies’ large-bank programs. 

2005, VOLUME 17, NO. 1 6 FDIC BANKING REVIEW 
a

Current and Potential Uses of Market Data 

sidiaries and insured subsidiary assets. As this 

table reveals, although large-institution programs 

cover a very small percentage of the number of 

FDIC-insured financial institutions (less than 2 

percent), they cover the majority of insured-institution assets. 

Large-bank examiners are instructed to review all 

available information relevant to the risk classification of the bank, including market information. 

Although market signals for these large institutions are unlikely to convey any new information 

to the supervisor, they are nevertheless useful in 

corroborating and validating perceptions and 

judgments about risk, particularly when disclosures and trends are hard to quantify independently. In the context of large-bank programs, 

market data are also useful as an alternative measure of relative risk. In other words, market data 

provide a measure of the market’s perception of 

this company’s risk relative to the risk of its peers. 

Midsize-Institution Supervisory Programs. 

Thresholds and considerations for placing institutions in a midsize supervisory program vary from 

agency to agency. However, these programs typically include institutions with more than $5 billion in assets. Although less formalized and less 

intensive than large-bank programs, midsize-institution programs are designed to provide for 

reviews of greater depth and frequency than is the 

case with a point-in-time examination approach. 

Table 5 

Examination programs of midsize institutions are 

often tailored to the institutions’ specific risk profiles. For example, institutions engaged in complex banking activities might be subjected to 

periodic targeted reviews throughout the year and 

be assigned dedicated staff with strong technical 

expertise related to the institution’s particular 

activities. For institutions engaged in less complex activities, the supervisory approach might 

resemble the more traditional periodic-examination approach but generally with a much greater 

degree of oversight than is applied to smaller 

institutions. As a result, market investors typically do not learn of negative news about a midsize 

institution before supervisors do. 

In midsize supervisory programs, market information is used in much the same way as in largeinstitution programs—that is, less as a signaling 

device or tool and more as corroboration of risk 

issues and trends and as an alternative measure of 

relative risk. 

Small-Institution or Community-Bank Supervisory Programs. As shown in table 4, the vast 

majority of publicly held insured institutions fall 

within a small-institution supervisory program. 

These programs usually consist of periodic examinations whose scopes vary considerably, depending on the overall risk profile of the institution 

being examined. It is in this area that market signals, used in conjunction with off-site surveilBanking Agencies’ Large Institution Supervisory Programs 

Percentage of 

Insured Percentage of Assets Insured 

Institutions All Insured ($ B) Institution 

Covereda Depositories 9/30/04 Assets Covered 

Comptroller of the Currency (OCC) 103 1.1 $4,767 48.2 

Federal Reserve (FR) 144 1.6 4,984 50.4 

Federal Deposit Insurance Corp. (FDIC) 11 0.1 247 2.5 

Total 172 1.8 $5,656 57.2 

a The OCC and FDIC large-bank supervision programs overlap in many instances with the FR’s LCBO supervision programs. 

However, the number and assets of covered organizations are counted only once in the total. 

FDIC BANKING REVIEW 7 2005, VOLUME 17, NO. 1 
 

 

Current and Potential Uses of Market Data 

lance systems, have the potential to provide the 

most significant benefit to supervisors, given the 

time lag between examinations.10 

Examples of Regulatory Applications of 

Market Data 

Although the FDIC does not apply a formulaic 

approach to evaluating market signals, it does 

incorporate market data into its analytical products, early-warning systems, and decision-making 

processes requiring an assessment of prospective 

risks. The examples presented here relate to offsite monitoring, both of individual banks and of 

industry trends; monitoring for potential liquidity 

pressures in banks; corroborating the importance 

of risk events; developing risk rankings; monitoring credit risk trends in banks’ corporate loan 

portfolios; formulating supervisory outlooks and 

strategies; and influencing decisions about the 

appropriate level of contingent loss reserves for 

potential failures. 

Contributing to the Off-site Monitoring of 

Individual Banks 

The FDIC, along with other U.S. banking supervisors, has developed various off-site monitoring 

programs to supplement on-site examination programs. A primary objective of off-site surveillance systems is to alert supervisors to potential 

emerging risk issues. Market indicators play a significant role in such systems. As an example, the 

FDIC’s LIDI program (see note 9) instructs staff 

to consider all available data on the companies 

being reviewed, including more forward-looking 

information such as market indicators. Off-site 

reviews can influence supervisory strategies in 

variety of ways: for example, the scheduling of an 

on-site examination may be altered or accelerated, the resources allocated to an examination may 

be adjusted, or the planned scope of an on-site 

examination may be changed. 

Another objective of off-site surveillance programs is to identify institutions whose risk profiles 

deviate from expectations. When such outliers 

are identified, examiners or analysts are typically 

required to perform follow-up analyses to determine the reason for the outlier condition and to 

recommend changes in supervisory strategies 

when appropriate. Figure 2 shows how Moody’s 

KMV information might have been used to identify an outlier situation.11 Here, market-based 

default expectations for an insured institution 

began to deviate from those for peer institutions 

beginning in June 2000. In this particular example, the market provided an unambiguous and 

quantifiable signal of financial weaknesses that 

led to the institution’s failure some 21 months 

later. In mid-2000, an analyst would have 

responded to this information by reviewing financial data and supervisory information to try to 

determine the reasons for the negative market signal. Depending on the results of this review, the 

analyst would have either recommended a shift in 

supervisory strategy, such as an accelerated examination, or concluded that the strategy in place 

was sufficient. 

10 Institutions over $250 million are examined at least once a year. For institutions under $250 million, the intervals can be extended to 18 months. 

11 The Moody’s KMV model uses stock prices and financial information to 

derive an expected default probability or expected default frequency (EDFTM) for 

public firms. The model is based on a Merton contingent claims approach, 

where the probability of default is contingent on (1) a firm’s asset market 

value, (2) the volatility of a firm’s asset market values, and (3) the firm’s capital structure or financial leverage. 

Figure 2 

4 1 2 3 4 1 2 3 4 1 

0 

5 

10 

15 

20 

Bank 

Closed 

1999 2000 2001 2002 

EDF for Outlier Bank 

Median EDF for all U.S. Commercial 

Banking Companies 

One-Year EDFTM(%) 

Source: Moody's KMV. 

Identification of an Outlier Situation 

2005, VOLUME 17, NO. 1 8 FDIC BANKING REVIEW 
Current and Potential Uses of Market Data 

Monitoring General Banking Conditions 

and Trends 

Investor sentiment can be a good barometer of 

general risks and conditions in the banking sector. 

The FDIC and other supervisors evaluate this 

sentiment by monitoring banking stock indexes, 

debt spreads for bank debt, bond rating trends, 

debt and equity analyst research opinions, and 

various other market-based measures, such as 

Moody’s Corporation KMV model of expected 

default. Figure 3, for example, uses Moody’s KMV 

model to show the general trend in market 

default expectations for U.S. commercial banks 

since 1996. 

Such broad measures are of particular interest to 

managers because they provide a barometer of the 

current health of and outlook for the industry. 

Used in conjunction with other information, such 

as trends in supervisory ratings and economic 

indicators, market indicators can convey a sense 

of the level of concern that should be factored 

into strategic decisions involving the allocation of 

supervisory resources and contingency planning. 

Figure 4 shows another example of broad industry 

risk measures based on market information. This 

Figure 3 

Expected Default Trends as a Broad Measure 

of Risk in U.S. Commercial Banks 

Median 1-year EDF TM (%)
 

1.0
 

0.8 

0.6 

0.4 

0.2 

12/96 12/97 12/98 12/99 12/00 12/01 12/02 12/03 

Source: Moody's KMV. 

figure depicts a concept recently developed at the 

FDIC and referred to as a dashboard indicator. 

This particular indicator was designed to gauge 

general risk conditions in the universe of large 

insured depositories. Essentially an index compiled from a group of critical market-risk indicators, this indicator helps risk managers gauge the 

current health and outlook of large insured depositories relative to historical patterns. Indicators 

like this one are also important inputs into the 

strategic planning process at the FDIC. 

Monitoring Potential Liquidity Situations 

Sometimes supervisors must respond to changes 

in market indicators because of the liquidity pressures these changes can impose. For example, an 

organization that relies extensively on debt funding may face severe liquidity pressures if its debt 

ratings are downgraded. Many derivatives and 

securitization contracts also contain early termination or collateral clauses that are triggered by 

downgrades in the counterparty’s or issuer’s external debt rating. If a banking organization has a 

significant volume of such contracts, it may be 

unable to generate sufficient funding or collateral 

to meet the provisions of such contracts. As a 

Figure 4 

Q3'93 Q3'94 Q3'95 Q3'96 Q3'97 Q3'98 Q3'99 Q3'00 Q3'01 Q3'02 Q3'03 Q3'04 

0 

10 

20 

30 

40 

50 

60 

70 

80 

13.0 

29.8 

65.1 

Improving Credit Quality 

and Financial Conditions 

Russian Crisis, 

LTCM 

Deteriorating Corporate Credit 

Quality & Venture Capital Losses 

Sharp Stock 

Market Declines 

Significant Improvement 

in Credit Quality 

Index Value 

Components of Index 

Credit Risk 

Equity Market Indicator 

Default Indicator 

Debt Market Indicator 

Key to Index 

Low Value Equals 

Lower Risk 

Use of Market Measures to Develop a Risk 

Index for Large Depositories 

FDIC BANKING REVIEW 9 2005, VOLUME 17, NO. 1 

0 
Current and Potential Uses of Market Data 

result, supervisors closely watch trends in external 

debt ratings as well as other indicators that might 

signal potential contractual performance problems 

for banking companies that have issued debt. 

Corroborating Risks 

Regulators often use market indicators to validate 

or corroborate risks they observe in supervised 

institutions. Market signals can be valuable in 

this respect because they not only provide directional signals but also serve as a quantitative 

benchmark for the significance of certain risk 

events. The stock price performance of large U.S. 

money-center banks in 2002 is perhaps an example of how market measures convey information 

about the magnitude of seemingly unquantifiable 

risks related to corporate governance and reputation risk. Figure 5 shows the stock market’s reaction to a barrage of unfavorable publicity in late 

2001 and early 2002 relating to certain investment banking practices and dealings with customers in connection with high-profile corporate 

failures, including Enron and WorldCom. 

Although the interpretation of such signals is not 

always straightforward,12 the signals do convey a 

sense of the magnitude of events from the marFigure 5 

Negative Reactions of Markets to U.S.
 

Money Center Bank Disclosures in 2002
 

Indexed EOP Stock Price 

(Mar '99 = 100) Heightened scrutiny of 180 

140 

100 

60 

20 

1999 2000 2001 2002 

ket’s perspective. In this case, the market corroborated the seriousness with which the regulatory 

community viewed corporate governance issues 

surrounding larger banking organizations. 

Developing Risk Rankings 

Market data can be used to inform decisions having to do with the relative risks posed by institutions with similar supervisory ratings. Because 

supervisory-based ratings fall within a narrow 

range of possibilities (well-rated companies are 

assigned CAMELS or BOPEC composite ratings 

of 1 or 2), market indicators can help provide 

additional granularity to risk rankings. Such 

rankings can then be used to establish supervisory 

priorities. Figure 6 shows subordinated debt pricing spreads to Treasuries for three large institutions whose supervisory ratings are identical. The 

difference in spreads among the three institutions 

helps corroborate the relative risks posed by these 

companies, and the corroboration in turn supports 

decisions about the allocation of resources. 

12 In this illustration, declining credit quality probably contributed to the declining market valuations. 

Figure 6 

Subordinated Debt Spreads for Three Large 

Institutions with Identical Supervisory Ratings 

Spread* (%)
 

3.0
 

Bank A 2.5 

2.0 

1.5 

1.0 

0.5 

Bank B 

Bank C 

0 

06/28/02 08/23/02 10/18/02 12/13/02 02/07/03 04/04/03 05/30/03 07/25/03 09/19/03 11/14/03 01/09/04 

Source: Interactive Data Corporation. 

* Spreads represent the difference between yields on each institution's subordinated debt and yields on 

* on treasury securities with comparable maturities. 

corporate governance 

practices. 

Company B 

Company A 

SNL Bank Stock Index 

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 

Source: SNL Datasource. 

2005, VOLUME 17, NO. 1 10 FDIC BANKING REVIEW 
Current and Potential Uses of Market Data 

Monitoring Risk in Corporate Credit 

Portfolios 

One of the more significant risks contained on 

the balance sheets of banks is corporate credit 

risk. Among larger banks, much of this exposure 

is related to publicly held companies. Hence, 

each of the supervisory agencies uses market data 

as an early-warning indicator of potential corporate loan performance problems. Figure 7 illustrates how Moody’s KMV information in 1998, 

and even more so in 2000, indicated significant 

deterioration in market-based default measures for 

U.S. telecommunication firms. By associating 

such measures with actual loan exposure data, 

supervisors are able to produce quantitative rankings of industry credit risk exposures, and these 

rankings in turn support decisions about resource 

allocations related to on-site loan review work. 

For instance, in the years 1999–2001 the supervisory agencies used a similar kind of analysis to 

support resource allocation decisions relating to 

the Shared National Credit program—an interagency program that annually reviews large syndicated credits held by three or more supervised 

institutions.13 

Figure 7 

Market-based Measures of Risk in the 

Telecommunications Sector 

One-Year EDFTM (%) 

20 

15 

10 

Median EDF TM for Telecommunications Firms 

5 

0 

Q4'94 Q4'95 Q4'96 Q4'97 Q4'98 Q4'99 Q4'00 Q4'01 Q4'02 Q4'03 

Source: Moody's KMV. 

Influencing Changes in Supervisory Outlook 

As mentioned above, market information can 

contribute to changes in supervisory outlook, and 

these changes, in turn, can cause shifts in priorities and supervisory strategies. Moreover, for the 

FDIC as the deposit insurer, the supervisory outlook for a given institution is often reflected both 

in the level of premiums assessed against insured 

deposits and in the amount of contingent loss 

reserves the Corporation sets aside for problem 

institutions. 

To illustrate the market information the FDIC 

might consider when setting premium levels, figure 8 shows a banking organization that experienced a significant fall in its stock price relative 

to the prices of other large banking organizations 

during the latter half of 1998. Around the same 

time, the FDIC began to have concerns about this 

otherwise well-rated company and took steps to 

downgrade its supervisory subgroup rating for purposes of setting deposit insurance premiums (see 

note 7). In this case, market signals were one of 

many factors that contributed to a change in the 

13 See Burton (2001) for an example of industry risk rankings that use default 

expectations and industry loan exposure data. 

Figure 8 

1/16/98 2/6/98 3/13/98 3/20/98 5/8/98 6/5/98 7/3/98 7/31/98 8/28/98 9/25/98 10/23/9811/20/9812/18/98 

3.5 

4.0 

4.5 

5.0 

Natural Log of Stock Prices 

A Large Banking Company 

Average of Selected Large Banks 

S&P Ratings 

Downgrade 

on 9/24/98 

Sources: Interactive Data Corp, Standard & Poor's. 

Deterioration in Stock Price 

for a Large Bank 

FDIC BANKING REVIEW 11 2005, VOLUME 17, NO. 1 
Current and Potential Uses of Market Data 

FDIC’s overall risk evaluation of the institution. 

The signals reinforced the FDIC’s supervisory outlook for the company, prompting the Corporation 

to act with one of the tools at its disposal—the 

imposition of higher deposit insurance premiums. 

Figure 9 shows a reverse example. In figure 9, 

market signals reinforced the supervisory view 

that a problem institution’s prospects were 

improving. 

Influencing Macro-Level Contingent 

Loss-Reserve Decisions 

The FDIC’s accounting function requires the Corporation to establish loss reserves for potential 

bank and thrift failures. Key factors the Corporation considers when setting these reserves are the 

historical failure rates of problem institutions and 

factors that might suggest some deviation from 

recent failure-rate trends. When the Corporation 

evaluates whether contingent loss-reserve allocations should deviate from historical failure-rate 

patterns, among the factors it considers are market indicators as well as a variety of factors 

including the performance of the economy and 

the capital markets. For example, significant 

deterioration in market indicators related to the 

industry as a whole or to some group of institutions might provide support for increasing the 

Figure 9 

1/4/99 7/6/99 12/30/99 7/3/00 12/26/00 6/25/01 12/19/01 6/17/02 12/16/02 6/16/03 12/9/03 

0 

0.5 

1.0 

1.5 

2.0 

A Large Insured Banking Company 

Philadelphia Bank Stock Index 

Stock Price Changes (1/4/99=1) 

Source: Yahoo Finance 

Gradual Improvement in Market Price for 

Previously Out-of-Favor Company 

reserve allocations for potential failures. Again, a 

shift in market indicators would probably not be 

the sole reason for such an action but could be 

one of several factors influencing the decision. 

Potential Uses of Market Data 

Beyond the applications discussed above, market 

measures have a number of potential applications. 

For the FDIC, some of these relate to the Corporation’s unique role as the insurer of bank and 

thrift deposits. Specifically, market information 

could enhance the following applications or 

processes: 

� Risk classifications for deposit insurance pricing purposes 

� Evaluation of institution-level contingent loss 

reserves for potential bank and thrift failures 

� Off-site surveillance models used to quantify 

the likelihood of downgrades in supervisory 

ratings 

� Basel II benchmarking tools. 

Using Market Data for Insurance Pricing 

In April 2001, the FDIC outlined a number of 

recommendations for deposit insurance reform, 

one of which was to allow the FDIC greater flexibility in setting deposit insurance premiums.14 In 

December 2003, the FDIC Banking Review contained an article that explored alternatives to the 

current risk-based pricing system, including the 

potential use of market indicators for setting 

deposit insurance premiums for large insured 

institutions.15 As noted in that article, the evaluation and pricing of risk related to large complex 

operations may be more precise when market 

indicators complement supervisory ratings than 

when supervisory ratings are used alone. The 

article also noted that market data help overcome 

weaknesses in model-based approaches that rely 

on accounting data: when funding and liquidity 

14 See FDIC (2001).
 

15 Bloecher, Seale, and Vilim (2003).
 

2005, VOLUME 17, NO. 1 12 FDIC BANKING REVIEW 
Current and Potential Uses of Market Data 

variables are included in such models they tend to 

unduly penalize larger institutions. 

Market Variables under Consideration. For purposes of deposit insurance pricing, the FDIC is 

presently considering a variety of market variables 

that best differentiate risk in financial institutions. These variables include stock price volatility measures, external bond ratings, subordinated 

debt spreads, Moody’s KMV measures of expected 

default, and stock price-to-book ratios. As shown 

in the article mentioned just above, these variables appear to be strongly correlated with subsequent downgrades. 

Ways of Incorporating Market Data into a 

Deposit Insurance Pricing System. There are a 

variety of ways in which market information 

could be used in a risk-based premium framework. 

Three implementation possibilities, for illustrative 

purposes only, are described here (figures 10, 11, 

and 12). Figure 10, for example, shows a framework that considers market data in conjunction 

with supervisory ratings to determine an institution’s risk premium category. In this case, market 

information results in a more granular set of risk 

rankings than would be feasible if only supervisory ratings were used.16 

Figure 11 shows an alternative approach that uses 

market data as the basis for adjustments to initial 

Figure 10 

risk assessments that are based on supervisory ratings, a continuous pricing model, or a scorecard.17 

In this example, an institution with favorable 

market indicators (e.g., a strong debt rating or relatively low stock price volatility) would receive 

an adjustment to a lower-premium subgroup. 

In contrast to figure 11, in which market data are 

used to adjust initial assessments, figure 12 shows 

how market data could be used to trigger changes 

to an institution’s risk-based premium subgroup. 

This trip-wire approach would result only in negative adjustments and might involve such occurrences as the lowering of a debt rating to 

subinvestment-grade status or the decline in a 

price-to-book ratio to below 1.0. 

Implementation Issues. If the deposit insurance 

reform proposals pending before Congress are 

enacted (see note 7), incorporating market data 

16 In this example, the intent is to differentiate risk only for institutions that 

would be categorized as well-capitalized and highly rated (that is, 1A institutions) under the current nine-cell risk-based pricing matrix. The rest of the 

matrix, which is reserved for poorly rated and less than well-capitalized institutions, remains the same. As of year-end 2003, 92 percent of insured institutions were categorized as 1A institutions. 

17 Continuous pricing models might use the output from failure-prediction models as the basis for pricing deposit insurance premiums. (Failure-prediction 

models typically rely on accounting information.) The scorecard approach is 

also based on a failure-prediction model but applies expert-based subweightings 

to each variable in the model to produce discrete risk-based premium subgroups for pricing purposes. 

Figure 11 

Risk-Based Premium Pricing Using a 

Combination of Supervisory and 

Market Measures 

Risk Premium Categories 

Lower Risk Higher Risk 

Lower Premium Higher Premium 

CAMELS 1 CAMELS 2 

Rated Rated 

Pricing system based solely 

on supervisory ratings. 

Pricing system using market 

indicators to further 

differentiate risk. 

Less Risky More Risky Less Risky More Risky 

Market View of Risk 

Adjustment Approach Using 

Market Indicator 

Risk Premium Categories 

Lower Risk Higher Risk 

Lower Premium Higher Premium 

Initial risk premium category is 

determined by supervisory rating, X 

model, or scorecard. 

Market indicators serve as the 

basis for adjustments to the X X 

risk premium category. 

Indicator suggests Indicator suggests 

lower risk higher risk 

FDIC BANKING REVIEW 13 2005, VOLUME 17, NO. 1 
Current and Potential Uses of Market Data 

into a new risk-based pricing framework will 

require the resolution of two practical issues. 

First, market data are typically available only for 

consolidated companies, whereas insurance premiums are currently assessed at the insured-subsidiary level. This issue could be overcome if an 

organization-wide view were adopted, at least for 

“significant” subsidiaries—those for which there is 

likely to be a close correspondence among a subsidiary’s performance, its risk indicators, and the 

company’s market signals. For “nonsignificant” 

subsidiaries—those for which performance and 

risk are not linked to market signals—it may be 

more appropriate to apply a general framework 

that does not include market information. 

A second practical issue is the determination of 

what constitutes a large institution. As shown in 

table 4 above, numerous subsidiaries of companies 

fall into the category of midsize institutions.18 

Where to draw the line between large and all 

other institutions could depend on a variety of 

factors relating to an institution’s complexity and 

the availability of certain kinds of market information. Continuously available subordinated 

debt pricing, for instance, is generally available 

only for the largest banking and thrift organizations (perhaps as many as the top 50 in terms of 

asset size). Thus, the availability of certain types 

of market data could be used to determine which 

banks would be priced under one system compared with another. 

Figure 12 

Lower Risk 

Lower Premium 

Higher Risk 

Higher Premium 

Risk Premium Categories 

Initial risk premium category is 

determined by supervisory rating, 

model, or scorecard. 

At certain levels, market indicators 

trigger shift to a higher risk premium 

category. 

X 

X 

Indicator suggests 

higher risk 

Trip-Wire Approach Using Market 

Indicators 

Using Market Data in the Evaluation of 

Contingent Loss Reserves 

The FDIC is required to establish adequate 

reserves to cover potential insurance fund losses 

from failures. The process of establishing such 

reserves essentially entails considering three 

prospective factors: (1) the likelihood of failure of 

an individual institution, (2) the loss that will be 

incurred if that institution fails, and (3) the level 

of insured deposits at the institution when it fails. 

For publicly held banking and thrift organizations, 

market indicators can be useful in assessing the 

first two of these factors. 

As shown by Moody’s KMV model and others, 

market information such as equity prices and subordinated debt spreads can be used to provide 

quantifiable measures of market failure expectations. When supervisors are evaluating the failure 

prospects of troubled institutions, they can compare these measures with judgment-based assessments that rely on supervisory and financial data. 

In addition, equity prices are a direct measure of 

the value assigned by shareholders to a firm’s 

assets and liabilities. Thus, market valuations 

may be useful when supervisors evaluate the liquidation-value scenarios related to probable failures. 

Incorporating Market Data into Off-site 

Surveillance Models 

Merton-based models such as those used by 

Moody’s KMV are just one of many approaches 

that incorporate market information in the measurement of default probabilities. Arguably, supervisors have the means to improve on these 

models by incorporating both public and nonpublic data into failure estimations. For example, 

logit models that incorporate market, supervisory, 

and financial variables could result in more accurate failure predictions than models that rely solely on market data. 

18 As of year-end 2003, approximately 80 insured banking organizations had 

between $5 billion and $20 billion in assets. 

2005, VOLUME 17, NO. 1 14 FDIC BANKING REVIEW 
Current and Potential Uses of Market Data 

Research at the FDIC has shown that incorporating market signals into early-warning systems 

improves the ability of supervisors to predict 

supervisory ratings of holding companies.19 Such 

early-warning systems are relevant for failure 

models as well, since it is reasonable to expect the 

factors associated with supervisory downgrades to 

be predictive also of financial-institution failures. 

Using Market Information as Benchmarks 

for the Outputs of Internal Ratings-Based 

Capital Models 

Market information could be useful for evaluating 

the consistency and integrity of the advanced 

internal ratings-based (A-IRB) models used for 

Basel II capital calculation purposes.20 Given not 

only the variations among institutions in the 

characteristics of loan portfolios but also the flexibility that exists in the Basel II implementation 

requirements, it is not feasible to use market 

measures to definitively validate or invalidate the 

outputs of A-IRB models. Rather, such measures 

would provide approximations or rough benchmarks, which might highlight potential biases or 

inconsistencies in A-IRB measures applied to corporate loan exposures. 

In terms of market measures, the most obvious 

candidate for producing A-IRB benchmarks is the 

Moody’s KMV model of estimated default frequencies (EDFs). Although not necessarily synonymous with the Basel II definition of the 

probability of default (PD), EDFs are expressed in 

the same basic unit of measurement: one-year 

default expectations related to an obligor.21 The 

most straightforward PD benchmarks would 

involve comparisons between firm-specific EDFs 

and the PDs assigned by the bank for that same 

firm. Less straightforward, but relatively easy to 

construct, would be industry-specific PD benchmarks that were developed from EDFs and could 

be compared with the weighted average portfolio 

PDs for similar industry credit exposures held by 

institutions. Such industry benchmarks hold the 

possibility of extending the use of market indicators beyond the exposures of publicly held companies. 

External loan and bond ratings (debt ratings) can 

also be used to develop proxy benchmarks for 

PDs. Unlike EDFs, debt ratings are not an explicit measure of PDs. Rather, they are long-run estimates of relative likelihood of default through an 

entire business cycle. Nevertheless, default studies produced by the rating agencies give long-run 

averages of default by debt grade. Hence, ratings 

can be associated with PDs if one uses the longrun average historical default rates for a particular 

debt rating. Again, the most straightforward PD 

benchmarks would involve credit exposures to 

firms with rated debt. PD benchmarks for industry credit exposures could also be developed if one 

used average industry debt ratings. 

Conclusion 

Market signals play an important role in supervisory processes. Incorporated into surveillance 

programs, market signals supplement supervisory 

and financial information for purposes of corroborating supervisory risk determinations and evaluations. Market signals also provide quantitative 

rankings of risk that can help in the evaluation of 

supervisory priorities. Although market signals in 

isolation rarely influence supervisory priorities 

and strategies, they are nevertheless a critical factor for supervisors to consider when formulating 

their outlooks for U.S. financial institutions. 

Market signals are important inputs into off-site 

surveillance systems, since they provide supervisors with an objective early-warning indicator. 

Such signals are especially important during the 

period between examinations. 

Beyond their use in surveillance programs, market 

indicators can play a role in the insurance pricing 

19 See Curry, Fissel, and Hanweck (2003). 

20 Under the A-IRB approach of Basel II, certain institutions will be allowed to 

use internal estimates of credit risk for individual loan exposures as inputs 

into regulatory formulas (risk-weight functions), and the regulatory formulas in 

turn determine minimum regulatory capital requirements. The principal internal risk measures provided by A-IRB banks include estimates of probabilities 

of default, losses given default, and facility exposures at default. 

21 EDFs are point-in-time estimates of the likelihood of default (usually 

expressed over a one-year time horizon). In contrast, PDs are intended to represent a conservative, long-run average view of the likelihood of borrower 

default. 

FDIC BANKING REVIEW 15 2005, VOLUME 17, NO. 1 
Current and Potential Uses of Market Data 

and funds management processes, where the 

deposit insurer requires estimates of both the likelihood of failure and the liquidation values of failing-institution assets. Market signals can also add 

explanatory power to failure- and supervisory 

downgrade-prediction models. Finally, applied to 

credit exposures, market data can be used to construct rough benchmarks for the outputs of A-IRB 

models, which serve as critical inputs into regulatory capital requirements under Basel II. 

Broader use of market data largely depends on the 

development of a reliable source of market prices 

that are linked directly with other supervisory and 

regulatory financial data. For example, off-site 

surveillance models could be significantly 

enhanced if they could be automatically linked to 

multiple sources of information on debt and equity prices. To apply this information to insured 

subsidiaries, it will also be necessary to identify 

explicit linkages between market data, which 

relate to the consolidated operations of a company, and financial performance information that is 

related to insured subsidiaries. Finally, analysts 

and examiners will have to be able to clearly 

define the notions of significance and permanence as they relate to changes in market valuations. For all these reasons, the FDIC is pursuing 

the creation of a market data warehouse. Such a 

warehouse of information will achieve several 

objectives, including those of collecting multiple 

sources of debt and equity information under one 

database, linking this information to financial 

information on insured institutions, and developing algorithms that alert analysts and examiners 

to significant, long-term shifts in debt and equity 

prices. 

2005, VOLUME 17, NO. 1 16 FDIC BANKING REVIEW 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Current and Potential Uses of Market Data 

REFERENCES
 

Bloecher, Eric P., Gary A. Seale, and Robert D. Vilim. 2003. Options for Pricing Federal 

Deposit Insurance. FDIC Banking Review 15, no. 4:1–15. Available at 

www.fdic.gov/bank/analytical/banking/2003dec/1_15n4.pdf. 

Burton, Steven. 2001. Credit Problems for U.S. Businesses Continue to Increase. 

National Regional Outlook. Available at 

http://www.fdic.gov/bank/analytical/regional/ro20011q/na/Infocus1.html. 

Curry, Timothy J., Peter J. Elmer, and Gary S. Fissel. 2001. Regulator Use of Market Data 

to Improve the Identification of Bank Financial Distress. Working Paper No. 

2001-01. Federal Deposit Insurance Corporation. 

———. 2003. Market Information, Bank Holding Company Risk, and Market 

Discipline. Working Paper No. 2003-04. Federal Deposit Insurance Corporation. 

Available at 

http://www.fdic.gov/bank/analytical/working/wp2003_04/wp2003_04.pdf. 

Curry, Timothy, Gary S. Fissel, and Gerald Hanweck. 2003. Market Information, Bank 

Holding Company Risk, and Market Discipline. Working Paper No. 2003-04. 

Federal Deposit Insurance Corporation. Available at 

http://www.fdic.gov/bank/analytical/working/wp2003_04/wp2003_04.pdf. 

Elmer, Peter J., and Gary S. Fissel. 2001. Forecasting Bank Failure from Momentum 

Patterns in Stock Returns. Unpublished Paper. FDIC. 

Evanoff, D., and L. Wall. 2001. Sub-Debt Yield Spreads as Bank Risk Measures. Journal 

of Financial Services Research 20:121–45. 

Federal Deposit Insurance Corporation (FDIC). 2001. Keeping the Promise: 

Recommendations for Deposit Insurance Reform. Available at 

www.fdic.gov/deposit/insurance/initiative/direcommendations.pdf. 

Feldman, R., and M. Levonian. 2001. Market Data and Bank Supervision: The 

Transition to Practical Use. Federal Reserve Bank of Minneapolis, The Region 15, 

no. 3:11–13, 46–54. 

Gilbert, A., A. Meyer, and M. Vaughn. 2001. Can Feedback from the Jumbo CD Market 

Improve Off-Site Surveillance of Small Banks? Working Paper No. 2002-08. 

Federal Reserve Bank of St. Louis. 

Gunther, J. W., M. E. Levonian, and R. R. Moore. 2001. Can the Stock Market Tell 

Bank Supervisors Anything They Don’t Already Know? Federal Reserve Bank of 

Dallas Economic and Financial Review (Q2): 2–9. 

Hall, J. R., T. B. King, A. P. Meyer, and M. D. Vaughn. 2001. What Can Bank 

Supervisors Learn from Equity Markets? A Comparison of the Factors Affecting 

Market-Based Risk Measures and BOPEC Scores. Working Paper No. 2002-06. 

Federal Reserve Bank of St. Louis. 

Krainer, J., and J. Lopez. 2003. How Might Financial Market Information Be Used for 

Supervisory Purposes? Federal Reserve Bank of San Francisco Economic Review: 

29–45. 

FDIC BANKING REVIEW 17 2005, VOLUME 17, NO. 1 
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Limited-Purpose Banks: Their Specialties, 

Performance, and Prospects 

Chiwon Yom* 

Limited-purpose banks are institutions that specialize in relatively narrow business lines. Some limited-purpose banks concentrate on making a certain 

type of loan, some serve a subset of consumers, and 

some offer an innovative product. As niche players focusing on a limited set of activities, these 

institutions can quickly develop expertise in their 

particular business lines and can become efficient 

producers. Specialization may have been promoted by technological innovations, which generally 

lead to gains in productivity and economies of 

scale. 

This study examines credit card banks, subprime 

lenders, and Internet primary banks. Although 

numerically these institutions make up a small 

share of the financial services industry, their 

unique products and technologies have attracted 

considerable attention. Insured institutions such 

as MBNA, Providian, and ETrade Bank are examples of limited-purpose banks specializing, respectively, in credit card services, subprime lending, 

and Internet banking. 

Credit card banks offer their customers both convenience and liquidity by providing a financial 

product that can be used as a means of payment 

and a source of instant credit. These banks are 

very profitable, earning higher income than the 

industry. Their use of technology and the benefits 

of economies of scale have probably contributed to 

their superior financial performance. 

Subprime lenders are insured institutions that specialize in lending to people with poor credit histories. By focusing on a customer base that was 

formerly shunned by the banking industry, these 

banks can boost their profit margins. Although 

some subprime lenders have outperformed the 

industry, others have either failed, experienced 

large losses, or remained in business but exited the 

subprime market altogether. 

Internet primary banks use the Internet as their 

sole means of delivering banking services. It was 

once widely believed that Internet banks could 

earn higher profits by eliminating physical branches and reducing overhead expenses. However, cost 

reductions and higher profitability have not been 

realized, and Internet banks continue to underper­

* The author is a senior financial economist in the Division of Insurance and 

Research at the Federal Deposit Insurance Corporation. The author would like 

to thank George Hanc, Dan Nuxoll, Jack Reidhill, Tim Curry, and Gary Fissel 

for valuable comments and suggestions, and Sarah Junker for research 

assistance. 

FDIC BANKING REVIEW 19 2005, VOLUME 17, NO. 1 
 

Limited-Purpose Banks 

form relative to the industry. Their underperformance may reflect limited consumer demand for 

Internet banking services. And relative to branching banks, Internet banks are at a competitive disadvantage in lending to small businesses because 

they lack the means of building long-term relationships with borrowers. 

The next section reviews some of the important 

technological innovations that promoted the 

growth of limited-purpose banks. The subsequent 

section describes the data used in this study. Then 

come three sections analyzing, respectively, credit 

card banks, subprime lenders, and Internet banks. 

Each of those three sections describes the unique 

characteristics of the particular type of limited-purpose bank, along with the distinctive business 

model used; compares that type of limited-purpose 

bank with the rest of the industry in terms of 

financial performance and risk characteristics; and 

assesses those banks’ viability and prospects. A 

final section concludes. 

Technological Innovations in the Financial 

Services Industry 

Technological improvements have played an 

important role in the growth of limited-purpose 

banks as well as in the broader financial services 

industry. Some people even argue that improvements in technology led financial institutions to 

specialize. Jim Marks, a director at Credit Suisse 

First Boston, states, “The lessons over the past 20 

to 30 years have taught us that technological 

improvements lead to specialization.”1 Technologically intensive production processes generally 

exhibit large economies of scale which means that 

larger operations have lower costs. By producing a 

large quantity of a single product, these banks can 

benefit from scale economies. In addition, specialization may reduce the risky investments in technology that banks need to make. 

A number of innovations were vital to implementing the business models adopted by limited-purpose banks. Among these innovations are 

data-mining techniques, electronic payment systems, securitization, and the Internet. 

Data-mining techniques are increasingly used for 

various purposes in the financial services industry. 

The most significant example of their use is in 

credit scoring. Credit scoring uses historical data 

and statistical techniques to produce a score that 

summarizes a loan applicant’s credit risk. Credit 

scoring is used to speed up credit decisions, to price 

loans, to constitute input in automated underwriting processes, to screen prospective customers, to 

price the default risk of asset-backed securities in 

secondary markets, and to monitor accounts. 

Data-mining techniques are also used by financial 

institutions to target potential customers for solicitations and to manage existing accounts. To 

attract new customers, institutions use data-mining 

techniques to identify potential customers. Institutions can target potential customers of a certain 

credit quality or can identify the potential customers most likely to respond to specific offers 

(such as free airline miles or low-cost balance 

transfers). Once the institutions obtain new customers, they can use the data to manage the 

accounts on an ongoing basis. They may use customer-specific information to assess which 

accounts are most profitable for them or to predict 

which customers are likely to defect to a competitor. The limited-purpose banks examined in this 

study, especially credit card banks and Internet 

banks, rely heavily on data-mining techniques. 

These banks operate in a national market and 

have little direct contact with borrowers, so data 

mining is the only feasible way for them to solicit 

potential customers, underwrite loans, and manage 

customers’ accounts. 

Electronic payment systems, which are methods of 

transferring funds electronically, are another 

important innovation in the financial services 

industry. Studies have found results that are consistent with electronic payments technologies displaying economies of scale (Berger [2003]). 

Moreover, improvements in technology have dramatically reduced the costs of processing electronic 

payments and increased the availability of such 

1 See Wenninger (2000). 

2005, VOLUME 17, NO. 1 20 FDIC BANKING REVIEW 
Limited-Purpose Banks 

processing. Such improvements benefited credit 

card banks as lower cost and increased availability 

of electronic payments technology has led more 

retail businesses to accept payments by credit card. 

Internet banks, too, rely heavily on electronic payments technology. Lacking physical branches, 

they rely both on ATMs to give their customers 

access to cash and on the Automated Clearing 

House (ACH) for fund transfers. 

Securitization, which is a process of pooling financial assets into commodity-like securities, has also 

played a vital role in the growth of limited-purpose 

banks. Securitized financial assets typically include 

credit card balances, automobile receivable paper, 

commercial and residential first mortgages, commercial loans, home equity loans, and student 

loans.2 The pool of assets is transferred to a special-purpose entity, which issues securities that are 

rated, underwritten, and then sold to investors. 

During the period 1984–2001, asset-backed securities grew at an average annual rate of 13.7 percent 

(Berger [2003]). According to Furletti (2002), 

$6.6 trillion of tradable securities made up the 

asset-backed securities market as of June 2002. 

Since its introduction in 1987, credit card securitization has become a primary source of funding 

(Furletti [2002]) and is integral to the growth of 

the credit card industry (Calomiris and Mason 

[2003]). More generally, securitization helped the 

consumer finance sector reach double-digit growth 

in the early 1990s (Calomiris and Mason [2003]). 

As of June 2002, credit card asset-backed securities 

amounted to $400 billion (Furletti [2002]). 

Securitization also contributed to the growth in 

subprime lending (Laderman [2001]). Mahalik 

and Robinson (1998) note that the production of 

subprime mortgage securities more than tripled 

between 1995 and 1997, going from $18 billion to 

$66 billion. In addition, the percentage of subprime mortgages being financed by securitizations 

is rising: approximately 53 percent of all subprime 

mortgage loans originated in 1997 were sold in the 

securities market, compared with 28 percent in 

1995. 

The Internet and Internet security and protection 

technologies are important for on-line banking. 

As part of information technology, the Internet 

brings together different parties and allows them to 

share information. Because banking is an 

exchange of information between a bank and its 

customers, the Internet has become an important 

innovation for financial institutions. Using the 

Internet distribution channel, banks can offer 

increased convenience to customers by allowing 

them to perform their banking activities on-line at 

any time and in any place. Moreover, improvements in Internet security and protection technologies help prevent hackers from breaking into 

the computer systems. These technologies provide 

consumers with some confidence that their Internet bank accounts will remain secure. 

Data 

The sample of limited-purpose banks used in this 

study is taken from various sources. Credit card 

banks are those defined as such by the FDIC’s 

Research Information System (RIS). The list of 

subprime lenders is from the FDIC’s Quarterly 

Lending Alert (QLA). The sample of Internet 

banks is from the FDIC’s informal database of 

Internet primary banks.3 

Credit card banks are institutions (1) the sum of 

whose total loans, asset-backed securities on credit 

card receivables, and bank securitization activities 

of credit card loans sold and securitized (with servicing retained or with recourse or other seller-provided credit enhancements) is greater than 50 

percent of the sum of total assets and bank securitization activities of credit card loans sold and 

securitized, and (2) the sum of whose credit card 

loans, asset-backed securities on credit card receivables, and bank securitization activities of credit 

card loans sold and securitized is greater than 50 

percent of the sum of total loans, asset-backed 

2 See OCC, et al. (1999). 


3 This is an informal database and may not be comprehensive.
 

FDIC BANKING REVIEW 21 2005, VOLUME 17, NO. 1 
Limited-Purpose Banks 

securities on credit card receivables, and bank 

securitization activities of credit card loans sold 

and securitized. 

The FDIC’s QLA is a database of insured institutions that engage in risky lending activities such as 

high loan-to-value loans, subprime lending, and 

payday lending. Insured banks with an aggregate 

credit exposure related to subprime loans that are 

equal to or greater than 25 percent of Tier 1 capital are referred to as subprime lenders. According 

to this FDIC definition, aggregate exposure 

includes principal outstanding and committed, 

accrued and unpaid interest, and any retained 

residual assets relating to securitized subprime 

loans. The QLA database includes information on 

types of subprime loans (e.g., automobile, credit 

card, mortgage, and other). 

As of October 22, 2002, there were 18 banks that 

used the Internet as their primary method of contacting customers. One institution has been 

removed from the sample because it has 17 fullservice brick-and-mortar branches, and it is hard to 

argue that an institution with 17 branches is an 

Internet bank. In addition, two institutions were 

involved in voluntary liquidation and closing prior 

to December 2003. As a result, 15 Internet primary banks remain in the sample. 

All balance-sheet and income-statement variables 

are from the quarterly Report of Income and Condition (Call Report). The Federal Reserve System’s Surveys of Consumer Finances data are also 

used. 

Credit Card Banks 

Credit cards date from the Diners Club, the first 

“universal” card, which was introduced in 1949 

and used for purchases at restaurants and in 

department stores. Recognizing the potential 

profitability of providing open-end financing to 

consumers who were willing to pay high rates of 

interest to obtain unsecured credit, commercial 

banks began offering general-purpose credit cards 

to individual consumers; the cards came into 

broad use in the middle-to-late 1960s (Canner 

and Luckett [1992]). Bank-type credit cards offer 

both convenience and liquidity to their customers: 

they can be used as a payment device or as openend revolving credit. Today, the bank-type card is 

the most widely held among different types of 

credit cards. 

Table 1, which reports the percentage of households with bank-type cards, shows the rise in ownership of bank-type cards over the past three 

decades. In 1970, 16 percent of households surveyed had bank-type credit cards. In 2001, the 

comparable figure was 72 percent. Moreover, the 

increase in the shares of households with credit 

cards over time is evident at all income levels 

(Durkin [2000]). Clearly, credit cards have 

become a consumer financial product important to 

households regardless of income. 

Credit card banks are affiliated with national credit systems, such as VISA and MasterCard, to be 

part of a network. The national credit systems 

allow the cardholder to use a credit card for purchasing goods and services in areas served by other 

banks. Thus, sales drafts can be transferred from 

the merchant’s bank to the cardholder’s bank for 

collection. The national systems effectively transform local cards into national cards. 

Business decisions, however, are made at the level 

of the card-issuing bank. Individual banks own 

their cardholders’ accounts and determine the 

interest rate, annual fee, grace period, credit limit, 

and other terms of the accounts. Thus, this study 

examines the credit card business at the individual-bank level. 

Table 1 

Usage of Credit Cards among U.S. Households 

1970 1977 1983 1989 1995 1998 2001 

Households with bank-type 

credit cards (%) 16 38 43 56 66 68 72 

Households with outstanding 

balances on bank-type card 

after the most recent 

payment (%) 37 44 51 52 56 55 54 

Source: Federal Reserve Board’s Survey of Consumer Finances. 

2005, VOLUME 17, NO. 1 22 FDIC BANKING REVIEW 
Limited-Purpose Banks 

Consolidation 

Figure 1 shows that in recent years, trends in the 

size and number of credit card banks have gone in 

opposing directions. Since 1995, the average asset 

size of credit card banks has been growing at the 

rate of roughly 20.5 percent annually. In contrast, 

the number of credit card banks has been declining 

at an annual rate of 6.8 percent. Similarly, figure 2 

shows that trends for the number of credit card 

banks and for the mean value of credit card loans 

have moved in opposing directions in recent years. 

The average credit card loan has been steadily 

increasing. 

Consolidation in the bank credit card industry can 

be attributed to a number of factors (Mandell 

[1990]). First, consolidation may be necessary to 

exploit economies of scale. There is some evidence that credit card bank operations exhibit 

increasing returns to scale. Pavel and Binkley 

(1987) find evidence of increasing returns to scale 

at small-to medium-size card banks. Canner and 

Luckett (1992) find that operating expenses 

account for a smaller portion of the total cost for 

the large issuers; thus, large card issuers would 

enjoy some benefits of economies of scale in their 

operations. 

Second, by consolidating, banks can achieve the 

size necessary to conduct certain activities. For 

instance, the marketing tools used by credit card 

Figure 1 

banks, such as television commercials, Internet 

advertisements, and mail solicitations, are expensive and can be used only by a few large institutions. Through consolidation, credit card banks 

may reach the size that will enable them to allocate funds for such costly marketing activities. 

Third, because most cardholders lack a sense of 

identification with the banks that issued their 

credit cards, their loyalty to specific card banks is 

likely to be low; accordingly, little (in terms of customer loyalty) is lost through consolidation. 

Financial Performance 

Credit card banks enjoy consistently higher earnings than the banking industry as a whole. Table 2 

presents interest and noninterest income for the 

three kinds of limited-purpose banks we are studying and for all banks. As of December 2003, the 

average return on assets (ROA) of credit card 

banks was 4.6 percent—more than four times the 

1.0 percent of the industry average. Possibly the 

card banks’ ROAs are being inflated by their securitization income. 

A closer examination of credit card bank operations will help us understand the revenue and cost 

structures of these banks. As mentioned above, 

consumers use credit cards mainly as a means of 

payment and a source of open-end revolving credFigure 2 

Mean Asset Size ($ Millions) No. of Credit Card Banks 

Number of Credit Card Banks and 

Mean Asset Size 

3/84 3/85 3/86 3/87 3/88 3/89 3/90 3/91 3/92 3/93 3/94 3/95 3/96 3/97 3/98 3/99 3/00 3/01 3/02 3/03 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

0 

20 

40 

60 

80 

100 

Number of Credit Card Banks 

Mean Asset Size ($ Millions) 

Mean Credit Card Loan Amount ($ Millions) No. of Credit Card Banks 

Number of Credit Card Banks and 

Mean Credit Card Loan Amount 

3/84 3/85 3/86 3/87 3/88 3/89 3/90 3/91 3/92 3/93 3/94 3/95 3/96 3/97 3/98 3/99 3/00 3/01 3/02 3/03 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

0 

15 

30 

45 

60 

75 

90 

Number of Credit Card Banks 

Mean Credit Card Loan Amount ($ Millions) 

FDIC BANKING REVIEW 23 2005, VOLUME 17, NO. 1 
 

Limited-Purpose Banks 

it. In transactions where consumers use credit cards 

as a payment device and pay back the loans within 

the grace period, banks forgo interest income, 

although they still earn noninterest income from fees. 

Only when the card is used as a source of credit do 

banks earn interest income as well as noninterest 

income. 

Column 1 of table 2 shows that credit card banks 

earn high interest income. On average, the card 

banks’ interest income is 10.8 percent of assets—more 

than twice the 5.3 percent earned by the industry 

during the year ending December 31, 2003. Historically, credit card rates have been higher than competitive rates and more stable than the cost of funds. 

Moreover, credit card loan rates are more stable than 

the rates of other types of loans, such as mortgage and 

auto loans (Canner and Luckett [1992]). 

Table 2 

Summary Measures of Profitabilitya 

(as of December 31, 2003) 

(1) (2) (3) (4) 

Credit Card Internet Subprime All Banks 

Banks Banks Lenders and Thrifts 

Mean 

Interest income 10.8 4.6 8.0 5.3 

Interest expense 1.6 2.1 2.3 1.6 

Net interest income 9.2 2.5 5.8 3.7 

Noninterest income 18.5 1.1 3.4 1.6 

Other noninterest income 0.0 0.1 0.1 0.0 

Noninterest expense 16.8 3.4 5.6 3.6 

Other noninterest expense 13.8 1.2 2.9 1.3 

Net noninterest income 1.7 -2.3 -2.3 -2.0 

ROA 4.6 0.7 1.2 1.0 

ROE 24.3 8.8 11.1 10.0 

Median 

Interest income 8.7 4.5 6.2 5.2 

Interest expense 1.6 2.0 2.1 1.6 

Net interest income 7.0 2.3 4.1 3.6 

Noninterest income 10.2 0.2 0.8 0.7 

Other noninterest income 0.0 0.0 0.0 0.0 

Noninterest expense 9.3 2.6 3.6 2.8 

Other noninterest expense 6.2 0.8 1.2 0.8 

Net noninterest income 1.5 -1.6 -2.4 -2.0 

ROA 3.6 0.8 1.1 1.0 

ROE 22.4 9.9 11.1 9.9 

No. of observations 36 15 116 9181 

a Income and expense ratios are expressed as a percentage of assets. ROA is 

expressed as a percentage of assets and ROE is expressed as a percentage of 

equity. The variables are merger-adjusted four-quarter totals. 

Some economists argue that cardholders are 

insensitive to interest rates because the cardholders persistently underestimate the extent to 

which they will carry over unpaid balances and 

thereby incur interest costs (Ausubel [1991]). 

Moreover, high-and-sticky card rates are attributed to the high search-and-switching costs. 

Cargill and Wendel (1996) claim that compared 

with small average balances, the cost of cardholders searching for lower rates is too high. 

Calem and Mester (1995) maintain that the 

inconvenience of switching accounts is another 

reason for cardholders to be insensitive to interest rates. 

While credit card banks’ interest income is substantially higher, their interest expense is similar 

to the industry average: during the year ending 

December 31, 2003, interest expense on average 

amounted to 1.6 percent of total assets at credit 

card banks, same as for the industry. By earning 

substantially higher interest income without 

having to incur higher interest expense, credit 

card banks earn a high net interest income. 

During the year ending December 31, 2003, the 

mean value of net interest income to total assets 

ratio for credit card banks was more than double 

the industry average. 

Credit card banks earn noninterest income by 

charging annual fees, finance charges, late-payment fees, over-limit fees, and other servicing 

fees. Feldman and Schmidt (2000) find that 

noninterest income makes up a greater share of 

net revenue at credit card banks than at noncredit card banks. Moreover, credit card banks 

earn noninterest income by servicing accounts 

that are taken off their balance sheets through 

securitization. By providing services to securitized asset trusts—for example, by mailing 

monthly statements to customers, answering 

phone calls, and collecting past-due balances— 

credit card banks earn servicing fees from the 

trusts (Furletti [2002]). Earning servicing fees 

from securitized assets has the effect of inflating 

the credit card banks’ ROAs: in most cases, 

credit card securitization is structured as a sale, 

and by earning noninterest income on securi2005, VOLUME 17, NO. 1 24 FDIC BANKING REVIEW 
Limited-Purpose Banks 

tized assets that are taken off their balance sheets, 

the credit card banks have an ROA that is elevated compared with the ROAs of institutions that 

keep their receivables in their asset portfolios. 

This situation suggests that simply examining the 

financial ratios, such as ROA, can be misleading, 

since these ratios mask the risks that banks are 

exposed to if they have recourse interest on their 

securitized assets. 

At the same time, credit card banks incur high 

noninterest expenses. On December 31, 2003, for 

instance, the average noninterest expense of credit 

card banks amounted to roughly 17 percent of 

total assets. Processing credit card transactions is a 

costly operation. Pavel and Binkley (1987) detail 

the mechanics of bank card transactions. When a 

cardholder uses his or her credit card, a sales slip is 

created and sent to a merchant’s bank for processing. The merchant’s bank credits the merchant’s 

account for the amount of the sale and sends the 

sales information to the interchange facilities 

(such as MasterCard or Visa). The interchange 

facilities transfer the sales information to the issuing bank and send the amount of the transaction 

less an interchange fee and a per-item fee to the 

merchant’s bank. Then the issuing bank bills the 

cardholder. Having to process a large volume of 

transactions and service a large number of 

accounts, credit card banks incur large processing 

expenses. Although advances in technology have 

substantially improved operating efficiency at credit card banks, operating expenses remain high. 

Other noninterest expenses include advertising 

and marketing expenses, fraud losses, and network 

access fees. 

Like other limited-purpose banks, credit card 

banks are likely to suffer from high income volatility because of a lack of diversification in their loan 

portfolios. There are, however, a number of factors 

that can dampen these income fluctuations. First, 

credit card banks’ greater dependence on noninterest income can partially offset and reduce the 

income volatility. Second, credit card banks’ cost 

of funds tends to go down when charge-off costs 

are high, and the lower cost of funds can offset the 

adverse effects of high default rates on the banks’ 

profitability. 

Empirical evidence however, shows that these factors fail to offset the credit card banks’ income 

volatility; these banks suffer from higher income 

fluctuations. At the same time, their earnings are 

consistently higher than those of a typical bank. 

Even during periods of low profitability, credit card 

banks continue to outperform other banks. 

Prospects 

Credit card banks are highly profitable and are an 

example of institutions that successfully implemented the business model of specialization. The 

successful use of technology and the benefits of 

scale economies are likely to have contributed to 

their superior financial performance. Given their 

profitability, it is reasonable to expect that these 

banks will continue to supply credit card services. 

On the demand side, the share of households with 

bank-type cards has been steadily rising, and these 

households maintain positive attitudes toward 

credit cards. According to the Survey of Consumer Finances in 2001, the holders of bank-type 

credit cards consider the cards useful and believe 

that they are better off with them. It is reasonable 

to expect that the demand for credit card services 

will remain high and that credit card banks will 

continue to provide the service. It remains to be 

seen whether these banks have exhausted the benefits of scale economies or will continue to consolidate. 

Subprime Lenders 

Subprime borrowers are those with weakened-orpoor credit histories, and traditionally banks have 

stayed away from extending credit to them.4 

Banks’ practices have locked subprime borrowers 

out of the mainstream credit system. 

4 The bank regulatory agencies have recently suggested that any of the 

following may indicate a subprime borrower: (1) a FICO credit score of 660 

or below; (2) two or more 30-day delinquencies during the past year; (3) 

bankruptcy within the last five years; (4) judgement, foreclosure, repossession, 

or charge-offs in the prior 24 months; or (5) debt service-to-income ratio of 

50 percent or greater (OCC et al. [2001]). 

FDIC BANKING REVIEW 25 2005, VOLUME 17, NO. 1 
Limited-Purpose Banks 

In the early-1900s, the credit market neglected 

lower-income households. At the time, usury laws 

set a maximum rate that could be charged on 

loans. Such laws reflected a sentiment shared by 

many at the time that regarded debt for the purposes of personal consumption with great disfavor. 

Because of high transaction costs per account, such 

usury laws effectively made small loans infeasible. 

In contrast, businessmen were easily able to obtain 

bank loans for both business and personal needs. 

Hence, usury laws had the effect of locking lowerincome households out of the credit market. Consequently, many of these households had to rely on 

loan sharks for credit and had to pay high (illegal) 

rates. 

Similarly today, subprime borrowers who cannot 

obtain credit from banks or other financial institutions are left to rely on pawnshops, payday lenders, 

and rent-to-own stores to meet their credit needs. 

Carr and Shuetz (2001) note that as many as 12 

million households either have no relationship 

with traditional financial institutions or depend on 

fringe lenders for financial services. The fringe 

lenders remain largely unregulated, and they frequently charge excessively high fees. Relying 

heavily on such lenders for credit needs can marginalize borrowers and expose them to predatory 

practices. Carr and Kolluri (2001) note that 

predatory lending thrives in an environment 

where competition for financial services is limited 

or nonexistent. 

In recent years, however, insured institutions have 

begun to participate in the subprime market. 

Their entry has been motivated by high prospective profits and the possibility of using existing 

capacity. Banks generally participate in the subprime market by, “Lending directly to subprime 

borrowers, purchasing subprime dealer paper or 

loans acquired through brokers, lending directly to 

financing companies involved in subprime lending, 

participating in loan syndications providing credit 

to such financing companies, and acquiring assetbacked securities issued by these financing companies.”5 

Table 3 summarizes the subprime loan portfolio of 

subprime lenders over time. The FDIC’s QLA 

database includes banks identified as subprime 

lenders starting with September 1999. For each 

quarter, one column reports the total amount of 

subprime loans in these lenders’ asset portfolios 

and a second column reports the ratio (as a percentage) of total subprime loans to total assets. 

Table 3 also breaks down subprime loans into different types, such as automobile, credit card, mortgage, and other. For September 1999 and 

September 2000, automobile, credit card, mortgage, and other subprime loan information are 

missing because these loans are not documented in 

the QLA database. For all periods, mortgage and 

credit card loans make up the largest volume of 

subprime loans. 

On average, subprime lenders are larger than a typical bank. As of December 31, 2003, the average 

total assets of subprime lenders were $4.0 billion, 

compared with $1.0 billion for the industry. It 

may well be that subprime lending requires a certain set of skills or resources that are more likely to 

be available to larger banks. These lenders may 

need staff with expertise in subprime lending activities, or larger staff to handle the collection efforts 

on delinquent loans. Moreover, accessing capital 

markets to fund these loans may be easier for large 

banks. 

In September 1999, subprime loans totaled $23 

billion, which made up 7.2 percent of these institutions’ assets. For the next two years the volume 

of lending by insured institutions to subprime borrowers steadily rose (except for June 2000), reaching $81 billion in September 2001. Since 

September 2001, however, the volume of subprime 

loans has been gradually decreasing. By December 

2003, total subprime loans had fallen to $52 billion, making up 11.21 percent of assets at these 

institutions. 

The number of institutions actively participating 

in the subprime market shows a similar trend. The 

number increased to 156 institutions in December 

2000 and fell thereafter, dropping to 116 by 

December 2003. 

5 See FDIC (1997). 

2005, VOLUME 17, NO. 1 26 FDIC BANKING REVIEW 
 

 

 

 

 

 

 

 

 

 

 

 

Limited-Purpose Banks 

Table 3 

Aggregate Subprime Loan Amounts: in Level and 

as a Percentage of Total Assetsa 

Level Ratio Level Ratio Level Ratio 

($ millions) (percent) ($ millions) (percent) ($ millions) (percent) 

Date 9/99 12/99 3/00 

Subprime total 23,143 7.19 28,840 5.97 66,770 7.12 

Automobile 69 0.01 2,924 0.31 

Credit card 22 0.00 10,076 1.07 

Mortgage 74 0.02 25,838 2.75 

Other 37 0.01 7,723 0.82 

Payday 

No. of observations 121 131 145 

Date 6/00 9/00 12/00 

Subprime total 70,914 7.29 67,408 6.77 67,860 6.68 

Automobile 2,872 0.30 3,611 0.36 

Credit card 14,479 1.49 18,505 1.82 

Mortgage 40,372 4.15 42,485 4.18 

Other 7,743 0.80 3,290 0.32 

Payday 

No. of observations 148 145 156 

Date 3/01 6/01 9/01 

Subprime total 71,503 7.20 73,149 7.36 80,717 7.97 

Automobile 2,860 0.29 4,806 0.48 5,245 0.52 

Credit card 24,393 2.46 24,936 2.51 25,105 2.48 

Mortgage 41,809 4.21 41,169 4.14 46,872 4.63 

Other 2,031 0.20 1,984 0.20 3,341 0.33 

Payday 79 0.01 64 0.01 38 0.00 

No. of observations 144 133 127 

Date 12/01 3/02 6/02 

Subprime total 71,157 14.56 69,203 13.21 65,145 12.43 

Automobile 4,410 0.90 4,282 0.82 4,898 0.93 

Credit card 26,256 5.37 27,962 5.34 25,371 4.84 

Mortgage 34,246 7.01 31,434 6.00 29,283 5.59 

Other 6,044 1.24 5,426 1.04 5,428 1.04 

Payday 52 0.01 42 0.01 49 0.01 

No. of observations 130 129 129 

Date 9/02 12/02 3/03 

Subprime total 65,800 12.21 53,879 9.86 53,775 9.67 

Automobile 4,602 0.85 4,504 0.82 21,156 3.81 

Credit card 20,504 3.81 18,667 3.42 17,319 3.11 

Mortgage 33,259 6.17 27,687 5.07 28,723 5.17 

Other 5,032 0.93 2,939 0.54 2,818 0.51 

Payday 46 0.01 39 0.01 18 0.00 

No. of observations 129 127 119 

Date 6/03 9/03 12/03 

Subprime total 55,417 10.67 51,382 10.85 52,119 11.21 

Automobile 23,954 4.61 6,516 1.38 6,470 1.39 

Credit card 16,104 3.10 15,916 3.36 15,675 3.37 

Mortgage 35,684 6.87 24,682 5.21 27,666 5.95 

Other 2,687 0.52 2,347 0.50 2,387 0.51 

Payday 8 0.00 0 0.00 0 0.00 

No. of observations 120 119 116 

Source: FDIC, Quarterly Lending Alert. a Level refers to the aggregate amount of the subprime loans. Ratio refers to the ratio 

(in percent) of aggregate subprime loan amounts to the aggregate assets of the subprime 

lenders. 

At the same time, the ratio of subprime 

loans to total assets at these institutions has 

been increasing. Figure 3 shows that the 

ratio of total subprime loans to total assets at 

subprime lenders rose sharply from December 2001. Although the concentration in 

subprime loans has fallen in recent periods, 

the ratio of subprime loans to total assets at 

subprime lenders remains above those prior 

to December 2001. This rise suggests that 

the insured institutions that continue to participate in the subprime market are the ones 

whose loan portfolios have higher concentrations of subprime loans. It may well be 

that the insured institutions that are successful in lending to the subprime market are 

staying in the market and increasing their 

concentrations in these loans. 

Financial Performance 

On average, subprime lenders earn higher 

net interest income compared with the 

industry. Figure 4 graphs the ratio of interest 

income, interest expense, and net interest 

income to total assets across time for subprime lenders and for all banks. 

Subprime lenders earn higher interest 

income. During the period September 1999 

to December 2003, the ratio of subprime 

lenders’ annual average interest income to 

assets was 9.3 percent. In comparison, the 

industry earned 6.8 percent on average. 

Subprime lenders charge higher interest 

rates to compensate for the greater risk posed 

by subprime borrowers. Some people argue 

that the higher interest rates charged also 

reflect a lack of standardization in underwriting that makes it more costly to originate 

and service loans to borrowers with blemished credit histories and limited income. 

The high interest income earned by subprime lenders more than offsets their higher 

interest expense and allows them to earn 

higher net interest income than the industry 

FDIC BANKING REVIEW 27 2005, VOLUME 17, NO. 1 
Limited-Purpose Banks 

average. For instance, during the period September 1999 to December 2003, subprime lenders had 

the average annual net interest income-to-assets 

ratio of 5.8 percent, compared with 3.9 percent for 

the industry. 

In many cases, the loan rate is not the entire 

source of income for subprime lenders. Subprime 

lenders generally charge up-front fees and prepayment penalties, both of which increase their noninterest income. At the same time, loans to 

subprime borrowers usually require intensive levels 

of servicing and collection efforts to ensure timely 

payment, with the result that noninterest expense 

is higher. Thus, subprime lenders earn lower net 

noninterest income (see figure 5). During the 

same period (September 1999 to December 2003), 

subprime lenders earned net noninterest income of 

–2.4 percent, compared with –2.1 percent for the 

industry. Moreover, high charge-offs and loan-loss 

provisions deplete the earnings of these institutions. 

Net of these factors, subprime lenders’ profitability 

is comparable to that of other insured institutions. 

During the period September 1999 to December 

2003, subprime lenders earned an average ROA of 

1.2 percent, compared with 1.1 percent for the 

industry average. Similarly, the average return on 

equity (ROE) of subprime lenders was 10.9 percent, compared with 10.8 percent for the industry. 

Figure 3 

9/99 12/99 3/00 6/00 9/0012/00 3/01 6/01 9/0112/01 3/02 6/02 9/0212/02 3/03 6/03 9/03 12/03 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

60,000 

70,000 

80,000 

90,000 

0 

2 

4 

6 

8 

10 

12 

14 

16 

$ Millions Percent 

Subprime Loans 

Subprime-to-Total Assets 

Subprime Loan Amounts 

It is important to note that the above-average rate 

of return masks the large fluctuations in earnings 

experienced by subprime lenders. Figure 6, which 

graphs the rate of return over time, shows these 

fluctuations. In some periods, subprime lenders 

performed worse than the industry. For instance, 

in December 2001 subprime lenders had an average ROA and ROE of 0.77 percent and 7.23 percent, respectively. In comparison, the industry 

average ROA and ROE for the same period were 

0.94 percent and 9.58 percent, respectively. In 

more recent periods, however, the subprime 

lenders have been outperforming the industry. 

Possibly there is a survivorship bias in the sample: 

only the successful participants are left, while poorly performing lenders have exited the subprime 

market. 

Figure 4 

A. Subprime Lenders 

Average Net Interest Income Income/Asset (percent) 

12 

10 

8 

6 

4 

2 

0 

Interest Income 

Net Interest Income 

Interest Expense 

9/99 12/99 3/00 6/00 9/00 12/00 3/01 6/01 9/0112/01 3/02 6/02 9/02 12/02 3/03 6/03 9/03 12/03 

B. All Banks 

Average Net Interest Income Income/Asset (percent) 

12 

10 

8 

6 

4 

2 

0 

Interest Income 

Net Interest Income 

Interest Expense 

9/99 12/99 3/00 6/00 9/00 12/00 3/01 6/01 9/01 12/01 3/02 6/02 9/02 12/02 3/03 6/03 9/03 12/03 

Note: 	Based on 147 observations during June 2000 instead of 148 observations. One institution

 was taken out for this calculation due to its extreme values. 

2005, VOLUME 17, NO. 1 28	 FDIC BANKING REVIEW 
 

Limited-Purpose Banks 

Prospects 

As stated above, the number of insured institutions 

participating in the subprime market and the dollar amount of subprime loans have fallen in recent 

quarters. Several factors may have led to this 

decreasing trend. First, some participants may 

have exited the market because they were performing poorly. This hypothesis is consistent with the 

result discussed above—that while some lenders 

were exiting the subprime market, the ones 

remaining have been outperforming the industry 

in recent periods. It may be that success in subprime lending requires an institution to have certain expertise and resources. 

Second, increased capital requirements may have 

effectively eliminated the advantage that insured 

banks enjoyed by participating in the subprime 

Figure 5 

A. Subprime Lenders 

Average Net Noninterest Income Income/Asset (percent) 

8 

6 

4 

2 

0 

-2 

-4 

Noninterest Expense 

Noninterest Income 

Net Noninterest Income 

9/99 12/99 3/00 6/00 9/00 12/00 3/01 6/01 9/01 12/01 3/02 6/02 9/02 12/02 3/03 6/03 9/03 12/03 

B. All Banks
 

Average Net Noninterest Income
 Income/Asset (percent) 

8 

6 

4 

2 

0 

-2 

-4 

Noninterest Expense 

Noninterest Income 

Net Noninterest Income 

market. Typically, insured banks hold lower capital than their nonbank counterparts (consumer 

finance companies and mortgage lenders). Thus, 

insured banks enjoyed an advantage in competing 

against the nonbank financial institutions in the 

subprime market. By holding lower capital, the 

insured institutions incurred a lower cost than 

their nonbank counterparts in making subprime 

loans. However, recent regulatory and supervisory 

changes may have effectively eliminated this 

advantage. 

Greater supervisory scrutiny of subprime lenders’ 

capital adequacy is well justified. Concern has 

been rising that subprime lending activities are 

accompanied by significant risks. A number of 

institutions have failed, while others have experienced large losses in recent years as a result of their 

participation in the subprime market. Among the 

Figure 6 

A. Subprime Lenders 

Average ROA/ROE 

Rate of Return (percent) 

16 

12 

8 

4 

0 

ROE 

ROA 

9/99 12/99 3/00 6/00 9/00 12/00 3/01 6/01 9/01 12/01 3/02 6/02 9/02 12/02 3/03 6/03 9/03 12/03 

B. All Banks
 

Average ROA/ROE
 Rate of Return (percent) 

16 

12 

8 

4 

0 

ROE 

ROA 

9/99 12/99 3/00 6/00 9/00 12/00 3/01 6/01 9/01 12/01 3/02 6/02 9/02 12/02 3/03 6/03 9/03 12/03 

Note: Based on 147 observations during June 2000 instead of 148 observations. One institution

 was taken out for this calculation due to its extreme values. 

9/99 12/99 3/00 6/00 9/00 12/00 3/01 6/01 9/01 12/01 3/02 6/02 9/0212/02 3/03 6/03 9/03 12/03 

Note: 	Based on 147 observations during June 2000 instead of 148 observations. One institution

 was taken out for this calculation due to its extreme values. 

FDIC BANKING REVIEW	 29 2005, VOLUME 17, NO. 1 
 

 

 

Limited-Purpose Banks 

failed subprime lenders have been Superior Bank of 

Chicago, First National Bank of Keystone, NextBank, 

and Pacific Thrift and Loan Company. Alexander, 

Grimshaw, McQueen, and Slade (2002) give examples of banking institutions that have experienced 

large losses in recent years. First Union National 

Bank closed its acquired subprime lender, The Money 

Store, and took a $2.8 billion restructuring charge in 

2000. In 2001, Bank of America announced its exit 

from the subprime lending market and sold its $22 

billion subprime loan portfolio and took a large 

restructuring charge. 

In general, subprime lenders have poor asset quality. 

As Table 4 shows, non-performing and non-accrual 

loans are substantially higher at subprime lenders 

than at a typical bank. Similarly, the average gross 

charge-offs were nine times those of a typical bank. 

In response, the bank regulators have begun to 

require more capital for subprime loans. This is both 

to ensure that banks’ capital matches the risks they 

carry and to help ensure the survival of these institutions. 

Table 4 

Mean Values of Size, Balance-Sheet Ratios, 

Asset Quality Ratios, and Growth Measuresa 

(as of December 31, 2003) 

(1) (2) (3) (4) 

Credit Card Internet Subprime All Banks 

Banks Banks Lenders and Thrifts 

Assets (in $1,000s) 9,677,284 3,490,314 4,008,279 988,648 

Balance Sheet Ratios 

Equity 20.6 9.3 11.8 11.5 

Noncore funding 54.6 44.5 27.7 19.5 

Liquid assets 17.1 27.8 26.1 34.3 

Loans & long-term securities 74.3 71.9 74.6 69.7 

Asset Quality Ratios 

Non-performing & non-accruals 4.9 1.1 4.0 1.4 

Gross charge-offsb 6.3 0.3 2.7 0.3 

Provision for loan lossesb 4.6 0.2 1.8 0.3 

Growth Measures (in percent) 

Asset growth 47.7 20.3 13.6 9.8 

Equity growth 18.9 2.5 10.5 0.5 

Loan growth 42.3 30.5 16.0 15.4 

No. of observations 36 15 116 9181 

a The variable Assets is expressed in $1000s. The growth measures are one-year change 

(in percent) in assets, equity, and loans. The remaining variables are expressed as a 

percentage of assets. 

b Gross charge-offs and provision for loan losses are merger-adjusted four-quarter totals. 

The bank regulators note that minimum capital 

requirements apply to loan portfolios that are 

less risky than the subprime loans. Therefore, 

the subprime lenders are expected to hold higher capital ratios and to quantify the additional 

capital needed for subprime lending activities. 

In 2001, the banking regulators noted that 

“...[g]iven the higher risk inherent in subprime 

lending programs, examiners should reasonably 

expect, as a starting point, that an institution 

would hold capital against such portfolios in an 

amount that is one and one half to three times 

greater than what is appropriate for non-subprime assets of a similar type.”6 

Moreover, because subprime lenders are active 

participants in securitizations, the recently 

established risk-based capital requirements on 

recourse obligations, residual interests, and 

direct credit substitutes for banks indirectly 

affect subprime lenders. 

There is some evidence that these supervisory 

and regulatory measures have led to an increase 

in the amount of capital held by subprime 

lenders. For instance, these lenders’ average 

capital-to-assets ratio was 11.8 percent in 

December 2003, compared with 9.3 percent in 

September 1999. 

The measures undertaken by bank regulatory 

agencies may have effectively leveled the playing field for different lenders in the subprime 

market. Consequently, the advantage banking 

institutions used to enjoy in the subprime market may have largely disappeared. The fall in 

the number of subprime lenders and in the dollar amount of subprime loans held by these 

lenders may reflect their response to the new 

regulatory regime. 

Both market forces and regulatory changes 

appear to be reducing insured institutions’ participation in the subprime market. Institutions 

that can effectively manage the elevated risks 

associated with subprime lending and also be 

6 See OCC et al. (2001). 

2005, VOLUME 17, NO. 1 30 FDIC BANKING REVIEW 
Limited-Purpose Banks 

profitable will continue extending credit to the 

subprime market. It is not clear whether insured 

banks’ participation in this market has already 

begun to stabilize or will decrease further. 

As a public-policy goal, the active participation of 

insured institutions in the subprime market may be 

important for promoting the availability of credit 

to all households. At the same time, it is important for these institutions to recognize the risks 

associated with subprime lending and to enhance 

risk-management practices accordingly. 

Internet Banks 

A small number of banks deliver banking services 

primarily on-line. In theory, Internet banking 

offers attractive features. By eliminating the physical branches and employing fewer workers, Internet banks can reduce overhead expenses (DeYoung 

[2001, 2002]) and salary expenses. Orr (2001) 

refers to a study by Booz, Allen & Hamilton that 

reports that a typical transaction over the Internet 

costs about a penny, compared with $1.07 at a fullTable 5 

Internet Primary Banks (as of May 20, 2002) 

Date 

Name Chartereda Type of Bank Offices 

Nexity Bank 1968 1 full-service brick-and-mortar office 

National American Bank 1983 1 full-service brick-and-mortar office 

Etrade Bank 1933 1 full-service brick-and-mortar office 

Netbank 1988 1 full-service cyber office 

Principal Bank 1998 1 full-service cyber office 

First Internet Bank 

of Indiana 1998 1 full-service cyber office 

Ebank 1998 1 full-service brick-and-mortar office 

GMac Bank 2001 1 full-service brick-and-mortar office, 

2 limited-service administrative offices 

BMW Bank of North 

America 1999 1 full-service brick-and-mortar office 

Deepgreen Bank 1999 1 full-service brick-and-mortar office 

Lydian Private Bank 2000 1 full-service brick-and-mortar office 

The Bancorp Bank 2000 1 full-service brick-and-mortar office 

ING Bank FSB 2000 2 full-service brick-and-mortar offices 

Bank of Internet USA 2000 1 full-service brick-and-mortar office 

Earthstar Bank 2001 1 full-service brick-and-mortar office 

a The chartered date is not necessarily the date these institutions entered the Internet 

banking business. Some institutions switched from offering banking services via 

branches, telephone, fax, and mail to Internet banking services at a later date. 

service teller window and $0.27 at an ATM. Furthermore, with an Internet-based distribution 

channel, Internet banks can easily enter new geographic markets without starting new branches. 

Thus, Internet banks can grow more rapidly. 

Likewise, Internet banking benefits customers by 

offering services at a low cost. The banks’ savings 

in overhead and salary expenses can be transferred 

to their customers. The banks can offer higher 

rates to depositors while charging lower rates to 

borrowers. According to one Internet banker, savings in fixed capital can make a difference of 

50–70 basis points of interest on savings accounts.7 

Moreover, Internet banking offers convenience to 

customers, for they can perform many types of 

banking transactions—for example, checking their 

account balances, paying bills, and applying for 

loans—on-line at any time without having to travel. 

To reap such benefits, some people have started 

Internet primary banks while some existing banks 

have entered the Internet banking business. Table 

5 lists the Internet primary banks included in this 

study. The first column reports the dates these 

banks were chartered: the dates range from 1933 to 

2001, although most were chartered in or after 

1998. The chartered date is not necessarily the 

date these institutions entered the Internet banking business—some institutions switched from 

offering banking services via branches, telephone, 

fax, and mail to Internet banking services. The 

second column describes the service facilities of 

these institutions. It is noteworthy that only three 

banks have exclusively cyber offices. Others maintain one or two full-service brick-and-mortar 

offices. It may well be that physical branches are 

made available for types of transactions that are 

impossible to perform via the Internet, such as 

withdrawing cash or depositing checks. 

Internet banks are bigger than the industry average. For instance, in December 2003, average 

total assets of Internet banks were $3.5 billion, 

compared with $1 billion for all banks and thrifts. 

7 See Orr (1999). 

FDIC BANKING REVIEW 31 2005, VOLUME 17, NO. 1 
Limited-Purpose Banks 

To achieve such size, Internet banks have been 

growing rapidly. Table 4 shows that their average 

asset growth is 20.3 percent, and loan growth is 

30.5 percent. To achieve such rapid growth, Internet banks are relying on expensive and volatile 

funds (average noncore funds amount to 44.5 percent of their assets). 

The large size and rapid growth of Internet banks 

may be associated with these institutions’ heavy 

reliance on technology. They may have to pass a 

certain size threshold in order to earn enough revenues to cover the high fixed costs associated with 

technology-intensive production processes. Earlier 

studies found that technology-intensive production 

processes exhibit economies of scale. Thus, these 

institutions are growing rapidly to take advantage 

of the benefits of scale economies associated with 

technologically intensive production processes. 

Financial Performance 

Contrary to prediction, Internet banks have not 

proven to be very profitable. In fact, their performance is inferior to that of the industry. As of 

December 2003, for instance, Internet banks had 

an average ROA of 0.7 percent, compared with 1.0 

percent for the industry. Moreover, the average 

ROE of Internet banks was 8.8 percent, compared 

with the industry average of 10.0 percent. 

These banks’ low profitability is attributed to both 

low net interest and low noninterest income. 

Internet banks earn lower interest income than the 

industry. Some Internet banks buy loans on the 

wholesale market instead of originating them, and 

thus earn lower interest income. Internet banks 

also incur higher interest expense by offering higher rates on deposits and relying more heavily on 

expensive sources of funds. As table 4 shows, in 

December 2003 noncore funds amounted to 44.5 

percent of total assets at Internet banks, compared 

with 19.5 percent at a typical bank. Such heavy 

reliance on “hot” money may have resulted from 

the failure to attract a core client base (Hine and 

Phillips [2003]) and from the attempt to achieve a 

certain size through rapid growth. 

Compared with the industry, Internet banks also 

earn lower net noninterest income. The reason is 

that although they earn higher noninterest 

income, they also incur higher noninterest 

expense; the technology-intensive production 

process used by Internet banks is likely to have 

high fixed costs. (Banks must generate a large 

enough volume to offset the high fixed costs.) 

Moreover, Internet banks spend more on salary 

expenses. It may well be that Internet delivery systems require fewer but better-skilled employees 

resulting in higher salary expenses (DeYoung 

[2001]). 

Internet banks are also likely to spend more on 

marketing and advertising to attract customers to 

their Web sites. Unlike a branching bank, an 

Internet bank does not benefit from free advertising whenever a potential customer walks or drives 

past it. Instead, Internet banks have to purchase 

advertising to attract new customers to their Web 

sites. DeYoung (2001) refers to a study by Rosen 

and Howard (2000) that finds that compared with 

the average brick-and-mortar retailer, the average 

on-line retailer spends more than ten times as 

much per purchase on marketing and advertising. 

Other expenses include contracts with vendors to 

service and maintain the Web site, and payments 

to ATM networks. 

In addition, Internet banks incur unanticipated 

costs by offering physical delivery channels. As 

noted above, the majority of Internet banks have 

one or two physical branches, probably because 

customers need to perform certain transactions at 

physical locations. 

Prospects 

Internet banks underperform brick-and-mortar 

banks, with little evidence of improvement over 

time. This situation may be attributed to a number of factors. For one thing, Internet banks suffer 

from low consumer demand. The low volume of 

business is partly explained by the fact that most 

Internet banks were established only recently. 

Like branching de novo banks, newly established 

2005, VOLUME 17, NO. 1 32 FDIC BANKING REVIEW 
Limited-Purpose Banks 

Internet banks need time to attract depositors, find 

borrowers who are good credit risks, and find other 

profitable opportunities. Low business volume is 

also attributed to limited consumer demand for 

Internet services (Orr [2001]). For many consumers, a technology-driven Internet delivery 

channel can be both intimidating and frustrating. 

In addition, transactions such as making deposits 

or withdrawing cash are impossible to perform via 

the Internet. Moreover, automated banking services lack person-to-person contact and do not create customer loyalty. 

Relative to branching banks, Internet banks are 

also at a competitive disadvantage in lending to 

small businesses because they lack the means of 

building long-term relationships with borrowers. 

Small businesses tend to be informationally 

opaque, with little public information available. 

Banks can alleviate information asymmetries and 

agency costs by building a relationship with the 

borrower. Through repeated interactions, banks 

can gain private information on borrowers and can 

better monitor the borrower to prevent unanticipated risk-taking activities. 

In contrast, Internet banks use automated underwriting procedures for generating loans and manage risk by diversifying large pools of these loans. 

Through such transaction-lending practices, Internet banks fail to build relationships with borrowers. Consequently, Internet banks are less likely to 

gain proprietary information about their borrowers 

and less likely to monitor them effectively. Hence, 

Internet banks are at a disadvantage compared 

with branching banks. 

For these reasons, Internet banks can be expected 

to have only a modest chance of success. 

Conclusion 

Limited-purpose banks challenge the traditional 

notion of banking. Although relatively few in 

number, they have unique business models and 

product mixes that have attracted considerable 

attention. This study has described their business 

models, evaluated their performance and risk characteristics, and discussed their prospects. 

Some business strategies adopted by limited-purpose banks lead to superior financial performance. 

For instance, credit card banks are highly profitable compared with both other limited-purpose 

banks and the industry benchmark. Because of the 

inherent riskiness of unsecured credit, credit card 

banks have poor asset quality and high default 

rates. However, their interest and noninterest 

income is sufficiently high, leading to high profits. 

Given their volatile yet robust profitability, credit 

card banks are likely to have found a permanent 

place in the banking sector. Moreover, the 

increasing trend of consolidation suggests that a 

few large institutions will remain and dominate the 

sector. 

In contrast, other business models show lackluster 

performance. Subprime lenders earn higher interest income than the industry average, yet poor 

asset quality diminishes those earnings. Moreover, 

recent initiatives by the banking regulators impose 

higher capital requirements on subprime loans and 

may have eliminated the advantage the insured 

banks enjoyed in the subprime market. Consequently, the number of subprime lenders has been 

falling in recent years. It is reasonable to expect 

that bank participation in subprime lending will 

remain at reduced levels, if it does not decline further. 

Similarly, Internet banks have not proven to be 

profitable. They incur high costs in acquiring and 

keeping customers and in using technology-intensive production processes. Moreover, Internet 

banks fail to build relationships with borrowers and 

thus forgo an informational advantage with respect 

to their borrowers. The evidence to date appears 

to suggest that Internet banks have only a modest 

chance of success. 

The evidence presented in this paper suggests that 

some limited-purpose banks may have little success 

in the long run. But although some of these business lines may be less successful as free-standing 

operations, they may be suitable as part of a larger 

2005, VOLUME 17, NO. 1 33 FDIC BANKING REVIEW 
Limited-Purpose Banks 

bank. Citibank, for example, offers all such services. 

Integrating such disparate business lines and offering various financial products and services may 

lead to economies of both scope and scale; and 

institutions with diversified asset portfolios may 

then achieve more stable streams of income. 

Moreover, institutions offer convenience to their 

customers by providing different financial products 

and services in one place. 

The trend of institutions offering multiple services 

and products is already evident. For instance, 

increasing numbers of banks are using the “clickand-mortar” strategy of adding an Internet site to 

their physical branches. Through the Internet site, 

customers can perform banking transactions such 

as accessing accounts and transferring funds online. In addition, customers can make deposits, 

apply for a loan, or withdraw cash from their 

accounts in physical branches or at ATM networks. Gup (2003) refers to studies that document 

the preference by customers of large banks (such as 

Morgan Online and Bank of America) for a combination of Internet-based tools and a close relationship with a personal banker. Thus, diversified 

banks offering multiple services may well be the 

wave of the future. 

2005, VOLUME 17, NO. 1 34 FDIC BANKING REVIEW 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Limited-Purpose Banks 

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