Credit scoring is a statistical technology that quantifies the credit risk posed by a prospective or current borrower. The technique is widely used to evaluate applications for credit, identify prospective borrowers, and manage existing credit accounts. The large savings in cost and time that have accompanied the use of credit scoring are generally believed to have increased access to credit, promoted competition, and improved market efficiency.
The expansion of the use of credit scoring, including by the adaptation of its methodology to insurance markets, has been accompanied by concerns that it may affect the availability and affordability of credit and insurance and that factors included in credit-scoring models may have adverse effects on certain populations, particularly minorities. Section 215 of the Fair and Accurate Credit Transactions Act of 2003 (Fact Act) directs the Federal Reserve Board and the Federal Trade Commission (FTC) to study how credit scoring has affected the availability and affordability of credit and insurance, to determine the relationship between credit scores and actual credit losses and insurance claims, and to determine how these relationships vary for the population groups protected under the Equal Credit Opportunity Act (ECOA).1 In addition, section 215 directs the Board and the FTC to study the extent to which the consideration of certain factors included in credit-scoring and insurance-scoring models could have a negative or differential effect on populations protected under ECOA and the extent to which alternative factors could be used in credit scoring to achieve comparable results with less negative effect on protected populations
In preparing the study, the Federal Reserve took the lead in assessing the effects of credit scoring on credit markets, the subject of the present document; the FTC took the lead in the area of insurance and has issued a separate report on that topic.
In the broadest terms, the findings of the Federal Reserve study are as follows:
(1) The credit history scores evaluated here are predictive of credit risk for the population as a whole and for all major demographic groups. That is, over any credit-score range, the higher (better) the credit score, the lower the observed incidence of default. These conclusions are limited to credit history scores, that is, scores calculated exclusively on the basis of individuals' credit records as assembled by the three national credit-reporting agencies (Equifax, Experian, and TransUnion). Other kinds of credit scores were not studied here.
(2) Results obtained with the model estimated especially for this study suggest that the credit characteristics included in credit history scoring models do not serve as substitutes, or proxies, for race, ethnicity, or sex. The analysis does suggest, however, that certain credit characteristics serve, in part, as limited proxies for age. A result of this limited proxying is that the credit scores for older individuals are slightly lower, and those of younger individuals somewhat higher, than would be the case had these credit characteristics not partially proxied for age. Analysis shows that mitigating this effect by dropping these credit characteristics from the model would come at a cost, as these credit characteristics have strong predictive power over and above their role as age proxies.
Evidence also shows that recent immigrants have somewhat lower credit scores than would be implied by their performance. This finding appears to derive from the fact that the credit history profiles of recent immigrants resemble those of younger individuals, whose credit performance tends to be poor relative to the rest of the population. Expanding the information supplied to credit-reporting agencies to include rent, other recurring bill payments, nontraditional uses of credit, and the credit histories of the foreign-born in their countries of origin may provide a broader picture of the credit experiences of recent immigrants and other individuals.
(3) Different demographic groups have substantially different credit scores, on average. For example, on average, blacks and Hispanics have lower credit scores than non-Hispanic whites and Asians, and individuals younger than age 30 have lower credit scores than older individuals. Also, for given credit scores, credit outcomes--including measures of loan performance, availability, and affordability--differ for different demographic groups. Data limitations (for example, regarding individuals' wealth, employment, and education) prevented a complete assessment of these differences in score averages and outcomes among groups. The study found that many of these differences were reduced, at least in part, by accounting for the limited factors available for this study; however, differences--sometimes substantial--often remained.
(4) Evidence provided by commenters, previous research, and the present analysis supports the conclusion that credit has become more available over the past quarter-century. Credit scoring, as a cost- and time-saving technology that became a central element of credit underwriting during that period, likely has contributed to improved credit availability and affordability. However, in part precisely because the use of credit scoring became widespread decades ago, only limited direct information could be obtained on the contribution of credit scoring regarding availability and affordability. The increase in credit availability appears to hold for the population overall as well as for major demographic groups, including different races and ethnicities. There is no compelling evidence, however, that any particular demographic group has experienced markedly greater changes in credit availability or affordability than other groups due to credit scoring.
Despite concerns about the potential effects of credit scoring on minorities or other groups, little research has been conducted on the issue, largely because of a lack of data linking credit scores to race, ethnicity, and other pertinent demographic information about individuals. With the exception of dates of birth, the credit records maintained by the credit-reporting agencies, which serve as the basis for most credit-scoring models, do not include any personal demographic information, and federal law generally prohibits the collection of such data on applications for nonmortgage credit. Even in the context of mortgage credit, for which some creditors are required to collect information on race, ethnicity, and sex, little information is publicly available.
This report was prepared using two types of information. The first type was gathered from public comments submitted for the report and from a review of previous research and surveys. The second type came from unique research conducted by the staff of the Federal Reserve Board specifically for this study. In that research, the Board's staff created a database that, for the first time, combines information on personal demographics collected by the Social Security Administration (SSA) with a large, nationally representative sample of the credit records of individuals. The sample comprised the full credit records of 301,536 anonymous individuals drawn in June 2003 and updated in December 2004 by TransUnion LLC (TransUnion), one of the three national credit-reporting agencies.2
Because the data set consisted of the credit records of the same individuals on two dates (June 30, 2003, and December 31, 2004), the Federal Reserve's staff was able to construct measures of loan performance, credit availability, and credit affordability and to create its own credit-scoring model (the FRB base model). Besides the FRB score created for this study, the data supplied by TransUnion for each individual in the database included two commercially generated credit scores--the TransRisk Account Management Score (from TransUnion) and the VantageScore (from VantageScore Solutions LLC).3 The design of the FRB base model followed general industry practice to the extent possible. The three credit scores, together with the unique combination of credit and demographic information in the data set created for this purpose, allowed the Federal Reserve to address the questions posed by the Congress.
The limited available evidence, including from public comments and previous research, suggests that credit scoring has increased the availability and affordability of credit. The basic reason is that credit scoring allows creditors to quickly and inexpensively evaluate credit risk and to more readily solicit the business of their competitors' customers regardless of location.
Credit scoring likely increases the consistency and objectivity of credit evaluation and thus may help diminish the possibility that credit decisions will be influenced by personal characteristics or other factors prohibited by law, including race or ethnicity. Credit scoring also increases the efficiency of consumer credit markets by helping creditors establish prices that are more consistent with the risks and costs inherent in extending credit. By providing a low-cost, accurate, and standardized metric of credit risk for a pool of loans, credit scoring has both broadened creditors' access to capital markets and strengthened public and private scrutiny of lending activities.
The data assembled for this study are used to investigate the variation in credit scores across populations and the relationship between credit scores and loan performance, availability, and affordability across populations.
Credit scores differ among subpopulations: Blacks, Hispanics, single individuals, those younger than age 30, and individuals residing in low-income or predominantly minority census tracts have lower credit scores than other subpopulations defined by race or ethnicity, marital status, age, or location. Group differences in credit scores are narrowed, but not always eliminated, when differences in personal demographic characteristics, in residential location, or in a census-tract-based estimate of an individual's income are taken into account.
The analysis conducted for this study finds that credit scores consistently predict relative loan performance within all population groups; that is, for all populations, the percentage of individuals experiencing a serious delinquency on one or more of their credit accounts consistently declines as credit scores increase.
The analysis also finds that some groups perform worse (experience higher rates of serious delinquency) on their credit accounts, on average, than would be predicted by the performance of individuals in the broader population with similar credit scores. For example, on average, blacks perform worse than other racial and ethnic groups with similar credit scores. Similarly, single individuals and those residing in predominantly black or low-income census tracts perform worse on their loans than do their complementary demographic groups with similar credit scores. In contrast, the loan performance of Asians, married individuals, foreign-born individuals (particularly, recent immigrants), and those residing in higher-income census tracts was better than the performance predicted by their credit scores. The results hold after controlling for the other personal demographics of these individuals and for an estimate of the individuals' incomes and locations; other factors that could be important, such as differences in employment experience, were not available.
The study also finds that credit scores are consistently related to measures of loan pricing and loan denial rates inferred from credit inquires.4 That is, for all populations, interest rates derived from the terms reported for closed-end loans and average inferred denial rates consistently decline as credit scores increase. As was the case for loan performance, some differences were observed across population groups after controlling for credit score: Most notably, younger individuals appear to experience somewhat higher inferred denial rates than older individuals; blacks appear to pay somewhat higher interest rates on auto and installment loans than do non-Hispanic whites; and Asians pay interest rates that, on average, are typically lower than, or about the same as, those paid by non-Hispanic whites across all loan categories for which rates could be estimated. Data limitations prevent a full assessment of the reasons for the remaining differences in credit outcomes.
This study reviewed the extent to which the consideration or lack of consideration of certain factors by credit-scoring systems could result in a negative or positive differential effect for different populations. By law and regulation, an individual's personal characteristics--such as race or ethnicity, national origin, sex, and, to a limited extent, age--must be excluded from credit-scoring models. A concern exists that, despite that prohibition, a credit characteristic may be included in a model not because it helps predict performance but because it is a substitute, or proxy, for a demographic characteristic that is correlated with performance.
The analysis of the data assembled for this report found that few credit characteristics, including those in the FRB base model, were correlated with personal demographics and that therefore they were unlikely to serve as proxies for demographic characteristics. Credit characteristics related to the age of an individual's credit record are the primary exception. The data show that some of these characteristics are often highly correlated with age. In addition, certain pertinent aspects of the credit files of recent immigrants tend to resemble those of younger individuals because they have not had sufficient time to build an extensive credit history in the United States.
To examine more closely whether the credit characteristics appearing in the FRB base model are serving, at least in part, as proxies for race or age, the model was reestimated in race-neutral and age-neutral environments. In each case, the FRB base model was reestimated with samples limited to a single race or age population respectively; in those reestimations, any credit characteristics serving solely as a proxy for race or age should have little weight in the reestimated model. Credit characteristics that have both an independent effect on performance and a correlation with race or age would be expected to have significantly different weights (either larger or smaller) in the reestimated models.
Reestimating the FRB base model in a race-neutral environment had little effect on credit scores. The result suggests that none of the credit characteristics included in the model serve, to any substantive degree, as proxies for race or ethnicity. However, when the FRB base model was reestimated in an age-neutral environment, credit scores did change: Scores for recent immigrants and younger individuals fell, and scores for older individuals rose.* These results were traced to the inclusion of a specific credit characteristic, namely, that which specifies the length of an individual's credit history. Further analysis showed that this credit characteristic served in part as a proxy for age. However, because the characteristic also had significant predictive power in an age-neutral environment, the effect could not be mitigated simply by excluding the credit characteristic from the FRB base model. An alternative means of mitigating the differential effect of this characteristic would be to use the weights derived from the age-neutral model. Use of the credit characteristic in this manner removes the differential effects relating to age with less loss of model predictiveness than would occur if this credit characteristic were excluded from the model entirely.