1. Credit Scoring and Access to Credit Community Development Policy Summit June 12, 2008 The views expressed do not necessarily represent those of the Board of Governors of the Federal Reserve System or members of the staff.

2. Credit Scoring: History in Brief Credit scoring is a statistical technology that quantifies credit risk –Primary goal is to rank-order individuals, distinguishing lower from higher risks Credit scoring was developed to address the need for quick, accurate, inexpensive, and consistent credit evaluation Credit history or bureau-based scores -- are based exclusively on credit record data from the credit reporting agencies Credit scores are widely used to: –evaluate and price credit –identify prospective borrowers for solicitation –manage existing accounts Credit scoring used in insurance, employment, utilities and housing

3. Concerns about the Effects of Credit Scoring on Access to Credit Credit scoring may have adverse effects on certain populations, particularly minorities Some factors used to estimate credit scores may have an adverse effect on certain groups Automated technologies may disadvantage individuals with nontraditional credit experiences Judgmental evaluations may be better able to detect errors or inaccuracies

4. Request for a Study Section 215 of the Fair and Accurate Credit Transaction Act (Fact Act) asked for a study of: –The effects of credit scoring on the availability and affordability of credit and insurance –Whether the use of credit scoring and credit-based insurance scores impact on the availability and affordability of credit to different populations including: The extent to which consideration of certain factors in scoring models could have adverse effects on protected classes The extent to which scoring systems could achieve comparable results through the use of factors with less negative impact

5. Approach for the Study Focus on generic credit history or bureau-based scores -- scores based exclusively on credit record data from the three national credit reporting agencies Two broad approaches used in study: –First, gather information on the effects of credit scoring on the availability and affordability of credit from public comments and previous research; –Conduct an analysis of credit use and holding using data from the 1983-2004 waves of the Surveys of Consumer Finances This is the period when credit history scores became widely used in underwriting and solicitation of credit

6. Approach for the Study (continued) Second, to assess the effects of scoring on different populations created a unique database and estimated a generic credit scoring model –Data includes 301,000 nationally representative credit records, credit scores, and demographic information from TransUnion –Credit records as of June 2003 and updated as of December 2004 –Data includes 312 credit characteristics covering all aspects of credit records –TransUnion data included 2 commercially available credit scores –Credit records do not include personal demographic information –Demographic information from applications for a Social Security Card and from a demographic information company Fed staff developed a credit history scoring model representative of industry approach

7. The FRB Base Model The FRB Base model consists of three scorecards: –Thin file (2 credit accounts or less) --10 percent of sample –Clean file (no record of a serious delinquency, public record or collection account) -- 59 percent of sample –Major Derogatory file (at least one serious delinquency, public record or collection account) -- 31 percent of sample FRB model includes 19 different credit characteristics FRB model has similar measures of fit and predictive power as those reported by industry modelers

8. Major Findings:1 Evidence provided by commenters, previous research, and the present analysis supports the conclusion that credit has become more available over the past quarter- century As a cost- and time-saving technology that became a central element of credit underwriting, marketing and account maintenance, credit scoring likely has contributed to improved credit availability and affordability

9. Major Findings: 2 Mean credit scores differ widely across groups: –Blacks and Hispanics; younger individuals; single individuals and those residing in lower-income areas or those with higher percentages of minority individuals have lower mean scores Some of the differences in credit scores across groups were reduced, at least in part, by accounting for other demographic (including an estimate of income) and location characteristics. However, significant differences remain unexplained

10. Incidence of Credit Record Item by Population Group Mean Trades% Public Rec.% Medical% Other Col.% 90+ Delinq. SSA Race White 171214 Black 1327364836 Hispanic 14 212823 Asian 16871012 National Origin Foreign-born 15111216 Recent Immigrants 115913 Total 1513161816

11. Mean Trades% Public Rec.% Medical% Other Col.% 90+ Delinq. Sex Male 1615161816 Female 16131718 SSA Age Under Age 30 99212921 Age 30 to 39 1719232824 Age 40 to 49 191819 Age 50 to 61 191513 15 Age 62 and Older 147757 Total 1513161816 Incidence of Credit Record Item by Population Group

12. Distribution of TransRisk Score by Race Mean Score White54.0 Black25.6 Hispanic38.2 Asian54.8

13. Distribution of TransRisk Score by Age Mean Score Under 3034.3 30 – 3939.8 40 – 4946.9 50 - 6154.5 62 and over68.1

14. Distribution of TransRisk Score by Sex/Marital Status Mean Score Married Male 55.7 Single Male 43.4 Married Female 57.5 Single Female 44.8

15. Major Findings: 3 The credit history scores are predictive of credit risk for the population as a whole –Over any credit-score range, the higher (better) the credit score, the lower the observed incidence of default The credit history scores are predictive of credit risk for all major demographic groups –For each population score/performance relationship is monotonic and downward sloping

16. Major Findings: 3 (continued) In some cases, the analysis found differences across groups in average performance for individuals with the same score –Blacks, single individuals, and those living in lower-income or minority neighborhoods underperform (that is, given score their default rate exceeds the rate for the rest of their respective demographic group –By contrast, Asians, married individuals, foreign born individuals (particularly recent immigrants) and those living in higher-income areas overperform Differences in performance residuals were reduced by accounting for the limited factors available for this study; but, substantial unexplained differences remained

17. Any Account Performance and TransRisk Scores by Race Mean Residual White Black5.6 Hispanic1.7 Asian-2.1

18. Any Account Performance and TransRisk Scores by Age Mean Residual Under 301.5 30 – 39-0.2 40 – 49-0.4 50 - 61-0.7 62 and over-0.3

19. Any Account Performance and Scores by Sex/Marital Status Mean Residual Married Male -1.2 Single Male 0.4 Married Female -1.1 Single Female 0.8

20. Major Findings: 4 For given credit scores, credit outcomesincluding measures of credit acquisition, availability, and affordabilitydiffer for different demographic 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, some (in most cases all) differencessometimes substantialoften remained

21. Evidence on Credit Outcomes from Credit Records Credit records provide data on: –incidence of new credit (loans between July 2003 and December 2004), –inferred denial rates and –estimated interest rates paid for-- mortgages, autos and other installment loans Results: individuals with lower scores are (1) less likely to get new credit; (2) experience higher inferred denial rates; (3) pay higher interest rates –Few differences across racial groups; but, after controls blacks experience higher denial rates compared to non-Hispanic whites and pay somewhat higher interest rates. Asians generally pay lower interest rates

22. TransRisk Score New-account Acquisition

23. TransRisk Score Inquiry-based Proxy for Denials

24. TransRisk Score Auto Loans Interest Rate

25. Major Findings: 5 Results obtained with the FRB Base model 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 –These credit characteristics all relate to age of accounts 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

26. Assessing Differential Effect Congress asked whether credit scoring and the factors in models have a negative effect on some populations and whether such effects could be mitigated by changes in the models –As defined in the study, a credit characteristic in a model is said to have a differential effect if the weight assigned to the characteristic in a model differs from the weight that would be assigned in a model estimated in a demographically neutral environment –Also, a model can be said to embed differential effect if the mean credit scores for a population change markedly when reestimated in a demographically neutral environment

27. Assessing Differential Effect (cont.) 4 types of analyses were conducted using the FRB Base model –Examine the correlation between credit characteristics and demographics and performance –Examine the possible differential effects of each characteristic in the FRB Base model by dropping characteristics from the model and measuring the resulting changes in scores for different groups –Inferences about the effect of credit characteristics not included in the FRB Base model are drawn by adding these characteristics and measuring the resulting changes in scores for different groups –Compare scores estimated from the FRB Base model and the demographically neutral environment

28. Correlations Between the 312 Credit Characteristics, Loan Performance and Demographic Characteristics For blacks, the only credit characteristics showing significant correlations are those representing past payment performance; all these are also strongly correlated to performance For age, some credit characteristics are highly correlated, nearly all involving length of credit history For sex, characteristics involving retail or store accounts are correlated with sex, but are only minimally related to performance and none are in the FRB Base model

29. Credit Characteristics and Correlation with Performance and Demographics Legend: Red - Types of Credit in Use; Black - Payment History; Green - Length of Credit History; Light Blue - Amounts Owed; Blue - New Credit

30. Credit Characteristics and Correlation with Performance and Demographics Legend: Red - Types of Credit in Use; Black - Payment History; Green - Length of Credit History; Light Blue - Amounts Owed; Blue - New Credit

31. Implications Failure to find differential effect for race, and sex, and only small effects for age, suggests that fair lending concerns regarding credit scores should focus primarily on whether scores are used consistently across groups (e.g., discretion, overrides). Also, whether the credit scores are used in manner consistent with their predictiveness. The research findings regarding recent immigrants underscores the importance of continuing efforts to expand the types of information considered in scoring models.