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.

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

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

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

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

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

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

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

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

Incidence of Credit Record Item by Population Group

Mean Trades % Public Rec. % Medical % Other Col. % 90+ Delinq.

SSA Race

White 17 12 14 14 14

Black 13 27 36 48 36

Hispanic 14 14 21 28 23

Asian 16 8 7 10 12

National Origin

Foreign-born 15 11 12 16 16

Recent Immigrants 11 5 9 13 13

Total 15 13 16 18 16

Mean Trades % Public Rec. % Medical % Other Col. % 90+ Delinq.

Sex

Male 16 15 16 18 16

Female 16 13 17 18 18

SSA Age

Under Age 30 9 9 21 29 21

Age 30 to 39 17 19 23 28 24

Age 40 to 49 19 18 19 19 19

Age 50 to 61 19 15 13 13 15

Age 62 and Older 14 7 7 5 7

Total 15 13 16 18 16

Incidence of Credit Record Item by Population Group

Distribution of TransRisk Score by Race

Mean

Score

White 54.0

Black 25.6

Hispanic 38.2

Asian 54.8

Distribution of TransRisk Score by Age

Mean

Score

Under 30 34.3

30 – 39 39.8

40 – 49 46.9

50 - 61 54.5

62 and over 68.1

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

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

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

Any Account Performance and TransRisk Scores by Race

Mean

Residual

White -1.0

Black 5.6

Hispanic 1.7

Asian -2.1

Any Account Performance and TransRisk Scores by Age

Mean

Residual

Under 30 1.5

30 – 39 -0.2

40 – 49 -0.4

50 - 61 -0.7

62 and over -0.3

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

Major Findings: 4

• For given credit scores, credit outcomes—including measures of credit acquisition, availability, and affordability—differ 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) differences—sometimes substantial—often remained

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

TransRisk ScoreNew-account Acquisition

TransRisk ScoreInquiry-based Proxy for Denials

TransRisk ScoreAuto Loans Interest Rate

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

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

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

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

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

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

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.