Statement Of Calvin Bradford


Calvin Bradford and Associates, Ltd.

The wide-scale use of credit scoring represents a significant efficiency in the competitive world of mortgage finance. Both the Federal Reserve, by its regulations, and lenders, who use credit scoring, refer to it as an objective process as opposed to judgmental systems. The largest purveyor of credit scores, Fair, Isaac and Company, Inc., has continually maintained that its scores could not be discriminatory because they do not contain race as an explicit variable. All of these statements appear to support a confidence in the fairness and equality in the use of credit scoring that is, in fact, unwarranted.

Credit scoring has not been intentionally discriminatory in its typical uses. Nonetheless, regulators, researchers and the developers of credit scoring systems have all recognized that, on average, minorities have lower credit scores than majority populations. Therefore, the use of credit scoring systems will frequently have an overall discriminatory effect. Such an effect, however, is not illegal if it is based on an overriding business necessity and if there is no less discriminatory way to achieve the underwriting goal.

With the understanding that all credit scoring systems need to be calibrated to the particular population of each individual lender and re-evaluated periodically, I offer several representative examples of fair lending issues.

Most Rejected Applicants Are Not Expected to Default

Consider the example, which I have made extreme for the sake of clarity, of a lender who finds that 100 percent of the loans predicted to go into default under its scoring system fall below the score of 620. This lender would assume that using this scoring model is a great business benefit because he could be reasonably confident that the system would exclude all borrowers who might default. Therefore, let us assume that the lender rejects, or "cuts off," all applicants with scores under 620.

A scoring system is able to predict, for any cutoff score, the percentage of applicants at or below that score who are likely to go into default (the odds of defaulting), but it is not able to precisely identify which specific individuals will default. While 100 percent of those predicted to default may have scores under 620, there also are many other applicants with scores under 620 as well. Indeed, in our example and in reality, whenever a lender chooses a particular cutoff score, most of the applicants with scores below the cutoff are, in fact, not predicted to default. In fact, in our example, it is fair to assume that the odds of any particular applicant with a score below 620 defaulting might be only 10 percent. That is, 90 percent of those with scores below 620 would not be predicted to default.

Credit Scoring Systems Disproportionately Reject Minority Applicants

Most lenders and secondary investors, as well as those who develop and market scoring systems, agree that, overall, minorities do have lower credit scores than whites. Suppose that all minority applicants in a given market, but only some whites, have scores that fall below 620. Obviously, all minority applicants would be excluded by a 620 cutoff. The lender, however, would argue that this clearly disproportionate impact on minorities is not unlawfully discriminatory, because it is a justifiable business necessity.

To clarify further, let us suppose that 3 percent of all people with any score will default. Out of 100,000 applicants, this would be 3,000 applicants. Now suppose that, of those 100,000 applicants, 30,000 had scores under 620. If our system predicts that 10 percent of all applicants under 620 will default, then these 30,000 applicants would include the 3,000 who will default, as well as 27,000 others who will not.

In our example, if the entire population of applicants included 10,000 minorities, all 10,000 would have scores under 620. There also would be 90,000 whites in the population. Of these, 20,000 would have scores under 620, making up the total of 30,000 applicants with these scores that we have specified in our example. There also would be 70,000 whites with scores at or above 620. If the 3,000 borrowers who will default were spread proportionately between whites and minorities in the group with scores under 620, then 2,000 whites (10 percent) and 1,000 minorities (10 percent) would be predicted to default. There also would be 18,000 whites and 9,000 minorities with scores under 620 who would not be predicted to default.

In this case, 90 percent of all minorities would be rejected even though the scoring system predicted that they would not default. But, of the total of 90,000 whites, only 18,000 with scores under 620 will be rejected, even though the model predicts that they will not default. The disparate impact is clear. If all applicants under 620 are rejected, 90 percent of the minority population, but only 20 percent of the white population, will be rejected when the model predicts that they will not default on their loans.

Obviously this is an extreme example, but in reality, the difference is only one of degree. If the Equal Credit Opportunity Act regula-

it generally does not explain most of the loans that go into default over the life of the loan because most defaults and foreclosures

TABLE I: Summary of Calvin Bradford's Example

Total Borrowers

Rejects (Scores <620)

10% Will Default

90% Not Default (Scores <620)

% Rejected Based on Score but Not Default













tions permit using a credit scoring system— if it is statistically reliable, but prohibits a discriminatory impact, absent a clear business necessity—then where should the "necessity" threshold be set? In other words, what level of differential impact of rejected good minority applicants to rejected good white applicants is acceptable and what level crosses over into discrimination? Would it be acceptable in our example to reject all applicants with a score below 620 because of the ability to weed out all applicants expected to default, even if 90 percent of the rejected minorities would not be expected to default? Or, on the other hand, do we decide that unless a credit score can achieve a less discriminatory impact, it has not achieved enough validity to be accepted? Should we, for example, disallow systems having a discriminatory impact unless they at least predicted that more than 50 percent of those with scores below the cutoff would be likely to default? At present, in the real world of credit scoring, the cutoffs used in prime lending are nowhere near that level of separation; they are much closer to the 90 percent rejection of predictably good loans used in our example.

Current Systems Measure Default in Discriminatory Ways

Credit systems actually are based on the prediction of early default, not lifetime default. While early default is important, take place several years into the loan, not during the first 6 to 18 months. Therefore, not only do the present scoring systems have a discriminatory effect, but they are based on a default of only a few months against loans that typically last for several years— and that last even longer for minorities who buy, sell and refinance less often than whites.

As a measure of early default, credit scores do not incorporate many of the factors that research suggests cause most defaults: job loss, temporary or long-term unemployment, divorce and so on. Because these factors are rarely part of credit bureau databases used in scoring models, such factors are not part of the scoring process. Of course, these events and factors often are not items that could be used in a score at the time of application because they are events and activities that have not yet happened. The result is that the scoring models actually are not predicting default altogether, but only that part of default that can be related to data stored in credit bureaus, and then only inasmuch as the defaults show up very early in the life of the loan.

Many "Predictive" Factors Used in Systems May Have No Causal Connection with Default

In social science research, the critical issue of the explanatory power of statistical models relates to the linkage between correlation and causation. Credit score developers try to squeeze all the correlation they can out of the limited set of factors stored at credit bureaus. In a general sense, they may seem to match correlation with causation, such as in the apparent logic between linking future credit performance to past performance. Still, many correlations raise serious questions of causal relationships. For example, where there is a correlation between the number of inquiries and later default—for some applicants—this may reflect attempts by a person with poor credit habits searching for an acceptance. For others, numerous inquiries may represent the impact of discrimination that forces borrowers to contact more lenders in search of a fair loan.

In one historical file, I saw an applicant with a low score where the main factor was listed as too many open lines of credit. After the person had consolidated his debts, credit bureaus continued to generate low scores on the basis that he now had too few credit lines.

Although debt consolidation often is recommended by credit counselors, the result in this case was lower scores, even though this applicant had never had a delinquent account. Credit scoring companies, lenders and investors often respond to such examples by insisting that their models are complex and not subject to simple understanding. We need to ask, however, as a matter of policy, whether—if we accept a scoring system because of its claimed statistical reliability— are we really accepting correlation without requiring a sound basis for causation? Why should we accept a process with a clearly discriminatory effect when it fails to meet the social science test of having a demonstrable linkage to causation?

Scoring Models Based on Non-Mortgage Credit Are Not Likely to Predict Mortgagor Behavior as Well

Most credit scoring models are not geared to mortgage loans but to all credit. Minorities stay in their homes longer than whites.

Many lenders, counselors and other players in the home sales market have perceived that a home is treated differently by many moderate-income and lower-income buyers—who also are disproportionately minority—than by higher-income buyers. The home is more than a commodity that can be replaced, for these buyers. More sacrifice may be made to keep the home than to protect other forms of credit from default. This is an example of just one aspect of lending that may separate the treatment of home-loan credit from other forms of credit that minorities use. Credit scoring used in mortgage loans needs to be based on mortgage loans, and perhaps even loans for the same type of mortgage product, in order to develop patterns that truly reflect mortgage risk.

Credit Scoring Ignores Change in Borrower Behavior

Scoring systems do not account for the ability of interventions to change behavior. For example, many lenders and special loan programs have discovered that pre-purchase counseling (when done well) and post-default counseling or interventions (when done rapidly at the point of first delinquency) can substantially reduce the likelihood of default or the likelihood that a default will result in foreclosure. Since these types of programs have been targeted disproportionately to minorities (usually either by the effect of geographic area or income targets), the failure to account for this ability to change predicted behavior results in credit scores imposing a discriminatory effect even though less discriminatory alternatives exist. This undermines the business necessity argument for the use of credit scores in an environment where they have a discriminatory effect.

Industry Claims That Scoring Frees Time to Spend on Applicants with Problems Are Unrealistic

The speed and economy of using credit scores allegedly frees up lenders to spend more time with those whose credit histories need more work. But, in a market of extreme competition and with a growing range of products for all credit scores, lenders are less likely to use the system to devote real time to problem scores than they are to simply divert those with low scores to higher-cost loan programs. They are, for example, not as likely as in the past to review the accuracy and basis of credit issues or even to ask borrowers to verify that derogatory information in their accounts are, indeed, the applicant's accounts and that they are correct. Lenders also are not as likely—as with non-scoring underwriting—to ask for explanations of credit issues. Therefore, credit blemishes that previously were considered acceptable because they were not the fault of the borrower or were considered temporary—such as a death in the family, medical bills or temporary unemployment—may now simply be counted against the borrower just as a voluntary disregard for credit would tarnish the borrower's credit history. We know from socioeconomic studies and health studies, for example, that minorities suffer loss of job and serious medical bills more often than the majority population.

Correcting bad information can be hard and time-consuming. The lender also may be concerned that the investor purchasing the loan will not have access to the corrected information or may secure a score from another credit bureau that does not contain the corrected information. Therefore, in a random quality-control audit or in a review if the loan goes into default, the lender may face negative ratings or even the requirement to repurchase the loan. Because derogatory credit ratings happen most often with minority loan applications, the lender may want to find ways to respond to the application that avoid having to verify and correct bad credit. This may lead to rejecting the loan or encouraging the applicant to withdraw the loan at the earliest time during the application process.

Alternatively, when faced with low credit scores, a lender may introduce a judgmental system of overrides, which can introduce discrimination into the system.

Rather than reject a loan with credit issues, a lender may steer the borrower away from prime conventional products toward FHA or subprime products, rather than try to deal with investigating a low credit score or correcting bad information. This would have the effect of imposing higher rates or more onerous terms on the borrower, or it could contribute to concentrations of FHA loans in minority areas—which have historically been shown to have an adverse effect on both the borrowers and the community. Recent studies indicate a similar concentration of subprime lending in minority communities, with similar adverse impacts.

These are some examples of how credit scores, both directly and indirectly, may have a discriminatory impact or may lead to differential treatment. The potential for discrimination and liability should not be ignored, either as an internal part of the scoring system or in the manner in which it is applied.


Response to Statement of Calvin Bradford

In his essay, Calvin Bradford poses an important question when he asks where the line should be drawn between approval and rejection. However, we must be careful not to oversimplify our consideration of this important issue.

Credit scores represent a leap forward in efficiency and access to the mortgage market compared to manual or judgmental underwriting. We should not be satisfied with our current achievements and should continue to work toward increasing the speed and fairness. However, in our efforts to critique the current arrangements, we should consider the alternatives. If we set an arbitrary standard for scoring systems, lenders might be forced to return to manual underwriting—a slower and more subjective approach to underwriting. We want to move forward and improve the current systems. Fortunately, scoring systems will improve over time, because competition will drive lenders and investors to develop more accurate risk assessments.

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