All aspects of the consumer lending process--including the identification of prospective customers, loan underwriting and pricing, and account management--changed dramatically in the last third of the twentieth century. Advances in information technology have lowered the costs of credit, opened new markets to lenders, and increased the speed of lenders' decisionmaking. Borrowers have seen a proliferation of products and services offered at prices more closely tied to the anticipated risks and costs of lending. The new methods have also generated concerns about the loss of individualized treatment of credit applicants and about the possibility of hidden biases in the technologies being used.
One of the keys to these changes in credit markets has been the automation of the lending decision through credit scoring. Credit scoring is any automated, statistically based system (or "model") that quantifies the credit risks posed by a prospective or current borrower relative to other borrowers and calculates a summary numerical "credit score" for each individual. Credit-scoring technologies may be used to support judgmental decisionmaking (that is, the judgment of the loan underwriter) or may serve as the sole basis for credit decisions.
Before the advent of credit scoring, individual credit analysts, or underwriters, manually reviewed applications and evaluated them on the basis of their own experience, sometimes in conjunction with specific rules or other non-empirically derived credit guides established by the creditor. However, such judgmental decisionmaking is time consuming, costly, and subject to inconsistency because different underwriters may weigh individual factors differently. In contrast, it is maintained that underwriting based on credit scoring is quick, inexpensive, and consistent. Moreover, credit scoring can potentially improve the accuracy of credit decisions and may reduce the potential for prohibited forms of discrimination to the extent it removes subjectivity from credit decisions.
Credit scoring was initially focused on the decision to accept or reject an application for credit. Over time, its use expanded into other aspects of the lending process, including loan pricing, various aspects of account maintenance, and the solicitation of new credit accounts. Credit-scoring technologies are now routinely used by lenders to help identify prospective customers and to make "firm offers" of credit to them. The increasing use of unsolicited offers of credit as a primary channel for consumer lending has likely promoted competition among lenders by allowing them to inexpensively reach beyond the traditional geographic markets served by their branch offices.
A number of concerns have been raised about the efficacy of credit-scoring technologies and how they are used in the marketplace. First, some have questioned whether risk estimation based on credit scoring affects population segments differently based on factors other than risk. Second, concerns have been raised about whether some of the specific factors used to estimate credit scores may have an adverse effect on individuals grouped by their race, ethnicity, sex, or other personal or demographic characteristics.
Third, some observers believe that automated technologies disadvantage individuals with nontraditional credit experiences because creditors offering such products may be less likely to furnish information to credit-reporting agencies (credit-reporting agencies are firms that gather and make available through credit reports and other techniques information on the credit-related behavior of consumers). These observers often maintain that individuals with nontraditional credit histories are better served by judgmental credit evaluations, which can consider information not included in credit reports and thus may provide a more accurate profile of credit risk. For example, sometimes lenders give weight to explanations provided by consumers regarding extenuating circumstances associated with credit problems they have encountered.
Fourth, it has been suggested that judgmental evaluations may be better able than credit-scoring technologies to detect errors or other inaccuracies in the information used to evaluate creditworthiness. And fifth, some observers argue that discrimination in lending markets has caused disadvantaged individuals to pay more for credit than is warranted or caused them to use less desirable sources of credit. Either outcome could lead to a greater possibility of loan payment problems and consequently tarnished credit histories, outcomes that would be reflected in poorer credit scores.1
To assess these concerns about credit scoring, the Congress mandated, in section 215 of the Fair and Accurate Credit Transactions Act of 2003 (Fact Act), a study of the effects of credit scoring on the availability and affordability of credit.2 The study is to include an analysis of the statistical relationship that controls for demographic characteristics between credit scores and the quantifiable risks and actual losses experienced by businesses. In addition, the study is to address "the extent to which, if any, the use of credit-scoring models, credit scores, and...impact on the availability and affordability of credit to the extent information is currently available or is available through proxies, by geography, income, ethnicity, race, color, religion, national origin, age, sex, marital status, and creed, including the extent to which the consideration or lack of consideration of certain factors by credit-scoring systems could result in negative or differential treatment of the protected classes, under the Equal Credit Opportunity Act (ECOA), and the extent to which, if any, the use of underwriting systems relying on these models could achieve comparable results through the use of factors with less negative impact."3