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• W291949680 abstract "Fair lending and CRA compliance have become more numbers-driven Compliance regulation in the areas of credit discrimination and the Community Reinvestment Act is turning into a numbers game. Attorney General Janet Reno has said that the Justice Department will analyze racial lending patterns statistically, and will treat unjustified disparities as illegal discrimination--whether or not there is discriminatory intent. The regulatory agencies are taking steps in the same direction, and are at the same time moving Community Reinvestment Act compliance evaluation toward a much more numbers-driven approach. On the plus side, greater quantification and objectivity in CRA and fair-lending analysis would be a welcome change for bankers and regulators. However, on the down side, thus far, regulators and courts cannot even articulate a clear set of standards for distinguishing legal from illegal actions in the realm of subtle discrimination. To try to distill these complex issues into formulae when the conceptual underpinnings remain so weak is potentially dangerous. Accordingly, bankers must develop the data systems and thought processes needed to manage their lending activities to pass quantitative tests. They need to understand the methodologies examiners will be using and try to apply these before the examiners arrive. They also must select and use software products that will give them the information that they need. None of this is easy. The regulators are a long way from having tools that have won the confidence of even one agency--not to mention all of them. Furthermore, there is enormous variability in the data approaches taken even within agencies. Some of this is regional, as certain district offices prefer certain methods. In many cases, though, individual examiners are using approaches they have developed for themselves. A similar state of confusion pervades the software market. Vendors are developing more and more products, yet no simple, reliable tool yet exists to meet all the emerging analytical objectives. (See Selecting software products to help with data analysis.) What follows is an overview of the major issues in data analysis for fair-lending and CRA evaluation. It is offered with the proviso that the scene is changing rapidly and that there is a high level of chaos among and within agencies, as well as in the industry. Underlying assumptions The unstated but operative assumption underlying most data analysis in credit discrimination is that all groups in a market should receive credit in a proportion about equal to their share of the population. While government and advocacy-group reviewers acknowledge that there may be reasons other than discrimination to explain cases where credit distribution is not proportional, the fact remains that this is the implicit starting point, and it drives all the rest. The thought process begins with looking for gaps in approval rates or application levels between whites and minorities, married and unmarried, men and women, young and old, etc., and all further analysis is aimed at seeking explanations for these gaps. What cannot be explained is viewed as discrimination, or at least potential discrimination. While this thought process is less clearcut with CRA, it is at work there as well. It shaped the way examiners looked at geographic distribution of lending under the old CRA, and it is clearly evident in the agencies' development of the market-share concept for CRA evaluation under the proposed rules (a substantial revision of which was pending at press time, though it could now be public). The market-share test begins with the implicit assumption that, if the bank is meeting its CRA obligation, its market share in low- and moderate-income areas will be similar to its share in middle- and upper-income areas. If not, there is a presumption that CRA performance is weak. …" @default.
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