Fair lending compliance remains treacherous waters for financial institutions. The advent of Dodd-Frank added a multiplicity of compliance challenges to an already significant regulatory burden. This has left fair lending just one of many issues with which banks must contend, meaning efficiency is an absolute necessity in order to be successful.
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In a whitepaper entitled “BISG Methodology and Its Impact on Regression Analysis,” we study the use of proxy methodologies as applied in regression analysis designed to test for potential discriminatory practices. In that study, we ran a number of simulations to test various aspects of proxy methods including accuracy and examining different ways to quantify […]
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The use of proxy methods in fair lending analysis – and in particular fair lending enforcement actions – has drawn much attention. In an article entitled “BISG Methodology and Its Impact on Regression Analysis,” we studied the use of proxy methodologies as applied in regression analysis designed to test for potential discriminatory practices.
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In determining whether discrimination occurs based on race or ethnicity, particularly with regard to fair lending, it is obviously important to know the race or ethnicity of the applicant(s). However, for some non-mortgage products lenders are not allowed to collect these data. Therefore, one must use proxies for race and ethnicity.
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In a previous article, we discussed the fair lending benefits of having a profitable and risk-based loan pricing strategy. In this post, we dissect the specific components that comprise an effective approach.
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Understanding the difference between Z statistics and T statistics for fair lending analysis can be frustrating, even more so when it comes to determining which test to use. Let’s take a more detailed look into this world of statistics.
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The first rule of policy analysis is that policies have both intended and unintended consequences. This is no more true than in the commercial banking space, as good intentions can sometimes produce negative results. In this article, we examine the importance of lenders having a well-designed and profitable loan pricing strategy as part of fair […]
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Regression analysis for fair lending with respect to underwriting analyses generally use what are known as “discrete choice” models. Such functional forms are used in which the measurement (dependent) variable is categorical or a limited outcome. In an underwriting evaluation for fair lending analysis, for example, what is measured is either approval or denial. A common […]
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In studying your bank’s loan data, how can you determine the relationships among various factors in your lending policies, customer base, pricing, and more? Through the use of regression modeling, an important tool in statistical analysis.
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