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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Ever wonder how one can conduct a fair lending review of your bank’s consumer lending products without having government monitoring information like race, gender, and ethnicity available? Enter the proxy.
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Consumer loans pose a great deal of risk for financial institutions. In this post, we’ll examine that risk to lenders.
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Understanding and managing loan policy exceptions is critical to a financial institution’s fair lending compliance. In this article we explain policy exceptions and describe the different types of exceptions. How an institution defines policy exceptions is also critical and will be the subject of another post.
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