Forms of Fair Lending Discrimination: Introduction
In an upcoming series of posts we address types of fair lending discrimination that are commonly recognized by the regulatory and enforcement agencies.
Read MoreIn an upcoming series of posts we address types of fair lending discrimination that are commonly recognized by the regulatory and enforcement agencies.
Read MoreIn our previous post, we addressed a few frequently encountered issues when using regression methods to conduct a fair lending analysis.
Read MoreEvaluating loan application outcomes (approval or denial) in the context of fair lending is referred to as an “underwriting analysis.” Regression modeling is commonly employed in such analyses.
Read MoreThe FDIC recent released its Winter 2016 edition of Supervisory Insights. This edition was focused on credit risk trends.
Read MoreIn our previous post, we started introducing the concept of statistical significance. We began with making two important points. First, statistical methods are applied in order to estimate or measure an unknown. A sample of data is analyzed which is then used to draw conclusions about a larger population. This is known as statistical inference. […]
Read MoreFDIC Chairman Gruenberg:“Revenue and net income were higher, loan balances grew, asset quality improved, and the number of unprofitable banks and ‘problem banks’ continued to fall,” Gruenberg said. “Community banks also reported solid results for the quarter and year with strong net income, revenue, and loan growth.
Read MoreWhen statistical methods are applied to evaluate fair lending compliance, one of the metrics of interest is the statistical significance of measured differences in treatment of applicants. Such differences may be measured by such things as the interest rates charged on loans or the rates of denial for one group versus another (such as males […]
Read MoreWhen conducting fair lending regression analysis of underwriting, we are examining a sample of loan applications that were either approved or denied. The practice is to regress denial (y=1 if denied, 0=approved) on a target group indicator variable and other attributes upon which the loan decision should have been based.
Read MoreSeveral years ago, I began a conference presentation by making the statement: “To be successful at fair lending, you must learn how to properly discriminate.” The statement was obviously meant to be provocative, and it must have worked because it was quite some time before I was invited back (just kidding). The statement, however, is […]
Read MoreIn a previous post, we addressed the importance of customer service as a component of managing fair lending risk. While it is important for an institution to have efficient processes in place to facilitate the lending process, equally important is the recognition that business is relationship.
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