Managing Data for Fair Lending Underwriting Analysis for Community Banks (Part 1)

Fair Lending  »  Managing Data for Fair Lending Underwriting Analysis for Community Banks (Part 1)

In Part 1 of this two-part post, we discuss some issues with conducting fair lending underwriting analysis for community banks:  challenges which are becoming more acute with the regulatory emphasis on quantification.

Previously, we have addressed some of the complications present when conducting an underwriting analysis of fair lending, particularly when using regression. As statistical methods become more and more the norm in fair lending evaluations, these complications become more apparent.

Regardless of whether statistical methods are in play or not during a fair lending review, the regulatory emphasis on quantifying explanatory factors raises issues for lenders with regard to underwriting analysis. This is especially true for community banks.

Setting aside secondary market lending for the moment (which has its own set of problems), loan decisioning in the community banking space is much more involved than it is perceived to be. The reason is borrower situations do not always fit a cookie-cutter mold and an array of conditions may affect the likelihood of repayment. Most banks, especially those that are CRA-minded, want to be flexible and make as many loans as possible.

This, of course, is where the challenges begin. First, situations can vary and decisioning factors can also be dependent upon one another. Hence, setting benchmarks is difficult because these may have to be conditioned on other factors. Second, the number of data points needed to explain credit decisions can become large rather quickly. 

However, not all credit attributes that can come into play on a given loan application decision are relevant for all decisions. This again, adds to the potential complexity of fair lending analysis for underwriting.

To successfully manage fair lending risk, the attributes that determine the decisions need to be available for analysis and testing. This then brings us to the fundamental question at hand: how and what data should be collected and managed?

The short answer is this should be driven by the decisioning process. This pre-supposes that there is a well-defined and consistent method for underwriting loans. This demands defining and quantifying key variables that determine the credit decision which is only possible with a consistent and enumerated process.

We will discuss this in more depth in Part 2 of this post and offer some guidance that should prove helpful.

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