In the current edition of the ABA Banking Journal Rich Riese, the Senior Vice President at the ABA Center for Regulatory Compliance, gave his view of the outlook for fair lending in 2013. He said “bankers can expect to see the aggressive pursuit of fair lending to continue – and grow in scope.” According to Rich the “grow in scope” phrase refers to word going around Washington that there is going to be an emphasis on direct and indirect auto lending particularly with respect to alleged discrimination against women and Hispanics. Although Rich did not directly mention it, there is another perspective on the “grow in scope” phrase. In the past, fair lending auto analysis focused primarily on pricing issues. That is, were banks and auto dealers charging similarly qualified applicants the same interest rates and fees? In the past year, the regulators have begun to ask banks involved in auto lending to also perform credit decision analyses of their portfolios.
Given this background, the question becomes how do you prepare for this potential regulatory initiative? Let me suggest some general steps.
· Perform a fair lending risk assessment of your underwriting and pricing risks by race, gender, ethnicity and age to see if there appears to be a fair lending problem. If this analysis suggests differential treatment is happening, then regression analysis is the next step.
· Before diving into the regression analysis itself, get copies of your automotive underwriting guidelines and price sheets so you can determine which factors are used by your underwriters and pricing people to decide whether to grant credit, and if so, at what price.
· Collect the data on the factors used for underwriting and pricing based on your review of your bank’s underwriting guidelines and price sheets.
· Estimate your credit decision and pricing equations using the data you have collected adding each of the prohibited basis variables, one at a time, to see if the variable is statistically significant.
· If one of your prohibited basis variables is statistically significant, you will want to find out why. There are numerous potential reasons for a statistically significant prohibited basis variable. Some of the reasons are: data errors, missing factors in the equation, improper equation structure and differential treatment. If differential treatment did occur then it is incumbent on the bank to fix the problem by adjusting policies and procedures and where needed make restitution to the offended applicant.
· Even if there are no statistically significant prohibited basis variables, you should review the exception and matching reports that typically are part of a regression analysis. Credit decision exception reports show you which applicants did not get the treatment the regression equation predicted. That is, the exception applicant was either denied when the regression equation predicted he/she should have been accepted or vice-versa. Pricing decisions exception reports list the applicants that were priced above or below what the pricing equation forecast. You should review these applicants’ files to be sure you understand why the applicant received the treatment he/she did.
· The second set of standard regression outputs are the matching reports which in essence allow you to do your side-by-side review. This is where you compare your prohibited basis applicant who, for example, was denied to a similarly-situated non-prohibited basis applicant who was accepted. Again, you should be able to explain why the disadvantaged applicant received the treatment he/she did.
If you follow these general steps, when it comes time for your exam, you should be in good shape. Recognizing that these steps are general in nature, as you begin your analysis questions will arise, I invite you to give me a call or drop me an email with those questions.
Preiss&Associates has been doing custom fair lending analyses and fair lending training for more than 20 years. If you have fair lending questions, want to talk about your fair lending issues or have need for us to assist you with your fair lending program give us a call at 847-295-6881 or drop us an email at firstname.lastname@example.org.