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Disparate Impact Remains Fair Lending Risk to Banks

By: Joseph Porter and Michael Orlowski

Lenders seeking judicial relief from the Consumer Financial Protection Bureau’s heightened enforcement of the Equal Credit Opportunity Act were left disappointed by the settlement of Mt. Holly v. Mt. Holly Gardens Citizens in Action Inc. just three weeks prior to that case being heard by the Supreme Court on Dec. 4, 2013. The Supreme Court was to rule on whether intentional discrimination, an element needed to prove a violation of the act, could be shown using a disparate impact analysis, also referred to as the “effects test.” Unfortunately, the settlement prevents this review.

Under the act, it is unlawful for a creditor to discriminate against any protected class on the basis of race, color, religion, national origin, sex or marital status, age or source of income. The disparate impact theory enables enforcement agencies to prove lender discrimination via a regression analysis of statistical variations in loan terms between borrowers as evidence that a lender illegally facially discriminated against a protected class, even without a showing of discriminatory underwriting criteria. For this reason, disparate impact has been a hotly contested issue in appellate courts for nearly 40 years. The court’s missed opportunity to provide guidance on the viability of the disparate impact theory means that lenders continue to be at risk for unknowingly discriminating against certain groups, and thus remain exposed to fair lending violations.

Disparate impact enforcement actions can be broken down into three phases: data gathering and analysis; allegation and rebuttal; and referral to the Department of Justice. In the data-gathering phase, an agency obtains loan data samples from regular compliance examinations, from data reported to the agency under the Home Mortgage Disclosure Act, or from requests for the data (if the agency has been tipped off to pricing discrimination). Once compiled, data is sent to an agency statistician tasked with applying a regression analysis model to determine if the data reveals statistically significant differences in loan pricing for protected classes. This process may take a year or longer. If the regression analysis demonstrates a statistical difference in pricing related to a prohibited variable (such as race) having a predictive value to the outcome, the bank will receive a letter claiming that the agency has identified an unexplained pricing differential between groups that suggests “apparent” discrimination.

The second enforcement phase begins on the bank’s receipt of such a letter, to which it has a mere 15 days to respond. This second “allegation and rebuttal” phase is a critical and time-sensitive opportunity for a lending institution to rebut the apparent discrimination. The burden has shifted to the lender to prove it is not guilty of an infraction (note: the agency does not have to show intent to discriminate). If the lender is unable to rebut the presumption of apparent discrimination, the enforcement agency will conclude that a pattern or practice of discrimination has occurred, in which case the bank’s management rating may be downgraded or worse, may be referred to the Department of Justice for civil enforcement. The third phase is in the hands of DOJ; the department may either refer the case back to the referring agency for administrative enforcement only, or initiate its own investigation, which may culminate in federal charges.

The best way to avoid a fair lending charge is to understand what risk factors the examiners are looking for and the statistical analysis used, and to take preventive measures to reduce an institution’s risk profile. This may include implementation of an internal fair-lending risk assessment program providing fair lending training and conducting a review of pricing models, disparity ratios and denial percentages. It is also strongly recommended that banks go beyond the basics of a risk assessment and perform their own regression analysis to discover and remedy inequities before an examination.

In addition, the bank should consider having a fair lending allegation response and mitigation program in place. Such a response program would provide a timeline, checklist and general process for the bank to (i) initiate a request for the regression models, data and other documentation used by the agency so that the bank may attack the model’s underlying assumptions or accuracy, (ii) perform its own regression analysis based on the data and models used by the agency to explain how discrepancies are caused by non-discriminatory factors, and (iii) immediately engage outside experts to assist with the data analysis and to help craft a thorough and well-documented response that demonstrates a non-discriminatory and neutral lending practice. 


 

Joseph Porter and Michael Orlowski are attorneys with Polsinelli PC based in St. Louis. Contact the authors at 314-889-8000 or by email at jporter@polsinelli.com or morlowski@polsinelli.com.

Copyright February 2014. BankNews Media.


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