Address regulatory trends and institution specific risk
Fair lending has been a focus of regulators for several years, so it was no surprise when the NCUA (National Credit Union Administration) identified it as one of their consumer compliance scope areas for 2021. As the world around us continues to change, institutions who commit to performing data-driven fair lending reviews can expect to receive dividends from their efforts for years to come.
Historically, fair lending reviews have largely focused on ensuring borrowers were treated equitably, regardless of their gender, race, and ethnicity. Procedures usually consist of policy reviews, an analysis of approval/denial rates by protected class, and a comparative file review. While these procedures make for a strong foundation, institutions should assess whether they are addressing all inherent risks based on their operational structure and product offerings. To ensure your review program is up to par, start with the NCUA’s Fair Lending Guide (and review checklist), then be sure to consider each of the following:
- Loan Segments – are all segments and their unique risks being reviewed?
- Basis of Discrimination – are all bases, beyond the big 3 (gender, race, ethnicity) being reviewed?
- Tailored Approach – is our review targeting our institution’s specific risk areas?
Many institutions have traditionally focused their efforts around HMDA (Home Mortgage Disclosure Act) reportable loans. The HMDA data set is a great starting point as it includes all applications, physical addresses, and robust demographic information. Unfortunately, on average, these loans represent only 45% of a credit union’s portfolio, leaving a majority of loans untested. Recently, regulatory focus has increased around this issue with institutions receiving comments related to their monitoring of non-real estate, indirect, and purchased loan segments.
Generally, all segments should be evaluated to the extent possible to ensure that there are no systemic fair lending issues lurking in an institution’s blind spot. Reviewing non-HMDA segments can be a challenge as most institutions do not collect demographic information on their applicants. However, by using publicly available data sets, institutions can synthesize much of the demographic information necessary to perform an adequate analysis.
The Social Security Administration publishes a list of all baby names occurring more than 5 times annually. The list includes the name given, the gender, and the number of times it was registered. By using this data set as an index table, a “most likely gender” field can be synthesized, giving an institution the ability to review and analyze these segments for inconsistent lending practices based on gender, just as they would for HMDA reportable loans. Similarly, through their website, the United States Census Bureau (USCB) publishes a listing of all surnames occurring more than 100 times . Each name listed includes a total count and the percentage breakdown of how frequently the name was associated with each of the ethnicities captured in the UCSB’s canvassing process. This information can then be utilized to synthesize a “most likely ethnicity” field which can be used to evaluate these segments based on ethnicity. While this approach is less accurate than collecting the information from the borrowers directly, it provides institutions with an opportunity to evaluate these often-neglected segments.
Basis of Discrimination
As noted, fair lending reviews have historically centered around three main classifications of borrowers: gender, race, and ethnicity. However, based on applicable regulations, there are many more bases that should be considered when performing a review. According the NCUA, since 2017 the vast majority of fair lending findings (including 100% of those in the pipeline as of November 2020) related to discrimination based on age or marital status. This is a clear indication that the traditional focus of reviews may no longer be sufficient.
During their examinations, the NCUA identified a trend where married borrowers are often granted preferential treatment during approval and underwriting. Namely, when a single borrower with a cosigner applies for a loan, many institutions use the primary borrower’s credit score regardless of whether it is higher or lower than the co-borrower’s score. Contrarily, married couples who apply as borrower and co-borrower are often benefited by having their highest credit score selected. In this scenario, where both sets of borrowers are applying as primary and co-borrower, the only differentiating factor is their marital status. Therefore, inconsistent treatment of the two groups is clear discrimination based on marital status.
As noted by the NCUA’s fair lending guide, a creditor cannot consider an applicant’s age when making a credit decision except that they are of age to enter a legally binding contract. Specifically, borrowers who are 62 years of age or older must be treated equally to those borrowers under the age of 62. One area where age discrimination has been identified is within automatic loan approval systems, where the borrower’s age is treated as a reduction in the determination of their creditworthiness. Including age in a judgmental loan evaluation system violates guidance from both ECOA (Equal Credit Opportunity Act) and Regulation B.
Unlike the big three, marital status and age are data points largely available across all segments of the loan portfolio. By performing an analysis of your portfolio based on these data points, it is relatively simple to gain comfort that borrowers are not discriminating based on marital status and age.
To maximize the effectiveness of your fair lending review, it’s always best to start with an analytical approach. Doing so allows you to create a risk-based approach to identifying abnormalities that may require further investigation. When performing this analysis, think beyond approval and denial rates and consider analyzing your portfolio on other areas of borrower engagement for all bases of discrimination. Just a few examples include:
- Pricing compared to creditworthiness
- Pricing exceptions granted (frequency)
- Pricing exceptions granted (dollar impact)
- Time to close
- Loan fees
By combining the insights gained from your data-driven approach and the NCUA’s fair lending review checklist, your institution will be well on its way to a more robust fair lending review and the peace of mind that comes with it.
We Can Help
 NCUA’s November 17, 2020 “2020 Fair Lending and Consumer Compliance Regulatory Update Webinar”
 Calculated based on the NCUA’s 5300 Call Report Aggregate Financial Performance Reports for federally insured credit unions as of September 2020
 Available through ssa.gov and data.gov
 Available through census.gov
The information provided in this communication is of a general nature and should not be considered professional advice. You should not act upon the information provided without obtaining specific professional advice. The information above is subject to change.