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DOJ Delivers Reminders on Fair Lending Compliance & Discrimination

2 min read
Apr 28, 2015

In the Department of Justice (DOJ), the Civil Rights Division is responsible for enforcing the Equal Credit Opportunity Act (ECOA), Fair Housing Act (FHA), and Servicemembers Civil Relief Act (SCRA). In April 2015, the Civil Rights Division submitted their Annual Report to Congress. Here are some of the key issues presented within this report.

In the recently released ECOA Annual Report, the Civil Rights Division provided some key fair lending compliance reminders. 

The DOJ has the authority to enforce ECOA and FHA on its own, or upon referral from another agency. They investigate both mortgage lending and non-mortgage lending (including auto loans, unsecured consumer loans, student loans and credit card products), and tend to focus on a range of abuses in the lending market, including redlining, underwriting, and pricing discrimination. 

The Lending Discrimination Enforcement team's recent report featured four fair lending (ECOA) settlements in 2014 that included key areas of consumer lending, including auto, credit cards, mortgage and unsecured lending:

  1. Reverse Redlining Discrimination (Auto): The case alleged that there was a pattern or practice of reverse redlining, or intentionally targeting African-American customers for unfair and predatory credit practices, in the financing of used car purchases. In addition to the financial settlement, the dealerships were required to implement a number of specific practices to ensure that the terms of their loans are no longer unfair and predatory.
  2. National Origin Discrimination (Credit Card): The complaint alleged the bank excluded Hispanics from two of its credit card debt-repayment programs if they had a mailing address in Puerto Rico or denoted Spanish as their preferred language for various communications. In addition to the financial settlement, the financial institution agreed to credit repair corrective actions for affected borrowers.
  3. Discrimination Based on Disability and Receipt of Public Assistance Income (Mortgage Lending):  The complaint alleged that the defendants required credit applicants to provide an official letter from their medical doctor to substantiate that their disability income would continue, but did not impose a documentation burden on applicants without disabilities to prove their income would continue. In addition to the financial settlement, the financial institution agreed to implement new policies and train employees.
  4. Pricing Discrimination in Unsecured Consumer Lending (Unsecured Consumer Lending):  The complaint alleged the Bank charged higher prices (142 bps) on unsecured consumer loans made to Hispanic borrowers than to similarly situated non-Hispanic borrowers.  In addition to the financial settlement, the bank agreed to revise pricing policies, monitor loans for disparities, and provide training.

In 2014, there were 18 referrals to the Department of Justice in 2014 that included the following types of discrimination:

  • 12 Involving Race or National Origin.
  • 5 Involving Sex
  • 4 Involving Marital Status
  • 3 Involving Age
  • 3 Involving Source of Income
  • Note: Several referrals involved multiple protected classes; therefore, the number of referrals by protected class categories totals more than 18.

Over the years, Race/National Origin has become a larger focal point for the agencies, as evidenced by the number of referrals. Since 2009, Race/National Origin have accounted for over 50 percent of the Referrals. The chart below highlights this fact.

TRUPOINT Viewpoint:  These DOJ settlements highlight the fact that Fair Lending extends well beyond mortgage.  The four settlements highlighted in the report come from different lending areas (Auto, Credit Card, Mortgage and Unsecured). 

The annual ECOA report provides a good reminder to all of us about three best practices: (1) consider all areas of lending including marketing, underwriting, pricing, and servicing; (2) pay specific attention to where discretion may exist; and (3) know your numbers (analyze the disparities in your data).

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