Redlining, or the practice of denying credit to residents of certain areas due to the high number of ethnic or racial minorities living there, is not a new problem. It’s also not limited to mortgage loans.

For decades, many lenders avoided or flat-out refused to lend to home borrowers in minority communities. Redlining gets its name from the literal red lines that were on maps created by the federal government in the late 1930s to identify high-risk neighborhoods that they deemed to not be creditworthy. The Federal Housing Authority refused to insure mortgage loans in these areas, which were almost always minority communities.

This institutionalized racism prevented American minorities from investing homeownership and contributed to racial segregation, since white borrowers couldn’t take out loans to buy in these communities either. This practice was made illegal by the Fair Housing Act of 1968, but redlining persists. The concept of redlining is evolving and expanding to include any discriminatory practice that results in unequal access to or unequal terms of credit because of the race, color, national origin or other prohibited characteristics of a credit seeker’s neighborhood.

This includes:

  • Small business loans
  • Student loans
  • Auto loans
  • Digital redlining
  • Algorithmic credit decisioning

In some cases, redlining is used more colloquially to refer to discriminatory credit practices – even when it does not have a geographic component. For example, the term “reverse redlining” describes instances when a protected class of people is given access to credit at an inflated cost compared to similarly situated individuals from other groups.

While this practice doesn’t technically meet the legal definition of redlining, it’s still discrimination. Violations of fair lending laws can lead to lawsuits and regulatory action, and damage a financial organization’s reputation. The public may not understand the technicalities of redlining, but they know treating people unfairly is bad business practices.

Regulators’ Increased Focus on Redlining
Engaging in any kind of redlining can lead to serious consequences for a bank. The Consumer Financial Protection Bureau recently declared that it’s “committed to rooting out all forms of lending discrimination, including redlining,” and promised to “pursue and address both high- and low-tech forms of fair lending violations.”

It’s not an empty promise. The CFPB recently collaborated with the Office of the Comptroller of the Currency and the U.S. Department of Justice on a $9 million lawsuit against Trustmark Corp., a $17.4 billion bank based in Jackson, Mississippi, for allegedu202fredlining in majority black and Hispanic neighborhoods inu202fMemphis, Tennessee.u202fIn announcing the suit, U.S. Attorney General Merrick Garland said the Justice Department is committed to “addressing modern-day redlining” and ensuring that “federal fair lending laws are vigorously enforced.”

The CFPB is also taking a broader approach to find the root causes of redlining. It warned institutions that it will be examining “decision-making in advertising, pricing, and other areas” to ensure companies are testing for and remediating discriminatory practices that violate federal law against unfair practices. One recent redlining enforcement action called out a lender for featuring only white non-Hispanic models and loan officers in its marketing campaigns and opening loan offices exclusively in majority white communities.

Fair Lending Analytics Can Help
It’s never safe to assume that your financial organization isn’t engaging in redlining. While financial institutions may not intend to discriminate, redlining is still surprisingly common – even at tech-savvy and experienced companies. Analyzing loan data is the only way to know for sure that your financial organization isn’t engaging in redlining. Banks, mortgage companies, fintechs, small business and student lenders should all be analyzing their loan data to uncover fair lending disparities.

Fair lending analyticsu202fhelp financial organizations avoid redlining by quantify their fair lending performance by identifying loans causing disparities to analyze and correct them. Not analyzing data can increase the risk of redlining and fair lending violations, exposing the organization to criticism from the media, public interest groups, and regulators.

Don’t ignore the risk of redlining. Whether it’s in the mortgage business or in other nontraditional areas, ignorance is not bliss. It’s a fair lending violation waiting to happen.


Michael Berman