Fair lending compliance: RegTech provides an even playing field

Financial institutions and specialty finance companies can face penalties for unlawful lending practices. The Consumer Financial Protection Bureau has recently embarked on a campaign to identify and punish discriminatory practices in the industry. This includes an expanded definition of what is considered “unfair” and an examination of financial institutions’ decision-making in advertising, pricing, and other areas to ensure that companies are appropriately testing for and eliminating illegal discrimination.

Fair lending requires that lenders issue credit to all qualified borrowers without discrimination, no matter what their background is. Specifically, the term “fair lending” refers to a body of regulations, at all levels of government, ensuring lenders follow basic principles of equity.

Fair lending exists to do exactly what its name implies — ensure that financial institutions treat all potential customers equitably. However, identifying and measuring the risk inherent in lending processes is very challenging. While certain special purpose credit programs help deliver positive social impact, they need to be supported by robust internal governance to prevent any instances of Unfair, Deceptive, or Abusive Acts or Practices (UDAAP). Lenders should focus on the short and long-term ESG issues originating from their business models by adopting activities and technology that can support compliance, increase operational efficiencies, improve customer relations, and optimize the customer experience. 

Fair Lending Risk and Assessment
Codified discrimination may occur across these previously mentioned laws. The CFPB, Department of Justice, U.S. Department of Housing and Urban Development and federal prudential bank regulators have issued various guidance and taken numerous actions indicating that financial institutions must be proactive, not reactive, in monitoring for patterns of potentially discriminatory lending activity also known as fair lending risks.

The risk assessment for these lending activities can therefore be defined as an effort to identify and measure the risk inherent in the bank’s lending processes and determine what control and monitoring mechanisms are in place to protect against illegal discrimination. 

Financial institutions should re-examine their compliance management systems in light of the expanded UDAAP standard and focus on the potential discriminatory impact of artificial intelligence use throughout their operations. It has become even more challenging to evaluate whether a prohibited basis was a factor in an institution’s credit decisions given the increased sophistication of existing black-box model and frameworks.

Lender Challenges and Violation Examples
In April 2020, the Office of the Comptroller of Currency referred a fair lending violation matter to the Department of Justice after information gathered during a 2018 fair lending examination suggested that a lender had engaged in unlawful redlining between 2014 and 2016. The proposed consent order requires the lender to pay a civil money penalty of $5 million for allegedly engaging in unlawful discrimination against applicants and prospective applicants, including (i) by redlining majority Black and Hispanic communities in a certain Metropolitan Statistical Area (MSA) and (ii) engaging in acts and practices directed at prospective applicants that would discourage prospective applicants from applying for credit in violation of the Equal Credit Opportunity Act (ECOA), Regulation B, and the Consumer Financial Protection Act of 2010 (CFPA).

The CFPB Exam Manual dated March 16, 2022 with updated UDAAP section notes that “[c]onsumers can be harmed by discrimination regardless of whether it is intentional. Discrimination can be unfair in cases where the conduct may also be covered by Equal Credit Opportunity Act (ECOA), as well as in instances where ECOA does not apply.”

“The CFPB will examine for discrimination in all consumer finance markets, including credit, servicing, collections, consumer reporting, payments, remittances, and deposits. CFPB examiners will require supervised companies to show their processes for assessing risks and discriminatory outcomes, including documentation of customer demographics and the impact of products and fees on different demographic groups. The CFPB will look at how companies test and monitor their decision-making processes for unfair discrimination, as well as discrimination under ECOA.”

Additionally, with the Black Box Press release and Circular 2022-03 on May 26, 2022, the Bureau makes the point that “Companies are not absolved of their legal responsibilities when they let a black-box model make lending decisions.”

Unfortunately, the Bureau does not provide further detail or concrete examples of what kinds of automated underwriting tools may meet the standard of being used without violating the ECOA, and what kind of compliance systems cover all aspects of risk assessment. Let’s try to uncover these with a primer on Fair Lending.

Regulatory Technology (RegTech) : How can data science and technology help?
As the transition from fair lending to customer-focused banking takes place, technology presents additional opportunities that go beyond compliance. Customer focused and responsible banking practices can keep a lender compliant and increase efficiencies in an institution’s operations, improve customer relations, and fine-tune the customer experience.

The scope of a consumer-focused compliance analysis should include the loan product(s), market(s), distribution(s), pricing, redlining, steering and adverse action reason and control group(s). This should include obtaining an overview of an institution’s compliance management system as it relates to fair lending. This can be done in the following two ways:

  1. Monitoring current decisions. Evaluate current data and processes to identify risk in key areas of fair lending compliance, such as marketing, underwriting, pricing, steering, redlining, and matched pairs. This would entail:
    a. Identifying disparate treatment using benchmarking, matched pair, and peer analysis capabilities 
    b. Testing disparate treatment by statistically sound, automated and customizable target and control selection that allows for flexible tolerance settings and tracking options 
    c. Building data visualizations that highlight disparities and issues of concern for ongoing and continuous monitoring
    d. Analyzing standard and loan-type specific input fields to identify any overt evidence of disparate treatment on a prohibited basis 
  2. Preparing the system for new regulations. Develop frameworks and leverage technology to:
    a. Incorporate alternate data sources for making better informed decisions
    b. Utilize unstructured data sources such as consumer complaints to understand any sort of disparate impacts from customers
    c. Build AI and Machine learning systems that have explainable and reproducible outputs

A bank that engages in these activities can use data and technology to help shape outreach programs to borrowers, reduce fair and responsible banking risks, and potentially support the institution’s Community Reinvestment Act (CRA) strategy. These targeted activities improve compliance, build a better brand and improve customer experience overall.


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