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How are you monitoring consumer loans for risk?

Clayton J. Mitchell, Niall Twomey
4/27/2023
How are you monitoring consumer loans for risk?

Collecting comprehensive and accurate consumer loan data can help your organization assess and mitigate potential fair lending risk.

With mortgage-related loans, Home Mortgage Disclosure Act (HMDA) data collection and reporting has been in place since 1975. While analyzing mortgage-related lending data certainly still presents a challenge for many financial services organizations, analyzing and monitoring consumer loans for fair lending risk has proven to be even more difficult for a variety of reasons.

The most obvious challenge is the prohibition from gathering information on an applicant’s race, gender, and ethnicity, thereby requiring a proxy methodology. Add in fintech partnerships and other third-party channels, pricing, and underwriting models, and the risk and effort significantly increase.

While HMDA-reportable loans require lenders to provide details related to race, ethnicity, and gender, consumer loans don’t require that same level of information, which can make monitoring for fair lending risk more complex and a heavier lift. In fact, many organizations haven’t yet assessed their credit card, indirect, or fintech portfolios because of these complexities. Even more, the complexities of artificial intelligence and machine learning within pricing and underwriting models can increase risk exponentially.

However, doing nothing with consumer loan data is not an option for financial services organizations. Regulators continually scrutinize organizations for noncompliance, and the last 12 months have brought many fair lending-related enforcement actions.

Looking at the bigger picture, a single fair lending violation can tarnish an organization’s reputation, result in civil money penalties or orders of consumer redress, and potentially derail strategies for growth and expansion. And beyond the regulatory aspects, isn’t it good for organizations to know and understand who is and is not receiving products and where those products are concentrated?

Collecting consumer-related loan data can be frustrating for many reasons

Collecting consumer related loan data can be frustrating for many reasons

Many organizations might not have a repeatable or scalable process in place that helps them consistently and accurately secure and maintain consumer loan data elements necessary for monitoring fair lending risk.

Data extraction can often seem like a piecemeal process. For example, organizations might find that loan application fields are missing information, are filled in inconsistently, or have different naming conventions than the more uniform HMDA loan codes and fields. Organizations might also store their application data in other systems or include disparate pieces of information, such as the valuation of collateral for indirect or vehicle equity loans. Ensuring that consistent and correct data is available is paramount, and it can highlight the need to address potential data governance process gaps.

One common proxy method used for meaningful fair lending analysis is Bayesian Improved Surname Geocoding (BISG), which relies on an applicant’s first and last name and street address to determine the probability of an applicant’s race, ethnicity, and gender. An organization can run a statistical analysis on known customer information, loan decisions, and pricing information to evaluate fair lending risk. But organizations that apply this method should have strong data governance to avoid incomplete or inaccurate results.

Now is the time to be proactive and mitigate potential fair lending violations

Now is the time to be proactive and mitigate potential fair lending violations

Regulatory agencies and consumer activist groups monitor application data for risk. But many financial services organizations need help with data collection and accuracy, particularly with consumer loans. Data that is incorrect and poorly organized is a big problem because it could potentially lead organizations to wrong conclusions regarding their fair lending risk.

Data governance is an important first step. However, many risk assessments don’t thoroughly evaluate all consumer loan portfolios. The entire organization should be informed on specific data that needs to be collected, especially if it is just beginning to put a plan in place to analyze consumer loans.

Establishing a continual process of examination and improvement can help reduce negative impacts in several areas:

  • Risk assessments
  • Data analysis
  • Model assessment
  • Auditing and testing
  • Remediation
  • Validation
  • Reporting
  • Program enhancements

Crowe can help you assess your consumer loan data for fair lending risk

Crowe can help you assess your consumer loan data for fair lending risk

Identifying and managing fair lending risk is a challenge, and it can be hard to know where to start. Crowe fair lending risk specialists can help define the variables and loan attributes your organization needs to collect to determine your data’s impact and potential scope.

Our consultants combine data analytics with deep banking industry experience and regulatory knowledge to help organizations accurately assess fair lending risks and develop policies and procedures to address them. We can help you apply analytics, proactively assess risk, and prepare your program for what’s ahead.

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Let’s start a conversation about your fair lending risks and how to address them

Does your organization have questions or need help analyzing consumer loan data? Get in touch – our fair lending consultants would love to help.
Clayton J. Mitchell
Clayton J. Mitchell
Principal, AI Governance
+1 317 208 2438
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Niall Twomey
Niall Twomey
Principal, Consulting
+1 630 574 1806
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