How are you monitoring non-HMDA-related loans for risk?

Clayton J. Mitchell, Niall Twomey
1/4/2022
How are you monitoring non-HMDA-related loans for risk?

Home Mortgage Disclosure Act (HMDA) data collection and reporting has been in place since 1975, and it still presents a common challenge for many financial services organizations. However, monitoring non-HMDA-related loans for fair lending risk can be even more difficult since organizations are prohibited from gathering information on an applicant’s race, gender, and ethnicity.

While HMDA-reportable loans require lenders to provide these details, non-HMDA-applicable loans don’t require the same level of information. That can make monitoring for fair lending risk more complex for non-HMDA loan data related to both consumer and commercial lending – and especially for small businesses and applications that originate through third-party agents such as fintechs and indirect lenders.

Doing nothing with non-HMDA loan data, however, is obviously not an option. Regulators are continually scrutinizing organizations for noncompliance, and a single fair lending violation can tarnish your organization’s reputation, result in civil money penalties or orders of consumer redress, and potentially derail strategies for growth.

Collecting non-HMDA-related loan data can be frustrating for many reasons

Collecting non-HMDA-related loan data can be frustrating for many reasons

Many organizations might not have a repeatable or scalable process in place to consistently and accurately secure and maintain the necessary non-HMDA loan data elements needed to monitor fair lending risk.

Data extraction can often feel 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 also might store their application data in different 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 highlights 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 names 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 that still means organizations 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 struggle with data collection and accuracy, particularly with non-HMDA loans. Data that is incorrect and not well organized is a big problem – and could potentially lead organizations to wrong conclusions surrounding fair lending risk.

While data governance is an important first step, many risk assessments don’t thoroughly evaluate non-consumer loans in addition to those that are not dwelling-secured. The entire organization should be informed on specific data that needs to be collected, especially for organizations that previously might not have had a plan in place to analyze non-HMDA-applicable loans.

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

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

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

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

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 the impact and potential scope of your data.

Crowe fair lending consultants combine data analytics with deep banking industry experience and regulatory knowledge to help organizations accurately assess fair lending risks and then develop the 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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Does your organization have questions or need help analyzing non-HMDA-applicable loan data? Get in touch – our fair lending consultants would love to help.
Clayton J. Mitchell
Clayton J. Mitchell
Managing Principal, Fintech
Niall Twomey
Niall Twomey
Principal, Financial Services Consulting