The key to mitigating fair lending risks: Analytics

For all financial institutions, equitably serving customers and members is the right thing to do — and it’s required for regulatory compliance. That’s where fair lending monitoring can help. Regulators continue to list fair lending as a significant area of focus for regulatory compliance exams, and effective monitoring can support your institution in avoiding regulatory scrutiny, federal or state fines, and civil lawsuits. But if you identify fair lending risks, it could take weeks or even months to implement remediation plans to address them. As a compliance officer, it’s imperative that you have an accurate understanding of your institution’s fair lending risk now, or at least sooner rather than later, so you can execute any necessary remediation plans — before the next scheduled regulatory exam. In your pursuit of this goal, there’s a powerful solution you might not have considered yet: fair lending analytics.

How can compliance officers better identify fair lending risks?

Fair lending risks are pervasive and can present themselves in many areas of a financial institution. But many compliance officers have limited internal resources to assist them in managing those risks. For that reason, they must be strategic when deciding where to devote their time and energy. Unfortunately, many compliance officers allocate fair lending risk mitigants using qualitative criteria, rather than data-driven quantitative criteria. Without analyzing your data for a more thorough risk assessment, your compliance group could be focusing its resources on areas with insignificant risk, while potentially overlooking higher-risk lending areas.

What is fair lending analytics?

Fair lending analytics refers to the use of data to assess key fair lending risk areas, such as application distribution, denials, fallout, pricing, steering, and redlining. And it should be a key component of a financial institution’s risk management efforts; fair lending analytics aids institutions in developing a more cohesive, effective risk mitigation action plan. As a bonus, by comparing your analysis to peer benchmarking data, you can get a clearer picture of how your organization is performing compared to other financial institutions in the marketplace with similar loan origination volumes.

Fair lending analytics refers to the use of data to assess key fair lending risk areas, such as application distribution, denials, fallout, pricing, steering, and redlining.

Some community financial institutions believe the use of data analytics is unnecessary because they possess deep institutional knowledge of the neighborhoods they serve. No matter how strong this connection between institution and community might be, fair lending risk profiles can change over time. Sometimes the change is sudden, such as the result of a merger with or acquisition of another institution. Other times, the change can be more subtle, such as demographic shifts over time. Without periodic data analysis of your financial institution’s actual lending activity, your assumptions could be incorrect, and your fair lending risk assessment could be inaccurate or ineffectual.

Additionally, remember that state and federal regulators often use available Home Mortgage Disclosure Act (HMDA) data to identify institutions that warrant additional fair lending scrutiny or any potential redlining violations among institutions that are due for an examination. This is yet another reason to take a similar approach to your own fair lending risk assessment and monitoring, using data analytics to verify all customers and members are being treated equally. As a result, you’ll be prepared to stay one step ahead of regulatory exam teams.

Without periodic data analysis of your financial institution’s actual lending activity, your assumptions could be incorrect, and your fair lending risk assessment could be inaccurate or ineffectual.

What are some ways to get started with fair lending analytics?

To mitigate your institution’s fair lending risk more effectively, you should:

• Determine how you will analyze your loan data for fair lending risks, as well as how frequently you will analyze it. While the typical cadence of reviews varies among financial institutions, it shouldn’t be less frequent than annually.
• Determine the tools you’ll use to analyze the data. Many community financial institutions don’t have the right technology to effectively analyze their lending data for the purposes of risk identification. Even if they do have the right software, they might not have the time, resources, or expertise to use it at its full potential. If your institution doesn’t have the tools or expertise to perform a data analysis internally, find a qualified third party to assist.
• Perform the fair lending analysis — but remember that simply running a software program isn’t enough. Work to understand what the data means holistically and practically.
• Translate the findings into an actionable risk mitigation plan that meets the specific needs of your institution and the communities it serves. Formulate and document any new or revised fair lending monitoring activities your institution should undertake, based on the analytics results.
• Consider any other fair lending monitoring activities you’re performing. Do they tie back to the data in your most recent analysis? If not, ask whether those activities are actually effective at mitigating fair lending risk, or if they’re just a “check-the-box” exercise.

Community banks and credit unions take pride in working with their local customers and members, and feel a personal responsibility to treat people fairly — and that’s a good thing. Use that mindset to empower a culture of continuous improvement, rather than complacency. Effective monitoring of fair lending risk, fueled by data analytics, will help your institution serve its clientele equitably, avoid regulatory actions and penalties, and preserve the reputation and trust you’ve built with your community.