There are many ways to analyze lending data for fair lending compliance, but few are as powerful as regression analysis — a statistical model that explains relationships among different variables. It goes beyond simple comparisons to reveal whether prohibited basis factors such as race, gender, or ethnicity are influencing lending decisions after accounting for legitimate credit factors.
However, before spending the time and resources conducting a regression analysis, it’s essential to determine if it’s the right fit for your financial institution’s (FI’s) fair lending program. Use the five questions below to determine whether regression analysis is the right investment for your fair lending program — and whether you're ready to make the most of it.
Related: Regression Analysis in Fair Lending: How It Works and When to Use It
Regression analysis is most effective for lenders with larger datasets, typically comprising 1,000 or more loan records, whether Home Mortgage Disclosure Act (HMDA) reportable data or non-HMDA data. It can be applied to nearly any loan data, with variations in products, terms, and outcomes, including credit cards, consumer loans, and indirect auto loans.
For high-volume lenders, manual review or statistical testing that doesn’t consider credit factors can be time-consuming — and with thousands of lending decisions each year, it may miss meaningful patterns. Variances in outcomes that are hidden by the range of data testing may seem minor; however, they can expose potential compliance, operational, and other risks that may trigger regulatory findings.
With 1,000-plus records, regression allows you to:
FIs with smaller portfolios may opt for traditional comparative analysis and basic statistical testing. Regardless of an FI’s size or lending data, ongoing analysis is still critical for fair lending compliance.
Related: Making Lending Fair for Everyone
Regression analysis requires reliable and well-structured data. Rushing into regression analysis without ensuring data integrity and accuracy is not only a waste of time, but it also doesn’t provide meaningful results.
If you agree with these statements, your data is most likely in a good place to support regression analysis:
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Some disparities are easy to explain. Perhaps there’s a data entry issue, or maybe only a handful of people applied for a specific loan (i.e., a small sample size). Others are not so simple.
When used for fair lending, regression analysis can reveal whether race, gender, ethnicity, age, or other prohibited factors have a measurable impact on loan approvals or pricing. If denial or pricing disparities appear high, regression helps identify whether legitimate credit factors explain those differences or if an actual gap remains.
Let’s say your analysis appears to show a group of protected class applicants is receiving higher rates and tends to have higher debt-to-income (DTI) ratios, lower credit scores, or higher loan-to-value (LTV) ratios. Regression analysis can test whether those variables account for the difference or if prohibited factors still play a role.
Regression models also estimate pricing and denial probabilities to identify outliers, which are cases where actual outcomes differ from expected ones. These can then be reviewed through matched-pair or comparative file analysis to uncover the underlying reasons or mitigate the findings.
If documented policies or legitimate credit factors can't fully explain your loan disparities, it may be time to incorporate more sophisticated analysis. Discover how Regression Ntelligence can help your FI uncover fair lending insights and take corrective actions in real time.
Simple, formula-based pricing (e.g., all borrowers with a 720 credit score and 80% LTV receive the same rate) makes disparities easier to detect. However, when pricing includes multiple changes over time, multiple product/pricing options, discretionary or relationship-based adjustments, regression analysis is most useful.
Complex pricing scenarios include
In these cases, multiple regression models can simultaneously analyze dozens of variables to determine their impact on pricing outcomes. Even if your current analysis shows no unexplained disparities, other factors — such as those listed above — may create patterns that are not detected by basic statistical testing in current data or over time.
Related: 7 Fair Lending Risks Every Financial Institution Needs to Know
Every compliance officer should understand the story their lending data tells before regulators do. Knowing this narrative allows your FI to explain disparities, provide context, and address risk proactively.
Compliance can be reactive — investigating issues after they occur — or proactive — using advanced analytics to identify potential problems early. Regression analysis supports a proactive approach by enabling your FI to identify which factors drive lending decisions, assess their appropriateness, pinpoint where processes need refinement, and determine how to prevent risk before it materializes.
Implementing regression analysis before an examiner recommends it demonstrates analytical rigor, a commitment to fair lending, and a sophisticated approach to risk management. It provides defensible, quantitative evidence — exactly what examiners expect.
Related: What You Need to Know Ahead of Your FI's Next Exam
If your institution has high-quality data, enough loan volume, identifiable disparities, and complex pricing, regression analysis can help deliver deeper insight into your fair lending risk.
Ready to put regression analysis to work for your FI? Explore our new tool, Regression Ntelligence, to learn how you can transform your lending data into deep insights.