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Fair Lending Regression Analysis

Regression Analysis Identifies Compliance Risk for Banks, Mortgage Companies, Credit Unions & Auto Lenders

Sometimes, out-of-the-box Fair Lending analysis software isn't enough to determine if discrimination exists. That's where Regression Analysis can help. Regression will determine if age, race, gender, ethnicity or another prohibited basis factored into the decision to make the loan, or its pricing. 

Consider Regression Analysis when any of your origination, denial, and price disparity rates are higher than expected and may indicate a Fair Lending compliance issue

Regression Analysis can:

  • Explain credit and pricing decisions to determine if a prohibited basis factored into the credit decision or loan price.
  • Predict which applicants should have been approved or denied, loan price, and price-predicted APR, and then compare those results to what actually happened.
  • Investigate “outliers,” like loans that were denied or priced higher/lower than the model predictions.

We offer two kinds of Regression Analysis, as well as Overlap Analysis:

  • Loan Decisioning Regression Analysis: The Loan Decisioning Regression Analysis uses a logistic model. It attempts to capture all credit policy factors that inform the underwriting decision. This model tests for statistically significant relationships between decisioning and prohibited basis groups.
  • Loan Pricing Regression Model: The Loan Pricing Regression Analysis uses a linear model. It determines if loan pricing is similar for “similarly situated” applicants, that is, applicants with similar credit and loan characteristics.
  • Overlap Analysis: The Overlap Analysis, or Matched Pairs analysis, determines outliers using studentized residuals (a measure of discrepancy) of each loan for additional review.

Gain a Deeper Understanding of Your Fair Lending Risk!

Get this free Fair Lending Regression Analysis eBrief.