Fair Lending Analytics

Is your fair lending analysis built to withstand regulatory scrutiny?

Standard approaches rely on data that isn't there—missing credit scores, unobserved risk factors, incomplete borrower profiles. Our peer-reviewed methodology solves the omitted variable problem that undermines traditional fair lending regression.

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The Problem with Standard Fair Lending Analysis

Traditional fair lending regression compares loan pricing or approval rates between protected and non-protected groups within a single institution, controlling for observable risk factors. For internal reviews with full proprietary data, this can work. But for peer comparisons using reported HMDA data—or when regulators analyze data to flag discriminatory practices—there is a fundamental flaw:

Key underwriting variables—most notably credit scores—are unavailable in HMDA data. When these risk factors are omitted, standard tests conflate discrimination with legitimate risk-based pricing.

Our Solution: External Benchmarking with Sensitivity Analysis

We developed a peer-reviewed methodology that addresses unmeasured confounding through cross-market comparisons. Rather than asking the hard-to-answer question "Does this institution discriminate?", we ask:

"How does this institution compare to appropriately matched peers?"

By comparing your institution's racial/ethnic pricing gaps to a matched benchmark of similar borrower profiles, we difference out the bias from unmeasured confounders that affects all institutions equally—isolating the excess disparity specific to your institution.

How We Compare to Traditional Approaches

Traditional Approach Our Methodology
Assumes all risk factors are observed Accounts for unmeasured confounders
Difficult to defend in examinations Peer-reviewed, academically rigorous
Binary "discriminate or not" conclusion Quantifies excess risk relative to peers
No sensitivity analysis E-value robustness measures

What We Deliver

Benchmark-Adjusted Disparity Analysis

  • Comparison to matched peer institutions
  • Excess gap estimates with confidence intervals
  • Statistically defensible conclusions

Sensitivity Analysis

  • E-values quantifying robustness to unmeasured confounding
  • Clear interpretation of how strong an unmeasured factor would need to be to explain results

Risk Stratification

  • Product-level analysis (mortgage, HELOC, auto, credit card)
  • Branch, region, and loan officer decomposition
  • Identification of specific sources of disparity

Exam-Ready Documentation

  • Methodology documentation for regulators
  • Management-ready summary
  • Technical appendix with full statistical output

Applications Across Products

Product Data Availability Our Approach
Mortgage (HMDA) Race/ethnicity reported External benchmarking
Home Equity Race often missing BISG proxy + benchmarking
Auto (Indirect) Race not reported BISG proxy + dealer analysis
Credit Card Race not reported BISG proxy + benchmarking
Small Business Limited demographics Census-based proxy

Why This Matters for Your Institution

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Exam Preparation

  • Identify issues before examiners arrive
  • Defensible methodology that withstands scrutiny
  • Documentation demonstrating proactive compliance
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Risk Management

  • Quantify fair lending risk across products and geographies
  • Prioritize remediation where risk is highest
  • Monitor trends over time

Legal Defense

  • Academic foundation withstands expert witness challenges
  • Sensitivity analysis demonstrates robustness
  • Peer comparison contextualizes any disparities

Our Credentials

28.8M Loans analyzed across seven years of HMDA data
Peer-Reviewed Methodology published in leading economics journals
Multi-Agency Exposed to real-world examinations across multiple regulatory agencies
SOC 2 Certified data security and operational controls

Engagement Options

Sophisticated analytics. Defensible methodology. Actionable results.

Ready to Strengthen Your Fair Lending Program?

Schedule a confidential consultation to discuss how our methodology can protect your institution.

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