FAIR LENDING & AI GOVERNANCE · T452
v1.0 cat-28 · LendTech Compliance

Fair Lending AI Bias Assessment

Evaluate an AI/ML credit decision model for fair lending compliance. Assesses disparate impact, model explainability, adverse action requirements, and governance against ECOA, FHA, EU AI Act, and emerging AI model risk guidance.

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Model Characteristics
Fair Lending Testing
Regulatory Context
Risk Dimension Scores (0 = Low  ·  100 = Maximum Risk)
Overall Risk Assessment
Critical Flags & High-Priority Findings
Regulatory Obligation Checklist
Remediation Roadmap (Priority Order)
Regulatory Citations & Methodology Notes
1Equal Credit Opportunity Act (ECOA), 15 U.S.C. §1691 et seq., and Regulation B (12 CFR Part 1002): prohibits discrimination in any aspect of a credit transaction on the basis of race, color, religion, national origin, sex, marital status, age, or receipt of public assistance. Requires adverse action notices with specific reasons (Reg B §1002.9).
2CFPB Circular 2022-03 (Adverse Action Notification Requirements in Connection with Credit Decisions Based on Complex Algorithms): clarifies that ECOA/Reg B adverse action reason code obligations apply fully to AI/ML models; "black box" is not an excuse for failing to provide specific reasons for adverse credit decisions.
3EU Artificial Intelligence Act (Regulation (EU) 2024/1689), Annex III §5(b): classifies AI systems used for evaluating creditworthiness or establishing credit scores as high-risk AI. Requires conformity assessment, registration in the EU database, post-market monitoring, and human oversight prior to market deployment.
4Fair Housing Act, 42 U.S.C. §3605: prohibits discriminatory practices in residential real estate transactions including mortgage lending. The disparate impact standard (Inclusive Communities, 576 U.S. 519 (2015)) applies to algorithmic systems.
5Interagency Statement on Model Risk Management for AI/ML (OCC Bulletin 2021-xx; Fed SR 11-7 as applied to AI): establishes supervisory expectations for AI model risk management including independent validation, ongoing monitoring, explainability, and documentation. CFPB, OCC, Fed, FDIC, and NCUA have all issued complementary guidance on AI/ML model risk in credit decisions.