Tailored for evaluating High-Risk (Tier 1) artificial intelligence assets—specifically Credit Underwriting and Anti-Money Laundering (AML) / Bank Secrecy Act (BSA) transaction monitoring models—in accordance with Federal Reserve SR 11-7 / OCC Bulletin 2011-12 guidelines and the NIST AI Risk Management Framework.
1. Governance & Regulatory Alignment
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[ ] SR 11-7 Model Inventory Registration: Ensure the candidate model is formally cataloged in the Enterprise Model Inventory with an assigned Model Owner, Risk Rating (Tier 1), and scheduled validation cadence.
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[ ] Regulatory Scope Verification: Confirm model alignment with relevant federal mandates:
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Credit Models: Equal Credit Opportunity Act (ECOA / Regulation B), Fair Credit Reporting Act (FCRA), and Fair Lending laws.
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AML/BSA Models: FinCEN regulatory thresholds, USA PATRIOT Act compliance, and Suspicious Activity Report (SAR) filing triggers.
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[ ] Model Governance Council Sign-off: Secure formal pre-deployment review approval from the Joint Model Risk Management (MRM) and AI Ethics Steering Committee.
2. Data Integrity, Privacy & Lineage
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[ ] Data Provenance & Lineage Mapping: Document the full lifecycle of training, validation, and production data streams, identifying all source databases and transformation pipelines.
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[ ] PII & Protected Class Sanitization:
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Verify that prohibited variables (e.g., race, gender, age, marital status, national origin) are excluded from credit training datasets.
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Implement automated scanning at API gateways to prevent raw PII leakage to third-party endpoints or unauthorized vector stores.
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[ ] Data Quality & Representativeness Audit: Evaluate training data for historical bias, missing values, sample selection bias, and geographical representation across regional operating territories.
3. Conceptual Soundness & Model Architecture
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[ ] Theory & Methodology Documentation: Document the underlying algorithmic methodology (e.g., Gradient Boosted Trees, Neural Networks, Logistic Regression) and justify why the chosen approach is suitable for the business objective.
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[ ] Explainability (XAI) Architecture:
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Credit Models: Implement SHAP (SHapley Additive exPlanations) or Integrated Gradients to ensure every credit decision can generate rank-ordered Adverse Action Reason Codes required by ECOA.
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AML Models: Ensure transaction anomaly flags map to interpretable behavioral metrics (e.g., sudden velocity changes, structured cash deposits) for SAR documentation.
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[ ] Hyperparameter & Optimization Audit: Validate that model hyperparameter tuning routines were logged and audited to prevent overfitting on historical training data.
4. Model Validation, Testing & Bias Diagnostics
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[ ] Out-of-Sample & Out-of-Time Testing: Conduct rigorous backtesting using historical data across varying economic cycles (e.g., rate fluctuation periods or regional credit stress).
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[ ] Fair Lending & Disparate Impact Analysis:
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Perform statistical tests (e.g., Marginal Effect Analysis, Adverse Impact Ratios) across protected demographic groups to confirm compliance with 80% rule thresholds.
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Test alternative less discriminatory algorithms (LESS/ADA) if baseline models demonstrate statistically significant demographic variance.
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[ ] AML Sensitivity & False Positive Testing: Benchmark AML model detection rates against baseline rules engine performance, auditing false-positive reduction ratios and false-negative leakages.
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[ ] Robustness & Adversarial Stress Testing: Conduct sensitivity testing against corrupted, synthetic, or adversarial data inputs to measure model stability under unexpected data drift.
5. Operational Deployment & Human-in-the-Loop (HITL) Controls
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[ ] Policy-as-Code & Boundary Enforcement: Program dynamic business rules into execution gateways (e.g., setting hard credit line caps or minimum credit score thresholds independent of model output).
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[ ] Human Escalation Thresholds:
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Credit Underwriting: Define clear dollar thresholds (e.g., automated execution restricted to loans $\le \$250,000$ with $\ge 92\%$ confidence score; mandatory human underwriter review for all higher exposure or lower confidence cases).
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AML Monitoring: Require mandatory BSA Officer review before closing any system-flagged suspicious transaction alert.
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[ ] Circuit Breakers & Rollback Triggers: Implement automated kill-switches to halt automated execution if real-time error rates, drift thresholds, or system latencies breach predefined limits.
6. Continuous Monitoring & Audit Readiness
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[ ] Data & Conceptual Drift Tracking: Deploy real-time telemetry to continuously monitor Population Stability Index (PSI) and Characteristic Stability Index (CSI) for input features.
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[ ] Immutable Audit Logging (WORM Storage): Record all model queries, feature inputs, confidence scores, and outputs in Write-Once-Read-Many storage environments to ensure full auditability during federal regulatory examinations.
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[ ] Periodic Re-Validation Schedule: Establish an automated trigger for comprehensive annual re-validation or emergency re-validation upon significant economic shocks or regulatory updates.