Machine Learning Model Validation & Testing for AML Compliance

Executive Summary

As financial institutions increasingly adopt machine learning for AML compliance, robust model validation becomes critical. This whitepaper presents a comprehensive framework for validating ML models, covering conceptual soundness, ongoing monitoring, outcomes analysis, and governance— meeting regulatory expectations while ensuring model reliability.

1. Model Risk Management Framework

1.1 SR 11-7 Guidance

The Federal Reserve's Supervisory Guidance on Model Risk Management (SR 11-7) establishes three pillars of effective model risk management:

1.2 Model Risk Categories

We categorize our ML models by risk level to determine validation rigor:

2. Conceptual Soundness

2.1 Model Design Validation

Independent validators review model design documentation to assess:

• Business Objective Alignment: Does the model address the intended AML detection use case?
• Theoretical Foundation: Is the ML approach appropriate for the problem domain?
• Data Appropriateness: Is training data representative of production distribution?
• Feature Engineering: Are features relevant, non-collinear, and interpretable?
• Model Selection: Was model architecture chosen through rigorous comparison?
• Hyperparameter Tuning: Were parameters optimized systematically?

2.2 Graph Neural Network Validation

For our GraphSAGE-based transaction network model, we validate:

2.3 Anomaly Detection Validation

For unsupervised anomaly detection models (Isolation Forest, Autoencoders), we assess:

• Contamination Rate: Assumed percentage of anomalies matches empirical observations
• Feature Space: High-dimensional features don't degrade isolation performance
• Reconstruction Error: Autoencoder bottleneck preserves normal patterns while flagging anomalies
• Threshold Calibration: Anomaly score cutoffs balance precision and recall

3. Performance Testing

3.1 Hold-Out Test Set Evaluation

All models are evaluated on time-based hold-out test sets (20% of data, most recent 3 months):

3.2 Benchmark Targets

Our models must meet or exceed these performance thresholds:

3.3 Stress Testing

We stress test models under adverse scenarios:

• Volume Stress: 10x transaction volume spikes (Black Friday, year-end)
• Data Quality Degradation: Missing features, delayed data feeds
• Novel Typologies: Emerging money laundering patterns not in training data
• Adversarial Attacks: Intentional evasion attempts by sophisticated actors
• Regime Changes: Economic shocks, regulatory changes, pandemic events

4. Ongoing Monitoring

4.1 Production Performance Tracking

Real-time dashboards track model performance in production:

4.2 Model Drift Detection

We employ statistical tests to detect drift:

• Population Stability Index (PSI): Measures feature distribution drift (alert if PSI > 0.25)
• Kolmogorov-Smirnov Test: Detects distributional changes in continuous features
• Chi-Square Test: Identifies drift in categorical features
• Prediction Drift: Monitors changes in model output distribution

4.3 Back-Testing

Monthly back-tests compare model predictions against subsequently confirmed outcomes:

• Transactions flagged as high-risk that led to SARs (true positives)
• Cleared alerts that were later confirmed suspicious (false negatives)
• Low-risk transactions involved in confirmed money laundering (critical misses)
• Regulatory findings identifying missed suspicious activity

5. Outcomes Analysis

5.1 SAR Quality Analysis

We analyze whether model-generated alerts lead to high-quality SARs:

5.2 False Negative Analysis

Quarterly reviews identify and analyze false negatives:

• Lookback Analysis: Review past transactions of entities later confirmed suspicious
• Peer Comparison: Identify similar entities the model correctly flagged
• Feature Analysis: Determine which features could have detected the activity
• Model Retraining: Incorporate false negatives into training data

5.3 Operational Efficiency

Beyond detection accuracy, we measure operational impact:

6. Bias & Fairness Testing

6.1 Protected Attribute Analysis

While AML models don't explicitly use protected attributes, we test for proxy discrimination:

• Geographic Bias: Ensuring high-risk jurisdictions don't proxy for ethnicity
• Name Analysis: Verifying entity names don't introduce cultural bias
• Occupation Bias: Preventing discrimination against certain professions
• Network Effects: Avoiding guilt-by-association in graph models

6.2 Fairness Metrics

We calculate fairness metrics across demographic segments:

7. Champion/Challenger Framework

7.1 Continuous Model Improvement

We maintain a champion/challenger framework for model evolution:

• Champion Model: Current production model serving 100% of traffic
• Challenger Models: 2-3 candidate models scoring transactions in shadow mode
• Evaluation Period: 3-month comparison on identical production data
• Promotion Criteria: Challenger must show > 5% improvement in key metrics
• Gradual Rollout: New champion deployed to 10% → 50% → 100% of traffic

7.2 A/B Testing Framework

For feature or architectural changes, we conduct controlled A/B tests:

• Randomly assign entities to control (champion) or treatment (challenger) groups
• Ensure groups are balanced across relevant characteristics
• Monitor for statistically significant differences in SAR conversion rates
• Account for multiple testing with Bonferroni correction

8. Model Documentation

8.1 Model Inventory

We maintain a comprehensive model inventory documenting:

8.2 Model Cards

Following industry best practices, each model includes a model card specifying:

• Intended Use: Transaction monitoring, entity risk scoring, network analysis
• Training Data: Data sources, time period, labeling methodology
• Performance Metrics: Accuracy, precision, recall on test sets
• Limitations: Known failure modes, edge cases, monitoring requirements
• Ethical Considerations: Bias testing results, fairness metrics

9. Third-Party Validation

9.1 Independent Review

High-risk models undergo annual independent validation by qualified third parties:

• Big Four accounting firms with ML expertise
• Specialized model risk management consultancies
• Academic partnerships with financial ML research groups

9.2 Validation Deliverables

Independent validators provide:

10. Regulatory Examination Readiness

10.1 Examination Artifacts

During regulatory examinations, we provide:

• Model inventory and risk ratings
• Development documentation and theoretical justification
• Independent validation reports
• Performance monitoring dashboards
• Back-testing results and false negative analysis
• Model governance policies and procedures
• Change management logs and version history

10.2 Regulatory Hot Topics

Examiners frequently focus on:

11. Conclusion

Effective model validation is essential for regulatory compliance, risk management, and maintaining stakeholder trust. The nerous.ai validation framework provides comprehensive coverage of conceptual soundness, ongoing monitoring, and outcomes analysis—meeting regulatory expectations while enabling continuous model improvement.