Model Risk Management Compliance
SR 11-7 and OCC guidance require financial institutions to document, validate, and monitor all models. ML models need model cards, validation reports, and ongoing performance monitoring.
Services
AWS SageMaker for Fintech & Financial Services
We build financial ML models on SageMaker that satisfy model risk management requirements — credit scoring with ECOA explainability, real-time fraud detection, and AML models with the documentation regulators expect.
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Summary
Build credit scoring, fraud detection, and risk models on AWS SageMaker. MRM-compliant model governance, explainable AI for fair lending, and real-time inference for financial applications.
Key Facts
- • Build credit scoring, fraud detection, and risk models on AWS SageMaker
- • Model Risk Management Compliance: SR 11-7 and OCC guidance require financial institutions to document, validate, and monitor all models
- • Real-Time Fraud Inference: Fraud scoring must happen within the payment authorization window — typically under 100ms — requiring low-latency SageMaker endpoint configurations
- • Regulatory Data Residency: Customer financial data used for model training must stay within specific AWS regions to comply with data sovereignty requirements across jurisdictions
- • Explainable Credit Models: SageMaker Clarify for SHAP-based feature attributions that translate to human-readable adverse action reasons — integrating with your credit decision workflow
Entity Definitions
- SageMaker
- SageMaker is an AWS service relevant to aws sagemaker for fintech & financial services.
- Lambda
- Lambda is an AWS service relevant to aws sagemaker for fintech & financial services.
- API Gateway
- API Gateway is an AWS service relevant to aws sagemaker for fintech & financial services.
- compliance
- compliance is a cloud computing concept relevant to aws sagemaker for fintech & financial services.
Frequently Asked Questions
How do we document SageMaker models to satisfy SR 11-7?
SR 11-7 requires model documentation covering purpose, methodology, data, limitations, and validation results. SageMaker Model Cards provide a structured documentation format. We augment with use-case-specific documentation: training data lineage (SageMaker Experiments), validation backtesting results, and ongoing monitoring thresholds — all exportable for regulatory submissions.
Can SageMaker provide per-prediction explanations for credit decisions?
Yes. SageMaker Clarify generates SHAP values for every prediction, identifying which features most influenced the decision. These feature attributions can be translated into standardized adverse action reason codes (e.g., "insufficient credit history", "high debt-to-income ratio") for ECOA-compliant adverse action notices.
What fraud detection latency is achievable with SageMaker?
SageMaker Real-time Inference with optimized models achieves P99 latency of 20-50ms for typical fraud scoring models. Using ml.c5.xlarge instances with Provisioned Concurrency, you eliminate cold starts entirely. End-to-end payment authorization latency (API Gateway → Lambda → SageMaker) is typically under 150ms.
Related Content
- AWS SageMaker — Parent service
Key Challenges We Solve
Fair Lending Explainability
Credit decisions influenced by ML models must be explainable under ECOA and FCRA — providing adverse action reasons to declined applicants requires per-prediction explainability.
Real-Time Fraud Inference
Fraud scoring must happen within the payment authorization window — typically under 100ms — requiring low-latency SageMaker endpoint configurations.
Regulatory Data Residency
Customer financial data used for model training must stay within specific AWS regions to comply with data sovereignty requirements across jurisdictions.
Our Approach
MRM-Compliant Model Pipeline
SageMaker Pipelines with built-in model registration, automated validation steps, Model Registry approval gates, and Model Cards that capture the documentation SR 11-7 requires.
Explainable Credit Models
SageMaker Clarify for SHAP-based feature attributions that translate to human-readable adverse action reasons — integrating with your credit decision workflow.
Low-Latency Fraud Endpoints
SageMaker Real-time Inference with Provisioned Concurrency for consistent sub-50ms fraud scoring, integrated into payment authorization flows via API Gateway.
Frequently Asked Questions
How do we document SageMaker models to satisfy SR 11-7?
SR 11-7 requires model documentation covering purpose, methodology, data, limitations, and validation results. SageMaker Model Cards provide a structured documentation format. We augment with use-case-specific documentation: training data lineage (SageMaker Experiments), validation backtesting results, and ongoing monitoring thresholds — all exportable for regulatory submissions.
Can SageMaker provide per-prediction explanations for credit decisions?
Yes. SageMaker Clarify generates SHAP values for every prediction, identifying which features most influenced the decision. These feature attributions can be translated into standardized adverse action reason codes (e.g., "insufficient credit history", "high debt-to-income ratio") for ECOA-compliant adverse action notices.
What fraud detection latency is achievable with SageMaker?
SageMaker Real-time Inference with optimized models achieves P99 latency of 20-50ms for typical fraud scoring models. Using ml.c5.xlarge instances with Provisioned Concurrency, you eliminate cold starts entirely. End-to-end payment authorization latency (API Gateway → Lambda → SageMaker) is typically under 150ms.
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