🔄 AI Governance in Regulated Industries
📐 Architecture Diagram
graph TD
A[AI Governance Framework] --> B[Model Risk Management]
A --> C[Regulatory Compliance]
A --> D[Data Governance]
A --> E[Operational Controls]
B --> B1[Model Validation]
B --> B2[Performance Monitoring]
B --> B3[Model Inventory]
C --> C1[EU AI Act]
C --> C2[GDPR]
C --> C3[Industry Regulators]
D --> D1[Data Lineage]
D --> D2[Consent Management]
E --> E1[Access Controls]
E --> E2[Audit Logs]
E --> E3[Incident Response]
style A fill:#6C63FF,color:#fff
style C fill:#FF6584,color:#fff
style B fill:#00C9A7,color:#fff
Banking and insurance are among the most heavily regulated industries. Deploying AI in these sectors requires robust governance frameworks that satisfy regulators while enabling innovation.
📋 Model Risk Management (SR 11-7)
In banking, the Fed's SR 11-7 guidance applies to AI models:
- Model Inventory: Document every AI model — purpose, inputs, outputs, limitations
- Independent Validation: Models must be validated by teams that didn't build them
- Ongoing Monitoring: Track model performance and drift continuously
- Materiality Assessment: Classify models by risk tier (Tier 1: Critical → Tier 3: Low risk)
🇪🇺 EU AI Act Impact
- High-Risk Classification: Credit scoring, insurance pricing, and fraud detection are 'high-risk' uses
- Requirements: Risk assessments, human oversight, transparency, data quality standards
- Penalties: Up to €35M or 7% of global revenue for non-compliance
🏦 Banking-Specific Requirements
- Fair Lending (ECOA/FHA): AI must not discriminate in lending decisions
- Explainability: Adverse action notices must explain WHY a loan was denied
- BSA/AML: AI-driven AML models need thorough documentation
🏥 Insurance-Specific Requirements
- Actuarial Standards: AI pricing models must meet actuarial soundness requirements
- Rate Filing: Some states require explanation of AI factors in rate calculations
- Unfair Discrimination: AI must not use prohibited factors (even indirectly)
✅ Building a Governance Program
- Establish an AI Risk Committee with C-suite sponsorship
- Create model risk policies aligned with regulatory expectations
- Implement model registry and lifecycle management
- Build automated monitoring and alerting
- Conduct regular internal and external audits
- Train all stakeholders on responsible AI practices
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