Model Risk Management: Validation and Governance

Module: Fintech AI | Difficulty: Advanced

Model Risk (SR 11-7)

Validation Pillars

1. Conceptual soundness
2. Outcomes analysis
3. Benchmarking
4. Sensitivity analysis

Model Inventory

| Tier | Complexity | Validation | |------|-----------|------------| | Low | Simple | Self-assessment | | Medium | Moderate | Independent review | | High | Complex | Full validation |

class ModelRiskFramework:
    def __init__(self):
        self.models = {}
    def register_model(self, name, tier, owner):
        self.models[name] = {
            'tier': tier, 'owner': owner,
            'last_validation': None, 'next_validation': None
        }
    def validate(self, name, model, test_data):
        results = {}
        results['conceptual'] = self.assess_conceptual(model)
        results['outcomes'] = self.analyze_outcomes(model, test_data)
        results['sensitivity'] = self.sensitivity_analysis(model)
        self.models[name]['last_validation'] = results
        return results

Research Insight: Model risk management is critical for financial institutions. The 2008 crisis demonstrated the dangers of relying on unvalidated models. SR 11-7 requires comprehensive model governance, including independent validation and ongoing monitoring.