Lilly Tech Systems
Model Risk Management SR 11-7
Master the Federal Reserve's SR 11-7 guidance on model risk management. Learn to build comprehensive model inventories, conduct independent validations, implement ongoing monitoring, and establish best practices for managing model risk in financial institutions and regulated enterprises.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is model risk, why it matters for AI/ML, and the regulatory context for model risk management.
2. SR 11-7 Framework
Deep dive into the three pillars of SR 11-7: model development, model validation, and governance.
3. Model Inventory
Building and maintaining a comprehensive model inventory with risk tiering and metadata management.
4. Validation
Independent model validation processes, challenger models, conceptual soundness, and outcome analysis.
5. Monitoring
Ongoing model performance monitoring, drift detection, threshold management, and escalation procedures.
6. Best Practices
Extending SR 11-7 to AI/ML models, automation opportunities, and building a model risk culture.
What You'll Learn
By the end of this course, you'll be able to:
Understand SR 11-7
Master the Federal Reserve's guidance on model risk management and its three pillars of sound practice.
Build Model Inventories
Create comprehensive model inventories with risk tiering, metadata standards, and lifecycle tracking.
Conduct Validations
Design and execute independent model validation programs with challenger models and outcome analysis.
Monitor Models
Implement ongoing monitoring frameworks with drift detection, performance tracking, and automated alerting.