Upon completion of this course, participants will be able to:

• Articulate the distinct sources of **Model Risk** (e.g., data quality, design, implementation, use) specific to AI and Machine Learning models.
• Design and implement a comprehensive Model Risk Governance (MRG) framework tailored for advanced quantitative and AI-driven investment strategies.
• Develop rigorous validation methodologies, including backtesting, stress testing, and sensitivity analysis, for complex, non-linear ML models.
• Establish clear documentation standards and a formal approval process for models throughout their entire lifecycle (development to retirement).
• Apply **Explainable AI (XAI)** techniques (e.g., LIME, SHAP) to ensure interpretability and address the "black box" problem of deep learning models.
• Define the roles, responsibilities, and reporting lines for the three lines of defense in an AI-driven investment environment.
• Evaluate the ethical and compliance risks, including data privacy and bias, associated with the deployment of AI models.
• Design a continuous performance monitoring system for real-time model drift and decay detection.

• Chief Risk Officers and Heads of Model Risk Management.
• Internal Auditors and Compliance Officers.
• Quantitative Analysts responsible for model development and validation.
• Heads of Investment and Technology tasked with AI adoption.
• Regulators and Supervisors overseeing Central Banks and SWFs.
• Senior Management responsible for investment oversight committees.

• Workshops on Applying XAI Techniques (LIME/SHAP) to Financial Models
• Case Studies on Designing a Model Tiering System for a Reserve Portfolio
• Group Exercises in Writing a Model Validation Report for a Deep Learning Algorithm
• Discussions on Ethical AI and Bias Detection in Investment Algorithms
• Expert Presentations on Regulatory Expectations (SR 11-7, Basel) for AI Models
• Individual Assignments on Developing a Continuous Monitoring Dashboard Design

Unit 1: Foundations of Model Risk in AI

• Review of traditional Model Risk (e.g., SR 11-7) and its application to advanced quantitative models.
• Unique sources of risk in AI/ML: data risk, overfitting, concept drift, and adversarial attacks.
• The criticality of **Model Interpretability** and transparency for official sector mandates.
• Differentiating between model design risk, implementation risk, and usage risk in an AI context.
• Case studies of high-profile AI model failures and their financial consequences.

Unit 2: The End-to-End MRG Framework

• Establishing a formal Model Inventory and Tiering System based on risk and materiality.
• Designing the Model Risk Policy and Governance Charter for AI-driven investment.
• Defining the roles and responsibilities: model owners, developers, validators, and approvers (Three Lines of Defense).
• The Model Approval and Exception Process (MAP) for new and significantly changed AI models.

Unit 3: Validation Methodologies for AI Models

• Advanced backtesting techniques: walk-forward, cross-validation, and non-stationary time series challenges.
• Stress-testing AI models against extreme market regimes and tail events.
• Sensitivity analysis and input parameter stability for complex algorithms.
• Benchmarking and comparison of AI model performance against traditional benchmarks.
• Data validation and the critical role of independent data quality assurance.

Unit 4: Model Interpretability and Documentation

• Conceptual understanding and application of **Explainable AI (XAI)** techniques (LIME, SHAP, feature importance).
• Establishing rigorous documentation standards: clear logic, assumptions, limitations, and governance structure.
• The challenge of communicating model output and confidence levels to non-technical senior management.
• Designing an audit trail and version control system for continuous model development.

Unit 5: Ethical AI, Monitoring, and Auditing

• Addressing **Ethical AI** concerns: model bias, fairness, and discrimination in investment decisions.
• Designing a continuous performance monitoring system to detect model decay and concept drift in real-time.
• The role of Internal Audit (Third Line of Defense) in reviewing the MRG function and model compliance.
• The legal and regulatory environment for AI in finance (e.g., data privacy, transparency requirements).