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

• Evaluate the limitations of traditional stress testing and identify where **AI/ML** can enhance modeling realism and speed.
• Apply ML algorithms (e.g., LSTMs, VAEs) to generate more granular, non-linear **macroeconomic and financial stress scenarios**.
• Utilize deep learning and machine learning for more accurate, forward-looking modeling of **Probability of Default (PD)** and **Loss Given Default (LGD)**.
• Incorporate **Network Analysis** to model second-round effects and contagion losses between institutions.
• Develop a robust **Model Risk Governance** framework for validating and overseeing complex AI-driven stress testing models.
• Integrate alternative and granular data sources (e.g., trade data, firm-level financials) into the stress testing process using ML.
• Translate complex, AI-derived stress testing results into clear, actionable advice for supervisory decision-making.
• Understand the computational and data infrastructure required to support large-scale, AI-driven stress test exercises.

• Financial Stability and Macro-Prudential Supervision Analysts.
• Banking Supervisors and Stress Testing Model Owners.
• Quantitative Analysts and Data Scientists in Regulatory Bodies.
• Chief Risk Officers and Heads of Model Risk Management at Central Banks.
• Economists responsible for Macro-Scenario Generation.
• IT and Data Architects supporting Supervisory Technology (SupTech).

• Hands-on Python/R Workshops on Implementing Advanced PD/LGD ML Models
• Simulated Network Contagion Modeling Exercises under Stress Scenarios
• Group Activities on Designing an AI-Enhanced Scenario Generation Methodology
• Discussions on Model Risk Governance and XAI for Stress Testing
• Case Studies on Regulatory Use of Stress Testing (e.g., CCAR, EBA)
• Individual Assignments on Analyzing the Impact of AI on Stress Test Results and Capital Requirements

Unit 1: The Evolution of Stress Testing

• Review of regulatory stress testing (e.g., Dodd-Frank, EBA) and its limitations (linearity, fixed scenarios).
• The role of AI/ML in capturing non-linear relationships, feedback loops, and behavioral changes.
• Identifying key data and modeling gaps in traditional PD, LGD, and Exposure at Default (EAD) estimation.
• The concept of **Endogenous Risk** and how AI can help model it.
• Designing stress tests that explicitly incorporate market interconnectedness.

Unit 2: AI for Scenario Generation and Modeling

• Using Machine Learning (e.g., clustering, Factor Models) to identify latent risk factors and historical crisis patterns.
• Applying Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) to generate novel, severe, but plausible stress scenarios.
• Forecasting key financial variables (e.g., interest rates, house prices) under stress using LSTMs and attention mechanisms.
• Integrating alternative data and high-frequency indicators into the scenario specification process.
• Validating the severity and realism of AI-generated stress scenarios.

Unit 3: AI in Loss and Contagion Modeling

• Applying deep learning and advanced regression models to improve credit loss prediction (PD/LGD).
• Modeling the impact of stress scenarios on non-traditional balance sheet items (e.g., operational risk losses).
• Using **Network Analysis** to model contagion effects through interbank exposures and payment systems.
• Incorporating behavioral risk: modeling borrower and investor reaction to crisis conditions.
• Challenges of data sharing and granularity when modeling cross-bank exposures.

Unit 4: Model Risk and Governance

• Specific **Model Risk** challenges of AI/ML in stress testing (e.g., over-fitting, interpretability).
• Establishing a robust **Model Risk Governance (MRG)** framework for AI-driven stress models.
• Using **Explainable AI (XAI)** to ensure supervisors understand the drivers of capital losses.
• The need for independent validation, backtesting, and performance monitoring of AI models.
• Regulatory expectations for AI models used for prudential supervision and capital setting.

Unit 5: Implementation and Policy Integration

• Designing the computational infrastructure (cloud, GPUs) for large-scale, AI-driven stress tests.
• Integrating AI results into supervisory decision-making, capital buffers, and resolution planning.
• The role of AI in real-time stress testing and continuous monitoring of bank vulnerabilities.
• Communication strategies: translating complex results into clear policy recommendations for senior management.
• Future of stress testing: dynamic balance sheet reaction and linking to macro-prudential tools.