AI-Generated Synthetic Data for Stress Testing Financial Systems: A Machine Learning Approach to Scenario Analysis and Risk Management
Keywords:
AI-generated synthetic data, stress testingAbstract
As global financial institutions become increasingly linked, stress testing and risk management must improve. Traditional stress testing uses historical data, which may not represent severe or unusual market situations. AI-generated synthetic data may improve financial system stress testing via scenario analysis and risk management. Synthetic data is used by GANs, VAEs, and deep learning algorithms to mimic tough market circumstances. These models may reproduce real-world financial data distributions and include hypothetical scenarios for unusual or unobserved occurrences, giving financial organizations and regulators additional system resilience assessment tools.
Black swans are ignored in traditional stress testing, which looks backwards. Synthetic data generation systems' theoretical foundations and practical implementations are then examined, showing how they might enable forward-looking stress scenarios with non-linear dependencies and systemic feedback loops. This research examines how AI-generated synthetic data might enhance scenario assessments of financial system susceptibility to market shocks, liquidity crises, and contagion. Synthetic data helps financial organizations predict and prepare for interruptions in bad scenarios, improving risk management.
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