AI-AUGMENTED STRESS TESTING AND SCENARIO ANALYSIS IN BANKING RISK MANAGEMENT

Abstract

An intensive introduction of artificial intelligence (AI) into financial systems has changed the nature of risk management in the banking industry. The use of traditional forms of stress testing which are usually deterministic, backward-looking, relying on highly fixed macroeconomic assumptions are heavily flawed to forecast emerging risks. AIs based on stress testing and scenario analysis incorporate the more modern methods of computational AI, like machine learning, deep learning, and natural language processing, to provide a higher level of predictive confidence, flexibility, and real-time responsiveness. In this paper, the author will discuss how AI reinforces stress testing practices and protocols, identifies indicators of stress, enhances scenario construction and the process of dynamic risk evaluation. The analysis focuses on the conceptual analysis of the transformative effects of AI on credit, market, operational, and liquidity risk modeling to demonstrate the potential change in current models and frameworks described by the new regulations across the world and new technological innovations in the field. The results indicate that AI-based stress testing will have great potential in enhancing resilience, risk transparency, and supervisory compliance and imply the risks of data governance, interpretability, and ethical concerns. The study finishes by stating future directions of hybrid AI-human decision-making and the necessity to establish formidable regulation principles.