Future of Banking Risk: How Intelligent Stress Testing Is Transforming Financial Resilience

Abstract

Stress testing is a critical component of banking risk management and supervisory oversight, providing a structured means of evaluating whether financial institutions can absorb losses and maintain capital resilience under adverse conditions. Yet the increasing speed, complexity, and interconnectedness of financial markets raise questions about the ability of predominantly scenario-based and periodically executed approaches to identify emerging vulnerabilities. This conceptual paper examines how artificial intelligence (AI), machine learning (ML), alternative data, and advanced analytics could augment conventional bank stress testing by enabling more adaptive scenario generation, nonlinear risk estimation, continuous monitoring, and integrated portfolio analysis. The paper uses a structured conceptual synthesis of the stress-testing literature, supervisory principles, and recent research on AI-enabled financial risk modeling to identify limitations in traditional approaches and develop a framework for AI-enabled dynamic stress testing. The proposed framework links data and risk sensing, AI-assisted scenario generation, portfolio and balance-sheet simulation, capital and liquidity impact assessment, management decision-making, and model-risk governance. The analysis argues that AI should complement rather than replace established stress-testing governance and supervisory processes. In particular, model validation, explainability, data quality, human oversight, documentation, and ongoing monitoring are essential for responsible adoption. The paper contributes a research-oriented framework for understanding how banks can move from predominantly periodic stress exercises toward more adaptive and forward-looking risk intelligence while preserving regulatory discipline. It also identifies empirical research opportunities, including comparative testing of AI and traditional models, evaluation of scenario-generation quality, and measurement of governance effectiveness.