Artificial Intelligence for stress testing and risk assessment in financial institutions

Banks and financial institutions are using Artificial Intelligence to change how they handle stress testing and risk assessment. This review looks at current AI applications in financial risk management and examines how well these technologies work compared to traditional methods. Traditional financial stress testing faces challenges with nonlinear dependencies and emerging risks, while deep learning techniques can enhance predictive accuracy and robustness. The study covers regulatory requirements under frameworks like Basel III, implementation challenges, and performance measures that institutions use to evaluate AI systems. AI and machine learning technologies enhance data quality, automate workflows, strengthen compliance monitoring, and increase model precision, helping financial institutions streamline their CCAR processes while ensuring greater accuracy and transparency. However, banks still face significant hurdles in making AI models explainable, addressing bias issues, and managing systemic risks. The research shows that AI-driven approaches often perform better than conventional methods in accuracy and speed, but institutions need to balance innovation with regulatory compliance and risk management. This review provides insights for bank executives, risk managers, regulators, and researchers working to understand how AI is reshaping financial risk management and what it means for banking stability.