Monetary Policy Uncertainty, U. S Bank Stability and the Rise of AI-Enabled Stress Testing: A Review
Basirat Adebimpe Hammed *
Economics Department, College of Business and Analytics/ Southern Illinois University, Carbondale/Illinois, USA.
Philip Williams Appiah-Agyei
Department of Political Science & Public Administration, Mississippi State University, USA.
Charles Zormelo
Department of Economics and Finance, University of Texas at El Paso, El Paso, TX, USA.
Kayode L. Ogunsusi
Risk Management, CFR, American Express, Phoenix Arizona, USA.
*Author to whom correspondence should be addressed.
Abstract
This study examines the impact of Monetary Policy Uncertainty (MPU) on the U.S. banking system through credit supply, liquidity pressures, and capital adequacy. Using an integrative literature review over the period from 2010 to 2025, this study was conducted through PRISMA guidelines, combining Boolean search strategies across databases including EconLit, Scopus, and ScienceDirect.
The results show that MPU deprives credit supply, resulting in tight lending conditions and reduced loan performance as banks become risk-averse. As banks do not want to maintain capital reserves than lend, liquidity pressures increase, further exacerbated by a decline in liquidity pressure. AI and machine learning (ML) models have been useful for predicting bank distress, but there are still concerns about the privacy and interpretation of the AI models. There is no explanationable AI on stress testing, which can be utilised in real-time policy decisions, where regulators need to know how AI is thinking about their decisions. The real-time news, social media content data and predictive models of forecasting models remain unseen in the banking stability literature, potentially giving deeper insights into market sentiment and potential risks.
Based on the findings, the study suggests that the adoption of AI-based forecasting practices into regulatory policies could help build predictive capacity and enable more informed decisions that will contribute to resilience in financial systems. In addition, future research should focus on improving transparency and real-time macro-micro linkages to allow dynamic AI systems that inform regulatory processes, for prediction accuracy and overall banking stability.
Keywords: Banking stability, monetary policy uncertainty (MPU), stress-testing simulations, natural language processing (NLP), U.S.