SMARTER RISK, STRONGER BANKS: HOW MACHINE LEARNING, ACCURATE PREDICTIONS, AND BOARD OVERSIGHT ENHANCE BANKS FINANCIAL STABILITY

Abstract

This study examines how machine learning based risk management influences bank financial stability, with risk prediction accuracy acting as a mediating mechanism and board oversight serving as a moderating factor. Drawing on agency theory, the research investigates how improved information processing and governance oversight contribute to more stable financial outcomes. A quantitative cross sectional research design was employed, and data were collected from managerial employees working in the banking sector. The data were analyzed using structural equation modeling through SmartPLS, enabling simultaneous evaluation of measurement and structural models. The findings reveal that machine learning based risk management significantly improves financial stability and enhances the accuracy of risk prediction. The results further demonstrate that prediction accuracy mediates the relationship between machine learning adoption and financial stability, indicating that predictive capability is a key mechanism through which technological tools influence financial outcomes. Additionally, board oversight significantly strengthens the relationship between prediction accuracy and financial stability, highlighting the importance of governance mechanisms in supervising technology driven decision systems. The study contributes to the literature on financial risk management and digital governance by integrating technological capabilities, predictive mechanisms, and governance oversight within a unified analytical framework. The findings offer valuable implications for financial institutions, policymakers, and scholars seeking to understand how advanced analytical technologies and governance structures can jointly enhance financial stability in modern banking systems.