The aim of this study is to comprehensively examine the impact of artificial intelligence (AI) technologies on risk management in the banking sector. The research focuses on how machine learning, natural language processing, and predictive analytics enhance credit scoring, fraud detection, and regulatory compliance. A mixed-method approach was applied, including a systematic literature review, machine learning based analysis of open banking datasets(Kaggle), and a survey of 200 bank employees in the Middle East. The findings demonstrate that ensemble models such as XGBoost and Random Forest significantly outperform traditional techniques in prediction accuracy and classification efficiency. The scientific novelty lies in the development of a comprehensive framework for integrating AI into banking risk management systems while addressing ethical and regulatory concerns, practices, and minimize financial losses. Furthermore, the study identifies key challenges, including data privacy concerns, model interpretability, and regulatory constraints, that may hinder the effective integration of AI in banking. The research concludes that AI-driven models have the potential to revolutionize financial risk governance by enabling proactive, data-driven decision-making and fostering operational resilience. Strategic recommendations are provided to guide financial institutions and policymakers in implementing ethical and secure AI frameworks for sustainable innovation. Key words: artificial intelligence, risk management, the financial health of banks, machine learning decision-making, banking sector.
Kuanova et al. (Fri,) studied this question.