Artificial Intelligence and Its Impact on Banking Risk Management

Abstract

Recently, the banking sector has experienced an accelerated digital transformation driven by technological advancements and the emergence of Artificial Intelligence (AI) technologies, which have reshaped the operational and regulatory environment of financial institutions. This study aims to explore and analyze the impact of AI technologies—such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP)—on enhancing the efficiency and effectiveness of banking risk management, including credit risk, liquidity risk, operational risk, and market risk. To achieve this objective, the study adopts a descriptive-analytical methodology, reviewing the theoretical frameworks and practical applications of AI within the financial and banking industries. The study reveals several key findings: the adoption of AI technologies significantly contributes to improving real-time fraud detection and prevention, and enhances the accuracy of credit scoring for potential borrowers by analyzing vast amounts of unstructured and big data, ultimately reducing non-performing loans and mitigating financial and operational burdens. Based on these findings, the study recommends the urgent integration of smart systems as a core component within comprehensive banking risk mitigation strategies, while developing flexible regulatory environments capable of accommodating advanced predictive modeling to safeguard financial stability. This study is based on a descriptive approach, based on a review and analysis of academic literature, recent studies and reports issued by international financial institutions (such as the World Economic Forum and the Financial Stability Board). The independent variable (artificial intelligence and its various tools) is tracked to determine its impact on the dependent variable (bank risk management and regulatory compliance) and its influence on it.