Application of artificial intelligence in operational risk management in the banking sector

Abstract: This article examines the application of artificial intelligence (AI) in operational risk management within the banking sector. As operations become more complex and data volumes grow, traditional risk management methods face limitations. AI technologies such as machine learning, neural networks, and robotic process automation enhance risk identification, prediction, and response. The paper outlines practical use cases, including anomaly detection, automated incident processing, and AI-generated stress-testing. It also highlights key challenges such as data quality, model transparency, cybersecurity, and compliance. The study emphasizes the importance of explainability and ethical AI governance for safe and effective implementation in risk management.