APPLICATION OF ARTIFICIAL INTELLIGENCE IN OPERATIONAL RISK MANAGEMENT OF FINANCIAL ORGANIZATIONS

Abstract

 At the current stage of digital technology development, financial organizations are forced to work with an ever-increasing volume of data, complex internal processes, and stricter regulatory requirements. As a result, the likelihood of operational risks increases, leading companies to turn to new technological solutions, one of which is artificial intelligence. This article explores the main applications of AI in the financial sector and analyzes the advantages and potential challenges of its implementation. It focuses on issues such as the opacity of algorithmic operations, the errors of generative models, and the risk of "cognitive debt." This risk is related to the fact that excessive automation can gradually lead to a loss of independent analysis and professional thinking skills among employees. One of the most effective approaches is the "cognitive complementarity" model, which involves using artificial intelligence to enhance human capabilities rather than replacing them entirely. The article also explores the key benefits and potential costs associated with implementing AI, and concludes with recommendations for creating a hybrid risk management system that combines technology and expert oversight.