MANAGING COMPLIANCE RISKS IN BANKING SYSTEMS USING ARTIFICIAL INTELLIGENCE: AN ANALYSIS OF INTERNATIONAL STANDARDS AND NATIONAL PRACTICES

Abstract

This article systematically investigates the effectiveness of artificial intelligence (AI) technologies in managing compliance risks within the banking sector. The study evaluates the role of AI systems in transaction monitoring, detecting suspicious activities, risk assessment, and reducing operational costs. The research was conducted in accordance with international standards, including the Financial Action Task Force (FATF), the Basel Committee on Banking Supervision (Basel), and the International Organization for Standardization (ISO). These standards establish the most advanced frameworks for anti-money laundering, counter-terrorist financing, and risk management in banking institutions. The findings indicate that AI systems significantly enhance compliance processes in banks. Specifically, these systems improve the speed and accuracy of risk detection, reduce false positives, and enable real-time transaction monitoring. At the same time, challenges such as ensuring model transparency, explainability, data quality, and adherence to regulatory requirements have been identified. The article also analyzes the practical implementation of AI technologies in the banking sector of Uzbekistan, assessing the alignment of the national banking system with international standards and its readiness for digital transformation. The results of this study are not only scientifically relevant but also practically significant, providing banks and regulators with evidence-based recommendations to optimize compliance management using AI. This work can serve as an important reference for scholarly publications and as a foundation for developing new strategies in international financial communities for managing banking risks through AI technologies.