IMPROVING REAL-TIME ANTIFRAUD MONITORING SYSTEMS IN THE MANAGEMENT OF RISKS ASSOCIATED WITH RETAIL BANKING SERVICES AND THEIR APPLICATION IN BANKING PRACTICE

Abstract

This article examines the issues of improving real-time antifraud monitoring systems in the management of risks associated with retail banking services. The rapid development of digital banking services has significantly increased the exposure of banks to fraud-related risks, making effective risk management a critical priority. The study analyzes the theoretical foundations of antifraud systems, international best practices, and modern technological approaches to fraud detection and prevention.
In addition, a conceptual model for real-time antifraud monitoring based on artificial intelligence and machine learning algorithms is proposed. The model enables the identification of suspicious transactions and supports timely decision-making in risk management processes. The paper also highlights the current challenges in implementing antifraud systems in the banking practice of Uzbekistan and suggests practical recommendations for their improvement.
The results of the study contribute to enhancing the efficiency of retail banking risk management and strengthening the overall security and stability of the banking system.