DIGITAL TRANSFORMATION OF ANTI-MONEY LAUNDERING CONTROLS IN COMMERCIAL BANKS

Abstract

This thesis examines the role of digital transformation in improving anti-money laundering (AML) controls in commercial banks. The rapid development of digital banking, mobile payments, online transactions, and remote financial services has increased the volume and complexity of financial operations, creating new challenges for the detection of suspicious activities and illicit financial flows. Traditional AML control mechanisms based primarily on predefined rules may have limitations in processing large volumes of transactions in real time. Therefore, the application of artificial intelligence, machine learning, Big Data analytics, digital identification, and automated transaction monitoring has become increasingly important. The study analyzes the role of these technologies in strengthening AML controls, improving risk assessment, detecting suspicious transactions, and increasing the efficiency of compliance processes. Particular attention is also given to data quality, cybersecurity, false-positive alerts, and the need for qualified specialists. The study concludes that integrating modern digital technologies into a unified risk-based AML framework can significantly strengthen the effectiveness and responsiveness of commercial banks' financial crime prevention systems.