Leveraging AI/ML for Enhanced Detection and Prevention of Money Laundering Activities

Abstract

Money laundering poses significant risks to the  industry, undermining the integrity of financial institutions and facilitating illicit activities. Traditional methods for detecting and preventing money laundering are often inadequate in addressing the complexity and evolving nature of these activities. This paper explores the potential of Artificial Intelligence (AI) and Machine Learning (ML) to enhance the detection and prevention of money laundering. By leveraging advanced analytics, pattern recognition, and predictive modeling, AI/ML-based systems can provide a more effective and efficient approach to identifying suspicious transactions and activities. This paper reviews current AI/ML techniques, discusses their applications in anti-money laundering (AML) efforts, and highlights the challenges and future directions in this field.