Big Data Analytics for Anti-Money Laundering Compliance in the Banking Industry

Abstract

The rapid growth of the digital economy and the complexity of financial transactions have led to a significant increase in money laundering activities, posing a problem that threating the global financial system. This study examines the use of big data techniques to strengthen anti-money laundering (AML) measures, specifically in the areas of suspicious activity reporting, customer due diligence, and trade-based money laundering. According to the analysis of these applications, it has been demonstrated that big data techniques can substantially strengthen the detection and prevention of money laundering activities by providing more accurate and timely insights, streamlining compliance processes, and facilitating cross-border collaboration among financial institutions and regulators. However, challenges related to data quality, privacy, security, and the need for continuous improvement to keep up with evolving money laundering schemes remain. Overall, this research highlights the importance of leveraging big data techniques in AML and their potential for combating money laundering, providing valuable insights and solutions for maintaining the integrity of the global financial system.