Fraud Detection and Anti-Money Laundering

Abstract

Money laundering has affected the global economy for many years, and there are several methods of solving it, is presented in this survey. The rise of terrorism has brought to the forefront the critical issue of financing, with illicit funds. Modern society faces significant financial risks from various forms of fraud, necessitating the evolution of advanced detection techniques. This paper provides a comprehensive survey of methodologies to identify and prevent fraudulent activities, including credit card fraud, telecommunication fraud, and computer intrusion. Concurrently, the threat of terrorism underscores the need for effective financing, prompting terrorist organizations to engage in illicit activities such as bank scams, fraud, donations, taxation evasion, ransom, and oil sales. Laundering proceeds from these activities into legitimate channels poses a critical challenge. The paper emphasizes the contribution of these methodologies in combating financial crime and safeguarding global economies. This survey focuses on the technical aspects of anti-money laundering (AML) systems, exploring machine learning algorithms and techniques designed to detect money laundering patterns, identify abnormal behaviors, and track unlawful financial networks.