Comparison of Machine Learning Algorithms for Anti-Money Laundering Applications

Abstract

Money laundering is a widespread problem threatening international financial stability and is connected to all forms of crimes. Detection and prevention of suspicious transactions can be done by using the AML systems; however, traditional rule-based methods fall short because they are very rigid and inefficient. Machine learning (ML) presents a revolutionary approach to the analysis of large volumes of financial data by AML systems to find complex patterns and adapt to new tactics in laundering. The paper discusses the supervised, unsupervised, and hybrid algorithms in the application domain of AML, including their strengths, weaknesses, and performance metrics. It also explores some of the concerns associated with the adoption of ML in AML, including a lack of data, regulation, and operational limitations. Directions for the future include explainability, federated learning, and blockchain integration to make the AML systems scalable, accurate, and transparent. Advanced ML techniques can be incorporated by financial institutions to address money laundering better, which will ensure safe and compliant financial ecosystems.