Utilizing linear regression and random forest models for money laundering identification

Abstract

This paper investigates the effectiveness of traditional machine learning techniques, namely linear regression and random forest, in enhancing the detection of money laundering (ML) activities within financial systems. As ML schemes evolve in complexity, traditional rule-based methods struggle with high false favorable rates and a lack of adaptability, prompting the need for more sophisticated analytical approaches. In contrast to the complexities of deep learning models, this study explores the potential of these more accessible machine learning methods in identifying and analyzing suspicious transactional patterns. We apply linear regression and random forest (RF) models to transactional data to detect anomalous activities that could indicate ML. Our research thoroughly compares these models based on key performance metrics such as accuracy, precision, and recall. The findings suggest that while less complex than deep learning frameworks, linear regression, and RF models offer substantial benefits. They provide a more streamlined, interpretable, and efficient alternative to conventional rule-based systems in the context of ML detection. This study contributes to the ongoing discourse on the application of machine learning in financial crime detection, demonstrating the practicality and effectiveness of these methods in a critical area of financial security.