Automatic suppression of false positive alerts in anti-money laundering systems using machine learning

Abstract

Criminal activities generate an estimated $2 trillion in laundered money per year, highlighting the need for financial institutions to detect and report suspicious activity to protect their reputation. However, rule-based models commonly used for this purpose generate a high number of false positives, draining compliance team time, and increasing investigation costs. However, the application of machine learning in conjunction with rule-based models presents noteworthy implications, encompassing the potential reduction in false positives and the concomitant risk of machine learning inadvertently suppressing true positive alerts. This paper proposes a framework called automatic suppression based on XGBoost for anti-money laundering (ASXAML) to enhance detection by reducing false positives. ASXAML leverages recursive feature elimination with cross-validation for optimal feature selection. Subsequently, Optuna is employed to fine-tune hyperparameters for the XGBoost model. Results indicate that ASXAML achieves an optimal balance between reducing false positives and avoiding missed money laundering events, with an 86% F-beta score and only 11% money laundering customers were incorrectly closed out of 1926 in the test data.