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

• Published in: The Journal of supercomputing Vol. 80; no. 5; pp. 6264 - 6284
• Main Authors: Bakry, Ahmed N., Alsharkawy, Almohammady S., Farag, Mohamed S., Raslan, K. R.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Compilers; Computer Science; Interpreters; Machine learning; Money laundering; Processor Architectures; Programming Languages; Anti-money laundering; RFECV; Feature selection; Optuna; Random forest; XGBoost
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-023-05708-z

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.