Vol:.(1234567890) The Journal of Supercomputing (2024) 80:6264–6284 https://doi.org/10.1007/s11227-023-05708-z 1 3 Automatic suppression of false positive alerts in anti‑money laundering systems using machine learning Ahmed N. Bakry 1  · Almohammady S. Alsharkawy 1  · Mohamed S. Farag 1  · K. R. Raslan 1 Accepted: 1 October 2023 / Published online: 20 October 2023 © The Author(s) 2023 Abstract Criminal activities generate an estimated $2 trillion in laundered money per year, highlighting the need for financial institutions to detect and report suspicious activ- ity 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, encompass- ing the potential reduction in false positives and the concomitant risk of machine learning inadvertently suppressing true positive alerts. This paper proposes a frame- work 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 reduc- ing 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. Keywords XGBoost · Optuna · Anti-money laundering · Random forest · Feature selection · RFECV * Ahmed N. Bakry AhmedNagyBakry@gmail.com Almohammady S. Alsharkawy alm.alsharkawy@azhar.edu.eg Mohamed S. Farag mohamed.s.farag@azhar.edu.eg K. R. Raslan kamal_raslan@yahoo.com 1 Department of Mathematics, Faculty of Science, Al-Azhar University, Nasr City, Cairo, Egypt