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Automatic suppression of false positive alerts in anti-money laundering systems using machine learning.
- Source :
-
Journal of Supercomputing . Mar2024, Vol. 80 Issue 5, p6264-6284. 21p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09208542
- Volume :
- 80
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Supercomputing
- Publication Type :
- Academic Journal
- Accession number :
- 176005185
- Full Text :
- https://doi.org/10.1007/s11227-023-05708-z