Anti money laundering system in detecting and preventing money laundering activities: a systematic review

Mallika B.K. and V.H. Ramasubramanian

Journal of Money Laundering Control, 2025, vol. 28, issue 2, 385-407

Abstract: Purpose - Money laundering has affected the economy in different ways, where the fraudulent activities are either domestic or abroad, resulting in financial instability globally. Anti-money laundering (AML) system is applied to detect and report any suspicious transactions. There are numerous approaches, techniques and algorithms in AML that are applied to fight against money laundering. This study aims to understand, identify and document the AML techniques applied to detect and prevent money laundering activities. Design/methodology/approach - A systematic literature review is applied for searching articles based on methods used for AML from the electronic database platform. For review, data is considered from journal articles, books and conference proceedings with a time framework from 2014 to 2024. Findings - In total, 53 papers were selected in the domain of money laundering concepts, issues and techniques of AML. The review articles are on the techniques of AML, such as machine learning, data mining, graph networks and artificial intelligence, which are applied to detect and prevent money laundering issues. Originality/value - Money laundering, being a global issue, is a threat to the economy and society. Detecting money laundering activities is utmost required; this study contributes in selecting the articles that are involved in the application of techniques of AML in detecting and preventing money laundering activities. The results of this study can provide support instruments to identify the better AML techniques that are useful for practitioners and industry experts working in the AML domain. Further research can be explored with other AML techniques.

Keywords: Money laundering; Anti-money laundering; Suspicious transactions; Layering; Integration; Fraudulent activities; Data mining; Machine learning; Risk-based approach; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jmlcpp:jmlc-07-2024-0108

DOI: 10.1108/JMLC-07-2024-0108

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