A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration


  • Nadia Husnaningtyas Master of Accounting Program, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia
  • Ghalizha Failazufah Hanin Department of Accounting, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia
  • Totok Dewayanto Department of Accounting, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia
  • Muhammad Fahad Malik Department of Law, Khwaja Fareed University of Engineering and Information Technology, RahimYar Khan, Pakistan




Systematic Literature Review, Anti-Money Laundering, Financial Security, Machine Learning, Deep Learning


Money laundering is a complex issue with global impact, leading to the increased adoption of artificial intelligence (AI) to bolster anti-money laundering (AML) measures. AI, with machine learning and deep learning as key drivers, has become an essential enhancement for AML strategies. Recognizing this emerging trend, this study embarks on a systematic literature review, aiming to provide novel insights into the implementation, effectiveness, and challenges of these sophisticated computational techniques within AML frameworks. A critical analysis of 26 selected studies published from 2018 to 2023 highlights the essential role of machine learning and deep learning in identifying money laundering schemes. Notably, the decision tree algorithm stands out as the most commonly utilized technique. The combined use of both learning models has proven to significantly increase the effectiveness of AML systems in detecting suspicious financial patterns. However, the optimization of these advanced methods is still constrained by issues related to data complexity, quality, and access.


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How to Cite

Nadia Husnaningtyas, Ghalizha Failazufah Hanin, Totok Dewayanto, & Muhammad Fahad Malik. (2023). A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration. JEMA: Jurnal Ilmiah Bidang Akuntansi Dan Manajemen, 20(1), 91–116. https://doi.org/10.31106/jema.v20i1.20602