A Systematic Review of Anti-Money Laundering Systems Literature - Exploring the Efficacy of Machine Learning and Deep Learning Integration

Aug 4, 2026

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Money laundering poses a complex, global threat that has accelerated the adoption of artificial intelligence to strengthen anti-money laundering (AML) controls. Focusing on machine learning and deep learning, this study conducts a systematic literature review to clarify how these methods are implemented, how effective they are, and what challenges limit deployment. Analysis of 26 studies from 2018 to 2023 shows that machine learning and deep learning support identification of money laundering schemes. Decision trees are widely used, and combined models improve detection of suspicious patterns despite constraints from data complexity, quality, and access.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Abstract

Introduction

Definition and stages of money laundering

Global impact and reported scale

Background and related literature

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration Nadia Husnaningtyas 1*, Ghalizha Failazufah Hanin2, Totok Dewayanto3, Muhammad Fahad Malik4 1 Master of Accounting Program, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia 2 Department of Accounting, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia 3 Department of Accounting, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia 4 Department of Law, Khwaja Fareed University of Engineering and Information Technology, RahimYar Khan, Pakistan *Corresponding Author(s) [Email: nadiahusnaningtyas@gmail.com](Email: nadiahusnaningtyas@gmail.com) | ABSTRACT | | | --- | --- | | Money laundering is a complex issue with global impact, | JEL Code: | | leading to the increased adoption of artificial intelligence (AI) to bolster anti-money laundering (AML) measures. | C38, C45, G21, G28, K42 | | AI, with machine learning and deep learning as key | DOI: | | drivers, has become an essential enhancement for AML strategies. Recognizing this emerging trend, this study | 10.31106/jema.v20i1.20602 | | embarks on a systematic literature review, aiming to | Article History: | | provide novel insights into the implementation, | Received 2023-01-15 | | effectiveness, and challenges of these sophisticated | Reviewed 2023-02-02 | | computational techniques within AML frameworks. A | Revised 2023-03-01 | | critical analysis of 26 selected studies published from 2018 to 2023 highlights the essential role of machine learning and deep learning in identifying money | Accepted 2023-03-24 | | laundering schemes. Notably, the decision tree algorithm | Licensed: | | 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. | CC-BY | | Keywords: Systematic Literature Review; Anti-Money Laundering; Financial Security; Machine Learning; Deep Learning | | Introduction Money laundering crimes are a significant global challenge, increasingly drawing attention over the last two decades. Money laundering is commonly understood as the act of concealing the illegal source of 'dirty' money, transforming it into seemingly legitimate and valid funds (Le-Khac et al., 2016) . This process, often referred to as 'cleaning' dirty money, involves disguising proceeds obtained from unlawful activities such as drug trafficking, illicit gambling, and tax evasion (Soltani et al., 2016; Salehi et al., 2017) . Money laundering is further characterized as the conversion of unaccountable money, which lacks legal legitimacy, into accountable funds, effectively legitimizing it within the financial system (Suresh et al., 2016) . The Financial Action Task Force (FATF, 2022) defines this process as concealing the origins of illegal proceeds. Often, this involves the use of legitimate mechanisms such as intricate business structures or international transactions to give the appearance of legality to the funds. In executing these schemes, perpetrators may act alone orin concert with criminal networks, legal firms, and even complicit officials who take advantage of regulatory loopholes to carry out such transactions (Ahen, 2022) . Money laundering typically unfolds in three stages—placement, layering, and integration, as outlined by various researchers (Alexandre & Balsa, 2015; Salehi et al., 2017; Savage et al., 2016; Soltani et al., 2016; Suresh et al., 2016) . The initial, placement, stage sees the illicit funds being discreetly deposited into the financial system. This is followed by the layering phase, where the money's illicit origin is obscured through a series of complex tran

FAQs

What is the purpose of the systematic literature review in this study?

The review aims to provide insights into how machine learning and deep learning are implemented within AML frameworks, including their effectiveness and key challenges.

Which machine learning technique is reported as most commonly used?

The decision tree algorithm is identified as the most frequently utilized technique across the selected studies.

Why might optimization of these advanced methods be difficult in AML systems?

Optimization is constrained by issues related to data complexity, data quality, and access to suitable datasets.

Aug 4, 2026

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