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All Journal Diponegoro Journal of Accounting JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen
Hanin, Ghalizha Failazufah
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Economics, Econometrics & Finance

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PERAN MACHINE LEARNING DAN DEEP LEARNING DALAM PENDETEKSIAN PENCUCIAN UANG – A SYSTEMATIC LITERATURE REVIEW Hanin, Ghalizha Failazufah; Dewayanto, Totok
Diponegoro Journal of Accounting Volume 13, Nomor 3, Tahun 2024
Publisher : Diponegoro Journal of Accounting

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Abstract

This study aims to explore and uncover the complex phenomena in the financial world, particularly concerning the prevention and mitigation of money laundering, which is becoming increasingly rampant. In an era of advancing technology, the application of artificial intelligence such as machine learning and deep learning has become essential to enhance the effectiveness of anti-money laundering systems. This research employs a systematic literature review to analyze the role of AI, machine learning, and deep learning in detecting money laundering techniques. By collecting and systematically selecting 20 articles from the Scopus database, this study provides insights into the driving factors influencing the adoption and implementation of these technologies to combat money laundering. The findings highlight the importance of advanced technology in improving compliance, security, and the speed of detection, ultimately contributing to the development of more effective anti-money laundering strategies.
A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration Husnaningtyas, Nadia; Hanin, Ghalizha Failazufah; Dewayanto, Totok; Malik, Muhammad Fahad
JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen Vol. 20 No. 1 (2023): JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31106/jema.v20i1.20602

Abstract

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.
Co-Authors Husnaningtyas, Nadia Malik, Muhammad Fahad Totok Dewayanto