Exploring detection and prevention of money laundering with information technology

Geo Finna Aprilia and Meiryani

Journal of Money Laundering Control, 2023, vol. 27, issue 6, 995-1004

Abstract: Purpose - Regarding the magnitude of the impact caused by money laundering, the size of the organization and the many parties involved, this paper aims to explore the methods used in detecting money laundering, especially the use of technology. Design/methodology/approach - This research is a literature review from various research sources originating from Pro-Quest, Emerald, Science Direct and Google Scholar. Findings - The researchers found that the most widely used methods for detecting money laundering were artificial intelligence, machine learning, data mining and social network analysis. Research limitations/implications - This research is expected to help the government or institutions such as the police, forensic accountants and investigative auditors in the fight against money laundering. This research is limited to only a few sources, and it is hoped that further research can explore more deeply related to other methods for detecting money laundering. Originality/value - This paper discusses the methods that are widely used in detecting money laundering.

Keywords: Money laundering; Technology; Artificial intelligence; Machine learning; Data mining; Social network analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jmlcpp:jmlc-08-2023-0138

DOI: 10.1108/JMLC-08-2023-0138

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