Leveraging artificial intelligence to mitigate money laundering risks through the detection of cyberbullying patterns in financial transactions
1 Department of Economics, Southeast University, Dhaka, Bangladesh.
2 Associate Professor, Department of Law and Land Administration, University of Rajshahi, Bangladesh.
3 A2Z Finance Australia (Easy Mortgage Solutions Australia), Australia.
4 Department of Marketing, Jagannath University, Dhaka, Bangladesh.
5 Dublin Business School, Dublin, Ireland.
Research Article
Global Journal of Engineering and Technology Advances, 2025, 22(01), 094-115.
Article DOI: 10.30574/gjeta.2025.22.1.0015
Publication history:
Received on 10 December 2024; revised on 21 January 2025; accepted on 24 January 2025
Abstract:
Money laundering (ML) is a vital source to clean the money from the financial system with illegal funds. Corruption, exploitation of a given community, drug use, and much more are all associated with it. Due to the massive number of transactions worldwide, detection of ML operations is complex. But it makes it possible for criminals to exploit financial systems to facilitate illicit transactions. This is primarily about reducing the risk that someone will be out of pocket because of money laundering. AI- driven applications of AML tools are now monitoring transactions to deal with it. In total, 112 research papers are reviewed (identified the gap in literature) which serves as a guide for the future direction of this research domain. The outcome of this systematic literature review effort will not only pave the way for the research community, also aid the state agencies to formulate an ideal AML ecosystem to tackle these prominent concerns while ensuring a healthy environment for their inhabitants. Those starting points can be taken to evaluate the current state of affairs from diverse perspectives and pave the way towards future research directions to explore and develop the high levels of authenticity and security that artificial intelligence (AI) can bring to the finance sector.
Keywords:
Money Laundering; AI- Driven; Financial Systems; Artificial Intelligence (AI)
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0