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10.1080_08839514.2024.2344415.pdf (1.96 MB)

Identifying the AI-based solutions proposed for restricting Money Laundering in Financial Sectors: Systematic Mapping

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journal contribution
submitted on 2024-05-16, 06:45 and posted on 2024-05-16, 06:46 authored by Habib Ullah Khan, Muhammad Zain Malik, Shah Nazir

Money laundering (ML) is a critical source of extracting the money illegally from the financial system. It is linked to various types of crimes, including corruption, exploitation of a specific community, drug use, and many others. Detection of ML operations is a difficult task on a global scale due to the large volume of financial transactions. However, it also allows criminals to use financial systems to carry out fraudulent transactions. It mainly concern minimizing the potentially risks associated with money laundering. Anti-money laundering-(AML) tools based on AI-driven applications are now tracking transactions to overcome this challenge. A total of 112 research papers are assessed to identify the literature’s gaps and suggest new directions for the research area accordingly. The findings of this systemic literature review work will not only open new paths for the research community, but will also assist the state agencies in developing an optimal AML system to counter these major issues and provide a healthy environment for their residents. This article seeks to assess the existing situation from various angles and open up new pathways for future research directions to investigate and build high levels of authenticity and security in the financial industry using artificial intelligence (AI).

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Published in: Applied Artificial Intelligence
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Open Access funding provided by the Qatar National Library.



  • English


Taylor & Francis

Publication Year

  • 2024

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Business and Economics - QU

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