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Artificial intelligence for anti-money laundering: a review and extension

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Abstract

This paper surveys the existing academic literature on artificial intelligence (AI) technologies for anti-money laundering (AML). We review the state-of-the-art AI methods for AML and extend the discussion by proposing a framework that utilizes advanced natural language processing and deep-learning techniques to facilitate next-generation AML technologies. Our framework utilizes unstructured external information to assist domain experts, aiming to decrease the workload for the human investigator. We bridge the gap between the current AML methods and state-of-the art AI, highlighting new trends and directions in AI that can be used to develop the AML pipeline into a robust, scalable solution with a reduced false positive rate and high adaptability.

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Notes

  1. https://www3.nd.edu/~mcdonald/Word_Lists.html.

  2. We use https://www.ibm.com/watson/alchemy-api.html and it achieves 75.56% in terms of accuracy.

  3. https://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.README.1.0.txt.

  4. Prediction of five-point scale polarity of a tweet.

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Acknowledgements

This work was partially supported by Enterprise Ireland (grant number IP20170626). We would like to thank Accenture Applied Intelligence, Fraud and Risk Analytics and Technology Labs teams for inspiring conversations and support.

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Authors and Affiliations

  1. Vanke Service Research, Vanke Buidling, Meilin Road, Futian District, Shenzhen, China

    Jingguang Han

  2. Michael Smurfit Graduate Business School, University College Dublin, Dublin, Ireland

    Yuyun Huang & Sha Liu

  3. KPMG, 6 Ballynakelly View, Newcastle, Co. Dublin, Ireland

    Kieran Towey

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  1. Jingguang Han
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Han, J., Huang, Y., Liu, S. et al. Artificial intelligence for anti-money laundering: a review and extension. Digit Finance 2, 211–239 (2020). https://doi.org/10.1007/s42521-020-00023-1

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