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

Jingguang Han (), Yuyun Huang (), Sha Liu () and Kieran Towey ()
Additional contact information
Jingguang Han: Vanke Service Research
Yuyun Huang: University College Dublin
Sha Liu: University College Dublin
Kieran Towey: KPMG

Digital Finance, 2020, vol. 2, issue 3, No 3, 239 pages

Abstract: 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.

Keywords: Anti-money-laundering; Artificial intelligence; Natural language processing; Deep learning; G21; G23; C44; C45 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s42521-020-00023-1

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