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Data mining in anti-money laundering field

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  • Noriaki Yasaka

Abstract

Purpose - This report aims to focus on how suspicious transaction report is created with data mining methods and used from the point of view of knowledge management. Design/methodology/approach - This paper considers data mining versus knowledge management in the anti-money laundering (AML) field. Findings - In the AML field, the information and knowledge gained are not necessarily used for or shared with the related shareholders. Creating and co-evolving the network of “knowledge professionals” is the impending assignment in this industry. The first and most important task is knowledge management in the global AML field. Originality/value - The report considers the creation with data mining methods and utilization from the point of view of knowledge management.

Suggested Citation

  • Noriaki Yasaka, 2017. "Data mining in anti-money laundering field," Journal of Money Laundering Control, Emerald Group Publishing Limited, vol. 20(3), pages 301-310, July.
    • Handle: RePEc:eme:jmlcpp:jmlc-09-2015-0041
    DOI: 10.1108/JMLC-09-2015-0041
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    Cited by:

    1. Corina-Narcisa (Bodescu) Cotoc & Maria Nițu & Mircea Constantin Șcheau & Adeline-Cristina Cozma, 2021. "Efficiency of Money Laundering Countermeasures: Case Studies from European Union Member States," Risks, MDPI, vol. 9(6), pages 1-19, June.

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