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Detecting money laundering using filtering techniques: a multiple‐criteria index

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  • Shenggang Yang
  • Lai Wei

Abstract

Money laundering is a dynamic activity attempting to circumvent anti‐money laundering (AML) actions. We propose a money‐laundering detection approach encompassing three separate detection measures applied simultaneously, providing a consolidated index to minimize circumvention. The index incorporates three detection measures: (1) deviations in trading volume and frequency; (2) unusual payments to or receipts from an atypical trade partner; and (3) Benford’s Law, based on the number of times a specific digit occurs in a particular position in numbers to detect financial fraud. Finally, we design a numerical test that any reasonable detection approach should satisfy. Our results successfully discover possible fraud planted in the simulated data.

Suggested Citation

  • Shenggang Yang & Lai Wei, 2010. "Detecting money laundering using filtering techniques: a multiple‐criteria index," Journal of Economic Policy Reform, Taylor and Francis Journals, vol. 13(2), pages 159-178.
    • Handle: RePEc:taf:jecprf:v:13:y:2010:i:2:p:159-178
    DOI: 10.1080/17487871003700796
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    Cited by:

    1. Lucas Schmidt Goecks & André Luis Korzenowski & Platão Gonçalves Terra Neto & Davenilcio Luiz de Souza & Taciana Mareth, 2022. "Anti‐money laundering and financial fraud detection: A systematic literature review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 71-85, April.

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