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

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  • Shenggang Yang
  • Lai Wei
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    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.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/17487871003700796
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Economic Policy Reform.

    Volume (Year): 13 (2010)
    Issue (Month): 2 ()
    Pages: 159-178

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    Handle: RePEc:taf:jpolrf:v:13:y:2010:i:2:p:159-178

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    Related research

    Keywords: money laundering; financial fraud; data mining techniques; outlier detection;

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