The use of prior information in very robust regression for fraud detection

Marco Riani, Aldo Corbellini and Anthony C. Atkinson

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Misinvoicing is a major tool in fraud including money laundering. We develop a method of detecting the patterns of outliers that indicate systematic mis‐pricing. As the data only become available year by year, we develop a combination of very robust regression and the use of ‘cleaned’ prior information from earlier years, which leads to early and sharp indication of potentially fraudulent activity that can be passed to legal agencies to institute prosecution. As an example, we use yearly imports of a specific seafood into the European Union. This is only one of over one million annual data sets, each of which can currently potentially contain 336 observations. We provide a solution to the resulting big data problem, which requires analysis with the minimum of human intervention.

Keywords: big data; data cleaning; forward search; MM estimation; misinvoicing; money laundering; seafood; timeliness (search for similar items in EconPapers)
JEL-codes: C1 F3 G3 (search for similar items in EconPapers)
Date: 2018-08-01
New Economics Papers: this item is included in nep-big and nep-ecm
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Citations: View citations in EconPapers (3)

Published in International Statistical Review, 1, August, 2018, 86(2), pp. 205-218. ISSN: 0306-7734

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http://eprints.lse.ac.uk/87685/ Open access version. (application/pdf)

Related works:
Journal Article: The Use of Prior Information in Very Robust Regression for Fraud Detection (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:87685

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