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

Riani, Marco, Corbellini, Aldo and Atkinson, Anthony C. (2018) The use of prior information in very robust regression for fraud detection. International Statistical Review, 86 (2). pp. 205-218. ISSN 0306-7734

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Identification Number: 10.1111/insr.12247

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.

Item Type: Article
Official URL: https://onlinelibrary.wiley.com/journal/17515823
Additional Information: © 2018 The Authors, International Statistical Institute
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
H Social Sciences > HV Social pathology. Social and public welfare. Criminology
Sets: Departments > Statistics
Date Deposited: 27 Apr 2018 15:04
Last Modified: 20 Feb 2019 06:46
Projects: IC1408
Funders: European Anti‐Fraud Office, CRoNoS
URI: http://eprints.lse.ac.uk/id/eprint/87685

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