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

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  • Riani, Marco
  • Corbellini, Aldo
  • Atkinson, Anthony C.

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.

Suggested Citation

  • Riani, Marco & Corbellini, Aldo & Atkinson, Anthony C., 2018. "The use of prior information in very robust regression for fraud detection," LSE Research Online Documents on Economics 87685, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:87685
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    File URL: http://eprints.lse.ac.uk/87685/
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    1. Marco Riani & Aldo Corbellini & Anthony C. Atkinson, 2018. "The Use of Prior Information in Very Robust Regression for Fraud Detection," International Statistical Review, International Statistical Institute, vol. 86(2), pages 205-218, August.
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    Cited by:

    1. Marco Riani & Aldo Corbellini & Anthony C. Atkinson, 2018. "The Use of Prior Information in Very Robust Regression for Fraud Detection," International Statistical Review, International Statistical Institute, vol. 86(2), pages 205-218, August.
    2. Francesca Torti & Marco Riani & Gianluca Morelli, 2021. "Semiautomatic robust regression clustering of international trade data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 863-894, September.
    3. Vilijandas Bagdonavičius & Linas Petkevičius, 2020. "A new multiple outliers identification method in linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 275-296, April.
    4. Lucio Barabesi & Andrea Cerioli & Domenico Perrotta, 2021. "Forum on Benford’s law and statistical methods for the detection of frauds," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 767-778, September.

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    1. Vilijandas Bagdonavičius & Linas Petkevičius, 2020. "A new multiple outliers identification method in linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 275-296, April.
    2. Lucio Barabesi & Andrea Cerioli & Domenico Perrotta, 2021. "Forum on Benford’s law and statistical methods for the detection of frauds," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 767-778, September.
    3. Francesca Torti & Marco Riani & Gianluca Morelli, 2021. "Semiautomatic robust regression clustering of international trade data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 863-894, September.

    More about this item

    Keywords

    big data; data cleaning; forward search; MM estimation; misinvoicing; money laundering; seafood; timeliness;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • F3 - International Economics - - International Finance
    • G3 - Financial Economics - - Corporate Finance and Governance

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