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Fraud detection by a multinomial model: Separating honesty from unobserved fraud

Author

Listed:
  • Andersson, Jonas

    (Dept. of Business and Management Science, Norwegian School of Economics)

  • Olden, Andreas

    (Dept. of Business and Management Science, Norwegian School of Economics)

  • Rusina, Aija

    (Dept. of Business and Management Science, Norwegian School of Economics)

Abstract

In this paper we investigate the EM-estimator of the model by Caudill et al. (2005). The purpose of the model is to identify items, e.g. individuals or companies, that are wrongly classified as honest; an example of this is the detection of tax evasion. Normally, we observe two groups of items, labeled fraudulent and honest, but suspect that many of the observationally honest items are, in fact, fraudulent. The items observed as honest are therefore divided into two unobserved groups, honestH, representing the truly honest, and honestF, representing the items that are observed as honest, but that are actually fraudulent. By using a multinomial logit model and assuming commonality between the observed fraudulent and the unobserved honestF, Caudill et al. (2005) present a method that uses the EM-algorithm to separate them. By means of a Monte Carlo study, we investigate how well the method performs, and under what circumstances. We also study how well bootstrapped standard errors estimates the standard deviation of the parameter estimators.

Suggested Citation

  • Andersson, Jonas & Olden, Andreas & Rusina, Aija, 2020. "Fraud detection by a multinomial model: Separating honesty from unobserved fraud," Discussion Papers 2020/15, Norwegian School of Economics, Department of Business and Management Science.
    • Handle: RePEc:hhs:nhhfms:2020_015
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    File URL: https://hdl.handle.net/11250/2721233
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    References listed on IDEAS

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    1. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    2. Alan D. Olinsky & Paul M. Mangiameli & Shaw K. Chen, 1996. "Statistical Support of Forensic Auditing," Interfaces, INFORMS, vol. 26(6), pages 95-104, December.
    3. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    4. Jing Ai & Patrick Brockett & Linda Golden, 2009. "Assessing Consumer Fraud Risk in Insurance Claims," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(4), pages 438-458.
    5. Steven B. Caudill & Mercedes Ayuso & Montserrat Guillén, 2005. "Fraud Detection Using a Multinomial Logit Model With Missing Information," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 539-550, December.
    6. Artis, Manuel & Ayuso, Mercedes & Guillen, Montserrat, 1999. "Modelling different types of automobile insurance fraud behaviour in the Spanish market," Insurance: Mathematics and Economics, Elsevier, vol. 24(1-2), pages 67-81, March.
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    More about this item

    Keywords

    Fraud detection; EM-algorithm; multinomial logit model; Monte Carlo study;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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