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Statistical Medical Fraud Assessment: Exposition to an Emerging Field

Author

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  • Tahir Ekin
  • Francesca Ieva
  • Fabrizio Ruggeri
  • Refik Soyer

Abstract

Health care expenditures constitute a significant portion of governmental budgets. The percentage of fraud, waste and abuse within that spending has increased over years. This paper introduces the emerging area of statistical medical fraud assessment, which becomes crucial to handle the increasing size and complexity of the medical programmes. An overview of fraud types and detection is followed by the description of medical claims data. The utilisation of sampling, overpayment estimation and data mining methods in medical fraud assessment are presented. Recent unsupervised methods are illustrated with real world data. Finally, the paper introduces potential future research areas such as integrated decision making approaches and Bayesian methods and concludes with an overall discussion. The main goal of this exposition is to increase awareness about this important area among a broader audience of statisticians.

Suggested Citation

  • Tahir Ekin & Francesca Ieva & Fabrizio Ruggeri & Refik Soyer, 2018. "Statistical Medical Fraud Assessment: Exposition to an Emerging Field," International Statistical Review, International Statistical Institute, vol. 86(3), pages 379-402, December.
    • Handle: RePEc:bla:istatr:v:86:y:2018:i:3:p:379-402
    DOI: 10.1111/insr.12269
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    Cited by:

    1. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    2. Farbmacher, Helmut & Löw, Leander & Spindler, Martin, 2022. "An explainable attention network for fraud detection in claims management," Journal of Econometrics, Elsevier, vol. 228(2), pages 244-258.
    3. Chamal Gomes & Zhuo Jin & Hailiang Yang, 2021. "Insurance fraud detection with unsupervised deep learning," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 591-624, September.

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