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A survey on statistical methods for health care fraud detection

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  • Jing Li
  • Kuei-Ying Huang
  • Jionghua Jin
  • Jianjun Shi

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  • Jing Li & Kuei-Ying Huang & Jionghua Jin & Jianjun Shi, 2008. "A survey on statistical methods for health care fraud detection," Health Care Management Science, Springer, vol. 11(3), pages 275-287, September.
    • Handle: RePEc:kap:hcarem:v:11:y:2008:i:3:p:275-287
    DOI: 10.1007/s10729-007-9045-4
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    References listed on IDEAS

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    1. Shapiro, Arnold F., 2002. "The merging of neural networks, fuzzy logic, and genetic algorithms," Insurance: Mathematics and Economics, Elsevier, vol. 31(1), pages 115-131, August.
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    Citations

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    Cited by:

    1. Dmitriy Vorobyev, 2011. "Towards Detecting and Measuring Ballot Stuffing," CERGE-EI Working Papers wp447, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    2. Howard, David H. & McCarthy, Ian, 2021. "Deterrence effects of antifraud and abuse enforcement in health care," Journal of Health Economics, Elsevier, vol. 75(C).
    3. van Capelleveen, Guido & Poel, Mannes & Mueller, Roland M. & Thornton, Dallas & van Hillegersberg, Jos, 2016. "Outlier detection in healthcare fraud: A case study in the Medicaid dental domain," International Journal of Accounting Information Systems, Elsevier, vol. 21(C), pages 18-31.
    4. 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.
    5. Edward C. Malthouse & Wei-Lin Wang & Bobby J. Calder & Tom Collinger, 2019. "Process control for monitoring customer engagement," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(2), pages 54-63, June.
    6. Vijay Iyengar & Keith Hermiz & Ramesh Natarajan, 2014. "Computer-aided auditing of prescription drug claims," Health Care Management Science, Springer, vol. 17(3), pages 203-214, September.
    7. Bayerstadler, Andreas & van Dijk, Linda & Winter, Fabian, 2016. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 244-252.
    8. 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.
    9. Philippe Bernard & Najat El Mekkaoui De Freitas & Bertrand B. Maillet, 2022. "A financial fraud detection indicator for investors: an IDeA," Annals of Operations Research, Springer, vol. 313(2), pages 809-832, June.
    10. Zhang, Liangwei & Lin, Jing & Karim, Ramin, 2015. "An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 482-497.
    11. Rajeev K. Goel, 2020. "Medical professionals and health care fraud: Do they aid or check abuse?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 520-528, June.
    12. Tahir Ekin & Francesca Ieva & Fabrizio Ruggeri & Refik Soyer, 2017. "On the Use of the Concentration Function in Medical Fraud Assessment," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 236-241, July.
    13. Arash Rashidian & Hossein Joudaki & Taryn Vian, 2012. "No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.

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