IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v11y2008i3p275-287.html
   My bibliography  Save this article

A survey on statistical methods for health care fraud detection

Author

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

Abstract

No abstract is available for this item.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10729-007-9045-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10729-007-9045-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
    2. Shapiro, Arnold F., 2004. "Fuzzy logic in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 399-424, October.
    3. Stephan Birle & Mohamed Ahmed Hussein & Thomas Becker, 2016. "Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-11, July.
    4. Md. Shafiul Alam & Tanzi Ahmed Chowdhury & Abhishak Dhar & Fahad Saleh Al-Ismail & M. S. H. Choudhury & Md Shafiullah & Md. Ismail Hossain & Md. Alamgir Hossain & Aasim Ullah & Syed Masiur Rahman, 2023. "Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments," Energies, MDPI, vol. 16(2), pages 1-31, January.
    5. Dalkilic, Turkan Erbay & Tank, Fatih & Kula, Kamile Sanli, 2009. "Neural networks approach for determining total claim amounts in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 236-241, October.
    6. Belles-Sampera, Jaume & Merigó, José M. & Guillén, Montserrat & Santolino, Miguel, 2013. "The connection between distortion risk measures and ordered weighted averaging operators," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 411-420.
    7. Sancho Salcedo-Sanz & Leo Carro-Calvo & Mercè Claramunt & Ana Castañer & Maite Mármol, 2014. "Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms," Risks, MDPI, vol. 2(2), pages 1-14, April.
    8. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    9. Nader Salari & Shamarina Shohaimi & Farid Najafi & Meenakshii Nallappan & Isthrinayagy Karishnarajah, 2014. "A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-50, November.
    10. Mehdi Neshat & Ali Akbar Pourahmad & Mohammad Reza Hasani, 2016. "Designing an Adaptive Neuro Fuzzy Inference System for Prediction of Customers Satisfaction," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-21, December.
    11. Sancho Salcedo-Sanz & L. Carro-Calvo & Mercè Claramunt & Anna Castañer & Maite Marmol, 2013. "An Analysis of Black-box Optimization Problems in Reinsurance: Evolutionary-based Approaches," Working Papers XREAP2013-04, Xarxa de Referència en Economia Aplicada (XREAP), revised May 2013.
    12. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    13. Daniel Doyle & Chris Groendyke, 2018. "Using Neural Networks to Price and Hedge Variable Annuity Guarantees," Risks, MDPI, vol. 7(1), pages 1-19, December.
    14. Marc Sanchez-Roger & María Dolores Oliver-Alfonso & Carlos Sanchís-Pedregosa, 2019. "Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises," Mathematics, MDPI, vol. 7(11), pages 1-22, November.
    15. Atsalakis, George S. & Atsalaki, Ioanna G. & Zopounidis, Constantin, 2018. "Forecasting the success of a new tourism service by a neuro-fuzzy technique," European Journal of Operational Research, Elsevier, vol. 268(2), pages 716-727.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:hcarem:v:11:y:2008:i:3:p:275-287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.