(Translated by https://www.hiragana.jp/)
Development of an Expert System for Automatic Detection of Automobile Insurance Fraud
IDEAS home Printed from https://ideas.repec.org/p/fth/etcori/97-06.html
   My bibliography  Save this paper

Development of an Expert System for Automatic Detection of Automobile Insurance Fraud

Author

Listed:
  • Belhadji, B.
  • Dionne, G.

Abstract

The goal of this study is to develop a tool to aid insurance company adjusters in their decision making and to ensure that they are better equipped to fight fraud.

Suggested Citation

  • Belhadji, B. & Dionne, G., 1997. "Development of an Expert System for Automatic Detection of Automobile Insurance Fraud," Ecole des Hautes Etudes Commerciales de Montreal- 97-06, Ecole des Hautes Etudes Commerciales de Montreal-Chaire de gestion des risques..
    • Handle: RePEc:fth:etcori:97-06
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    Citations

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


    Cited by:

    1. 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.
    2. Yankol-Schalck, Meryem, 2022. "The value of cross-data set analysis for automobile insurance fraud detection," Research in International Business and Finance, Elsevier, vol. 63(C).
    3. Bermúdez, Ll. & Pérez, J.M. & Ayuso, M. & Gómez, E. & Vázquez, F.J., 2008. "A Bayesian dichotomous model with asymmetric link for fraud in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 779-786, April.
    4. Denisa BANULESCU-RADU & Meryem YANKOL-SCHALCK, 2021. "Fraud detection in the era of Machine Learning: a household insurance case," LEO Working Papers / DR LEO 2904, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    5. Donatella Porrini, 2002. "Frodi nell'assicurazione RC Auto: analisi economica e possibili rimedi," Rivista di Politica Economica, SIPI Spa, vol. 92(2), pages 109-138, March-Apr.

    More about this item

    Keywords

    INSURANCE ; AUTOMOBILES ; FRAUD;
    All these keywords.

    JEL classification:

    • K12 - Law and Economics - - Basic Areas of Law - - - Contract Law
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • R49 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Other

    Statistics

    Access and download statistics

    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:fth:etcori:97-06. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/hecmtca.html .

    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.