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Preventing and Detecting Fraud through Data Analytics in auto insurance field

Author

Listed:
  • Adrian BANARESCU

    (Postdoctoral researcher at the Institute of National Economy, Romanian Academy)

  • Aurel-Mihail BALOI

    (Intergraph Computer Services, Romania)

Abstract

Currently, in the autoinsurance field, fraud has become a real phenomenon, and insurance companies are making efforts to identify and implement effective means of fraud prevention and detection. This article aims to present the data analysis and related tools, as a possible alternative to fraud, in the context of the current information society (I-society).In the literature, data analysis processes are presented as a highly effective means for the prevention and detection of fraud, but their implementation at both inter and intra-company levels is a difficult process, because it involves organizational transformations in which the decision should be based on knowledge.Although in Romania the institutional framework for fraud detection in the auto insurance field is created, the information processes that take advantage of the potential of intangible assets (data, information, knowledge, relationships, etc.) are not systematically exploited and are not integrated into decision-making (whether strategic or operational).

Suggested Citation

  • Adrian BANARESCU & Aurel-Mihail BALOI, 2015. "Preventing and Detecting Fraud through Data Analytics in auto insurance field," Romanian Journal of Economics, Institute of National Economy, vol. 40(1(49)), pages 89-114, june.
    • Handle: RePEc:ine:journl:v:40:y:2015:i:49:p:89-114
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    References listed on IDEAS

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    1. Choi, Eun Kwan & Chen, Jiong, 1997. "Employment risk and optimal trade policies," Discussion Papers, Series II 338, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    2. Financial and Payment System Department, 1997. "Risk Disclosure by Financial Institutions," Bank of Japan Research Papers 1997-02, Bank of Japan.
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    Cited by:

    1. 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).

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    More about this item

    Keywords

    fraud; IT Systems; data analytics; network analysis; geospatial analysis;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • O39 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Other

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