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Combining expert knowledge and databases for risk management

Author

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  • Daniels, H.A.M.
  • van Dissel, H.G.

Abstract

Correctness, transparency and effectiveness are the principal attributes of knowledge derived from databases. In current data mining research there is a focus on efficiency improvement of algorithms for knowledge discovery. However important limitations of data mining can only be dissolved by the integration of knowledge of experts in the field, encoded in some accessible way, with knowledge derived form patterns in the database. In this paper we will in particular discuss methods for combining expert knowledge and knowledge derived from transaction databases.The framework proposed is applicable to wide variety of risk management problems. We will illustrate the method in a case study on fraud discovery in an insurance company.

Suggested Citation

  • Daniels, H.A.M. & van Dissel, H.G., 2003. "Combining expert knowledge and databases for risk management," ERIM Report Series Research in Management ERS-2003-002-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:266
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    More about this item

    Keywords

    datamining; knowledge based systems; knowledge discovery; risk management;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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