Combining expert knowledge and databases for risk management
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.
|Keywords||datamining, knowledge based systems, knowledge discovery, risk management|
|Publisher||Erasmus Research Institute of Management (ERIM)|
Daniels, H.A.M., & van Dissel, H.G.. (2003). Combining expert knowledge and databases for risk management (No. ERS-2003-002-LIS). Erasmus Research Institute of Management (ERIM). Retrieved from http://hdl.handle.net/1765/266