Post by Sapphire Capital on Aug 7, 2008 23:08:34 GMT 4
Combining Expert Knowledge and Databases for Risk Management
Hennie Daniels
Erasmus Research Institute of Management (ERIM) - Joint Research Institute of Rotterdam School of Management (RSM) and Erasmus School of Economics(ESE), EUR; Tilburg University, CentER for Economic Research
H.G. Van Dissel
Erasmus Research Institute of Management (ERIM) - Joint Research Institute of Rotterdam School of Management (RSM) and Erasmus School of Economics(ESE), EUR; Erasmus University Rotterdam (EUR) - Department of Decision and Information Sciences
October 2003, 01
ERIM Report Series Reference No. ERS-2003-002-LIS
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.
papers.ssrn.com/sol3/Delivery.cfm/266.pdf?abstractid=1098508&mirid=4
Hennie Daniels
Erasmus Research Institute of Management (ERIM) - Joint Research Institute of Rotterdam School of Management (RSM) and Erasmus School of Economics(ESE), EUR; Tilburg University, CentER for Economic Research
H.G. Van Dissel
Erasmus Research Institute of Management (ERIM) - Joint Research Institute of Rotterdam School of Management (RSM) and Erasmus School of Economics(ESE), EUR; Erasmus University Rotterdam (EUR) - Department of Decision and Information Sciences
October 2003, 01
ERIM Report Series Reference No. ERS-2003-002-LIS
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.
papers.ssrn.com/sol3/Delivery.cfm/266.pdf?abstractid=1098508&mirid=4