Combining expert knowledge and databases for risk management
2003-01-10
Research Paper
This publication is part of collection
| Related Files |
|---|
View PDF Version
(erimrs20030110180000.pdf, 0.2MB) |
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
Classifications using
Journal of Economic Literature (JEL) Classification System
- M11 : Production Management
- M : Business Administration and Business Economics; Marketing; Accounting
- R4 : Transportation Systems
- D83 : Search; Learning; Information and Knowledge
Automatically Extracted Terms
- knowledge
- management
- expert
- database
- claim
- system
- network
- risk indicators
- mining
- risk score
- model
- fraud
- expert knowledge
- information
- indicator
- data mining
- case study
- business
- decision
- process
