Boosting Naive Bayes for Claim Fraud Diagnosis
Lecture notes in computer sciencePublished 1 January 2002
Stijn Viaene, Richard A. Derrig, Guido Dedene
Citations5
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TL;DR
The weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. (1998) is applied for the diagnosis of insurance claim fraud and is revealed to be a valuable contribution to the design of effective, intelligible, accountable and efficient fraud detection support.
Abstract
In this paper we apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. (1998) for the diagnosis of insurance claim fraud. The method effiectively combines the advantages of boosting and the modelling power and representational attractiveness of the probabilistic weight of evidence scoring framework. We present the results of an experimental comparison with an emphasis on both discriminatory power and calibration of probability estimates. The data on which we evaluate the method consists of a representative set of closed personal injury protection automobile insurance claims from accidents that occurred in Massachusetts during 1993. The findings of the study reveal the method to be a valuable contribution to the design of effective, intelligible, accountable and efficient fraud detection support.
Keywords
Computer Science
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