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<p><b>Abstract</b>—In this paper, we apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. [38] to the problem of diagnosing insurance claim fraud. The method effectively combines the advantages of boosting and the explanatory power of the weight of evidence scoring framework. We present the results of an experimental evaluation with an emphasis on discriminatory power, ranking ability, and calibration of probability estimates. The data to which we apply the method consists of closed personal injury protection (PIP) automobile insurance claims from accidents that occurred in Massachusetts during 1993 and were previously investigated for suspicion of fraud by domain experts. The data mimic the most commonly occurring data configuration—that is, claim records consisting of information pertaining to several binary fraud indicators. The findings of the study reveal the method to be a valuable contribution to the design of intelligible, accountable, and efficient fraud detection support.</p>
Data mining, pattern recognition, classifier design and evaluation, claim fraud detection, knowledge discovery, decision support.

G. Dedene, R. A. Derrig and S. Viaene, "A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis," in IEEE Transactions on Knowledge & Data Engineering, vol. 16, no. , pp. 612-620, 2004.
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