Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Robust Classification for Imprecise Environments
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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In this paper we perform an exploratory study on the design of claim fraud detection for a typical property and casualty (P&C) insurance company using cost-sensitive classification. We contrast several cost incorporation scenarios based on different assumptions concerning the available cost information at claim screening time. Our empirical trials are based on a data set of real-life Spanish closed automobile insurance claims that were previously investigated for suspicion of fraud by domain experts and for which we obtained detailed cost information. The reported results show the added value of cost-sensitive claim fraud screening and provide guidance on how to operationalize this strategy.