文章基本信息

标题：A Comparative Analysis of Decision Trees Vis-à-vis Other Computational Data Mining Techniques in Automotive Insurance Fraud Detection
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作者：Adrian Gepp ; J. Holton Wilson ; Kuldeep Kumar 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2012
卷号：10
期号：3
页码：537-561
出版社：Tingmao Publish Company
摘要：The development and application of computational data miningtechniques in nancial fraud detection and business failure prediction hasbecome a popular cross-disciplinary research area in recent times involvingnancial economists, forensic accountants and computational modellers.Some of the computational techniques popularly used in the context of -nancial fraud detection and business failure prediction can also be eectivelyapplied in the detection of fraudulent insurance claims and therefore, can beof immense practical value to the insurance industry. We provide a comparativeanalysis of prediction performance of a battery of data mining techniquesusing real-life automotive insurance fraud data. While the data we have usedin our paper is US-based, the computational techniques we have tested canbe adapted and generally applied to detect similar insurance frauds in othercountries as well where an organized automotive insurance industry exists.
关键词：ANNs; decision trees; fraud detection; logit model; survival;analysis.