Application of Classification Methods to Individual Disability Income Insurance Fraud Detection

Authors:
Yi Peng;Gang Kou;Alan Sabatka;Jeff Matza;Zhengxin Chen;Deepak Khazanchi;Yong Shi
Affiliations:
Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA;Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA and Thomson Co., R&D, 610 Opperman Drive, Eagan, MN 55123, USA;Mutual of Omaha, Omaha, NE, USA;Mutual of Omaha, Omaha, NE, USA;Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA;Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA;Peter Kiewit Institute of Information Science, Technology & Engineering, University of Nebraska, Omaha, NE 68182, USA
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Abstract

As the number of electronic insurance claims increases each year, it is difficult to detect insurance fraud in a timely manner by manual methods alone. The objective of this study is to use classification modeling techniques to identify suspicious policies to assist manual inspections. The predictive models can label high-risk policies and help investigators to focus on suspicious records and accelerate the claim-handling process.The study uses health insurance data with some known suspicious and normal policies. These known policies are used to train the predictive models. Missing values and irrelevant variables are removed before building predictive models. Three predictive models: Naïve Bayes (NB), decision tree, and Multiple Criteria Linear Programming (MCLP), are trained using the claim data. Experimental study shows that NB outperformed decision tree and MCLP in terms of classification accuracy.