Application of classification methods to individual disability income insurance fraud detection

Yi Peng, Gang Kou, Alan Sabatka, Jeff Matza, Zhengxin Chen, Deepak Khazanchi, Yong Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings
PublisherSpringer Verlag
Pages852-858
Number of pages7
EditionPART 3
ISBN (Print)9783540725879
DOIs
StatePublished - 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: May 27 2007May 30 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume4489 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Computational Science, ICCS 2007
CountryChina
CityBeijing
Period5/27/075/30/07

Keywords

  • Classification
  • Decision tree
  • Insurance fraud detection
  • Multiple Criteria Linear Programming (MCLP)
  • Naïve Bayes (NB)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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