title: Multi-stage methodology to detect health insurance claim fraud.
abstract: Healthcare costs in the US, as well as in other countries, increase rapidly due to demographic, economic, social, and legal changes. This increase in healthcare costs impacts both government and private health insurance systems. Fraudulent behaviors of healthcare providers and patients have become a serious burden to insurance systems by bringing unnecessary costs. Insurance companies thus develop methods to identify fraud. This paper proposes a new multistage methodology for insurance companies to detect fraud committed by providers and patients. The first three stages aim at detecting abnormalities among providers, services, and claim amounts. Stage four then integrates the information obtained in the previous three stages into an overall risk measure. Subsequently, a decision tree based method in stage five computes risk threshold values. The final decision stating whether the claim is fraudulent is made by comparing the risk value obtained in stage four with the risk threshold value from stage five. The research methodology performs well on real-world insurance data.
authors.affiliation: Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY, 13902-6000, USA. eyuzgec1@binghamton.edu.
authors.forename: Marina Evrim
authors.initials: ME
authors.lastname: Johnson
authors.affiliation: Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY, 13902-6000, USA.
authors.forename: Nagen
authors.initials: N
authors.lastname: Nagarur
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining; D005607:Fraud / Q000706:statistics & numerical data*; D006282:Health Personnel; D006801:Humans; D007343:Insurance Carriers / Q000706:statistics & numerical data*; D007348:Insurance, Health / Q000706:statistics & numerical data*; D015233:Models, Statistical; D010361:Patients; D014481:United States
keywords: Classification; Decision trees; Fraud; Insurance; Provider profiling; Risk