A survey on statistical methods for health care fraud detection.
Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions.
Li
Jing
J
Department of Industrial Engineering, Arizona State University, P.O. Box 875906, Tempe, AZ 85287-5906, USA. jinglz@asu.edu
Huang
Kuei-Ying
KY
Jin
Jionghua
J
Shi
Jianjun
J
D005607:Fraud / Q000706:statistics & numerical data*; D019981:Health Care Sector / Q000706:statistics & numerical data*; D006801:Humans; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D015233:Models, Statistical