Healthcare fraud detection: A survey and a clustering model incorporating Geo-location information

Health care has become a major expenditure in the US since 1980. Both the size of the health care sector and the enormous volume of money involved make it an attractive fraud target. Therefore, effective fraud detection is important for reducing the cost of health care services. In order to achieve more effective fraud detection, many researchers have attempted to develop sophisticated antifraud approaches incorporating data mining, machine learning or other methods. This introduce some preliminary knowledge of U.S. health care system and its fraudulent behaviors, analyzes the characteristics of health care data, and reviews and compares currently proposed fraud detection approaches using health care data in the literature as well as their corresponding data preprocess methods. Also a novel health care fraud detection method including geo-location information is proposed.