标题:Community Outlier Based Fraudster Detection
作者:Sun, Chenfei; Li, Qingzhong; Li, Hui; Zhang, Shidong; Zheng, Yongqing
通讯作者:Li, Qingzhong
作者机构:[Sun, Chenfei; Li, Qingzhong; Li, Hui; Zhang, Shidong; Zheng, Yongqing] Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.
会议名称:10th International Conference on Knowledge Science, Engineering and Management (KSEM)
会议日期:AUG 19-20, 2017
来源:KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT (KSEM 2017): 10TH INTERNATIONAL CONFERENCE, KSEM 2017, MELBOURNE, VIC, AUSTRALIA, AUGUST 19-20, 2017, PROCEEDINGS
出版年:2017
卷:10412
页码:410-421
DOI:10.1007/978-3-319-63558-3_35
摘要:Healthcare fraud is causing billions of dollars in loss for public healthcare funds. In existing healthcare fraud cases, the convicted fraudsters are mostly physicians - the healthcare professionals who submit fraudulent bills. Fraudster detection can help us to find suspicious physicians and to combat healthcare fraud in advance. When it comes to the problem of fraudster detection, rule based fraud detection methods are not applicable because fraudsters will try everything to avoid detection rules. Meanwhile, outlier based fraud detection approaches primarily aim to find global outliers and can't find local outliers accurately. Therefore, we propose Community Outlier Based Fraudster Detection Approach - COBFDA in this paper. The proposed approach divides the physicians into different communities and looks for community outliers in each community. Extensive experiment results show that COBFDA outperforms the comparison approaches in terms of f-measure by over 20%.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028451517&doi=10.1007%2f978-3-319-63558-3_35&partnerID=40&md5=831ef15bc2a02f303a03dbe9dd534c02
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