标题:An Effective Hybrid Fraud Detection Method
作者:Sun, Chenfei; Li, Qingzhong; Cui, Lizhen; Yan, Zhongmin; Li, Hui; Wei, Wei
通讯作者:Li, Qingzhong
作者机构:[Sun, Chenfei; Li, Qingzhong; Cui, Lizhen; Yan, Zhongmin; Li, Hui] Shandong Univ, Natl Engn Lab Ecommerce, Sch Comp Sci & Technol, Jinan 250100, Peopl 更多
会议名称:8th International Conference on Knowledge Science, Engineering and Management (KSEM)
会议日期:OCT 28-30, 2015
来源:KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2015
出版年:2015
卷:9403
页码:563-574
DOI:10.1007/978-3-319-25159-2_51
关键词:Fraud detection; Dempster-Shafer evidence Theory
摘要:The rapid growth of data makes it possible for us to study human behavior patterns. Knowing the patterns of human behavior is of great use to help us detect the unusual fraud human behavior. Existing fraud detection methods can be divided into two categories: pattern based and outlier detection based methods. However, because of the sparsity and complex granularity of big data, these methods have high false positive in fraud detection. In this paper, we propose an effective hybrid fraud detection method. We propose SSIsomap which improves isomap to cluster behaviors into behavior classes and propose SimLOF which improves LOF to conduct outlier detection, then we use Dempster-Shafer evidence Theory for combining behavior pattern evidence and outlier evidence, which yields a degree of belief of fraud to the new coming claim. The experiment result shows our method has significantly higher accuracy than exsiting methods in medical insurance fraud detection.
收录类别:CPCI-S;EI;SCOPUS
Scopus被引频次:1
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-s2.0-84951842000&doi=10.1007%2f978-3-319-25159-2_51&partnerID=40&md5=80e81b2c33348cec605b9b1ade3e10db
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