Abnormal Group-Based Joint Medical Fraud Detection
Joint fraud is one of the most common fraud types existing in medical fraud. However, joint fraud detection is a difficult problem because fraudsters take only a very small part of the population and fraudsters do everything to bypass fraud detection constraints. Most existing fraud detection studie...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8579135/ |
| _version_ | 1831541735209041920 |
|---|---|
| author | Chenfei Sun Zhongmin Yan Qingzhong Li Yongqing Zheng Xudong Lu Lizhen Cui |
| author_facet | Chenfei Sun Zhongmin Yan Qingzhong Li Yongqing Zheng Xudong Lu Lizhen Cui |
| author_sort | Chenfei Sun |
| collection | DOAJ |
| description | Joint fraud is one of the most common fraud types existing in medical fraud. However, joint fraud detection is a difficult problem because fraudsters take only a very small part of the population and fraudsters do everything to bypass fraud detection constraints. Most existing fraud detection studies focus on finding normal behavior patterns and treat those who possess behaviors that violate behavior patterns as fraudsters. However, these methods generally have high false positives because normal people may also sometime behave contrary to normal behavior patterns. To address this issue, we propose an abnormal group-based joint fraud detection method named abnormal group-based joint fraud detection method. This method can distinguish suspicious fraudsters from normal persons who have unusual behaviors by abnormal group mining in person similarity adjacency graph so that the occurrence of false positives caused by non-fraudulent abnormal behavior can be reduced. The extensive experiments using medical insurance data show that our approach has improved the precision of joint fraud detection by more than 10% compared with the conventional methods. |
| first_indexed | 2024-12-17T00:22:43Z |
| format | Article |
| id | doaj.art-7270795b9296453fa2f4e75a7ae2b335 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:22:43Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7270795b9296453fa2f4e75a7ae2b3352022-12-21T22:10:32ZengIEEEIEEE Access2169-35362019-01-017135891359610.1109/ACCESS.2018.28871198579135Abnormal Group-Based Joint Medical Fraud DetectionChenfei Sun0https://orcid.org/0000-0002-6975-3137Zhongmin Yan1Qingzhong Li2Yongqing Zheng3Xudong Lu4Lizhen Cui5https://orcid.org/0000-0002-8262-8883Software and Data Engineering Center, Shandong University, Jinan, ChinaSoftware and Data Engineering Center, Shandong University, Jinan, ChinaSoftware and Data Engineering Center, Shandong University, Jinan, ChinaSoftware and Data Engineering Center, Shandong University, Jinan, ChinaSoftware and Data Engineering Center, Shandong University, Jinan, ChinaSoftware and Data Engineering Center, Shandong University, Jinan, ChinaJoint fraud is one of the most common fraud types existing in medical fraud. However, joint fraud detection is a difficult problem because fraudsters take only a very small part of the population and fraudsters do everything to bypass fraud detection constraints. Most existing fraud detection studies focus on finding normal behavior patterns and treat those who possess behaviors that violate behavior patterns as fraudsters. However, these methods generally have high false positives because normal people may also sometime behave contrary to normal behavior patterns. To address this issue, we propose an abnormal group-based joint fraud detection method named abnormal group-based joint fraud detection method. This method can distinguish suspicious fraudsters from normal persons who have unusual behaviors by abnormal group mining in person similarity adjacency graph so that the occurrence of false positives caused by non-fraudulent abnormal behavior can be reduced. The extensive experiments using medical insurance data show that our approach has improved the precision of joint fraud detection by more than 10% compared with the conventional methods.https://ieeexplore.ieee.org/document/8579135/Joint fraudabnormal groupfraud detection |
| spellingShingle | Chenfei Sun Zhongmin Yan Qingzhong Li Yongqing Zheng Xudong Lu Lizhen Cui Abnormal Group-Based Joint Medical Fraud Detection IEEE Access Joint fraud abnormal group fraud detection |
| title | Abnormal Group-Based Joint Medical Fraud Detection |
| title_full | Abnormal Group-Based Joint Medical Fraud Detection |
| title_fullStr | Abnormal Group-Based Joint Medical Fraud Detection |
| title_full_unstemmed | Abnormal Group-Based Joint Medical Fraud Detection |
| title_short | Abnormal Group-Based Joint Medical Fraud Detection |
| title_sort | abnormal group based joint medical fraud detection |
| topic | Joint fraud abnormal group fraud detection |
| url | https://ieeexplore.ieee.org/document/8579135/ |
| work_keys_str_mv | AT chenfeisun abnormalgroupbasedjointmedicalfrauddetection AT zhongminyan abnormalgroupbasedjointmedicalfrauddetection AT qingzhongli abnormalgroupbasedjointmedicalfrauddetection AT yongqingzheng abnormalgroupbasedjointmedicalfrauddetection AT xudonglu abnormalgroupbasedjointmedicalfrauddetection AT lizhencui abnormalgroupbasedjointmedicalfrauddetection |