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A Hybrid Approach for Detecting Fraudulent Medical Insurance Claims


Authors: C. Sun, Y. Shi, Q. Li, L. Cui, H. Yu, and C. Miao
Title: A Hybrid Approach for Detecting Fraudulent Medical Insurance Claims
Abstract: Medical insurance frauds are causing huge losses for public healthcare funds in many countries. Detecting medical insurance frauds is an important and difficult challenge. Because of the complex granularity of data, existing fraud detection approaches tend to be less effective in terms of recalling fraudulent claim behaviours. In this paper, we propose a Hybrid Fraud Detection Approach (HFDA) to address this problem, which is compared with four state-of-the-art approaches using a real-world dataset. Extensive experiment results show that the proposed approach is significantly more effective and efficient.
Keywords: Behaviour patterns; Outliers; Evidence theory; Fraud
Conference Name: 15th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS'16)
Location: Singapore, Singapore
Publisher: IFAAMAS
Year: 2016
Accepted PDF File: A_Hybrid_Approach_for_Detecting_Fraudulent_Medical_Insurance_Claims_accepted.pdf
Permanent Link: http://www.ifaamas.org/Proceedings/aamas2016/pdfs/p1287.pdf
Reference: C. Sun, Y. Shi, Q. Li, L. Cui, H. Yu, and C. Miao, “A hybrid approach for detecting fraudulent medical insurance claims,” in Proceedings of the 15th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS’16). IFAAMAS, May 2016, pp. 1287–1288.
bibtex: 
@inproceedings{LILY-c80, 
    author	= {Sun, Chenfei and Shi, Yuliang and Li, Qingzhong and Cui, Lizhen and Yu, Han and Miao, Chunyan},
    title	= {A Hybrid Approach for Detecting Fraudulent Medical Insurance Claims},  
    booktitle	= {Proceedings of the 15th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS'16)}, 
    year		= {2016}, 
    month	= {May}, 
    pages	= {1287-1288}, 
    location	= {Singapore, Singapore},
    publisher	= {IFAAMAS},
 }