A Fraud Resilient Medical Insurance Claim System

Authors

  • Yuliang Shi Shandong University
  • Chenfei Sun Shandong University
  • Qingzhong Li Shandong University
  • Lizhen Cui Shandong University
  • Han Yu Nanyang Technological University
  • Chunyan Miao Nanyang Technological University

DOI:

https://doi.org/10.1609/aaai.v30i1.9825

Keywords:

Medical Insurance Fraud Detection, Information Theory, Outlier Analysis, Decision Support

Abstract

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.

AAAI 2016 Proceedings Cover

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Published

2016-03-05

How to Cite

Shi, Y., Sun, C., Li, Q., Cui, L., Yu, H., & Miao, C. (2016). A Fraud Resilient Medical Insurance Claim System. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9825

Issue

Vol. 30 No. 1 (2016): Thirtieth AAAI Conference on Artificial Intelligence

Section

Demonstrations