Skip to main content
SpringerLink
Log in

A graph-based contrastive learning framework for medicare insurance fraud detection

  • Letter
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Li J, Lan Q L, Zhu E Y, Xu Y, Zhu D. A study of health insurance fraud in China and recommendations for fraud detection and prevention. Journal of Organizational and End User Computing, 2022, 34(4): 1–19

    Article  Google Scholar 

  2. Guo J, Liu G N, Zuo Y, Wu J J. Learning sequential behavior representations for fraud detection. In: Proceedings of 2018 IEEE International Conference on Data Mining. 2018, 127–136

  3. Shen Y K, Tan S, Sordoni A, Courville A. Ordered neurons: integrating tree structures into recurrent neural networks. In: Proceedings of the7th International Conference on Learning Representations. 2019

  4. Cao L H, Qin F L, Yan Z M. TLSTM-based medical insurance fraud detection. Computer Engineering and Applications, 2020, 56(21): 237–241

    Google Scholar 

  5. Li Q T, Xu Y Li Q T, Xu Y. VS-GRU: a variable sensitive gated recurrent neural network for multivariate time series with massive missing values. Applied Sciences, 2019, 9(15): 3041

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2018YFC0831500) and the National Natural Science Foundation of China (Grant No. 61972047).

Author information

Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Song Xiao, Ting Bai, Xiangchong Cui, Bin Wu, Xinkai Meng & Bai Wang

Authors
  1. Song Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangchong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinkai Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ting Bai.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, S., Bai, T., Cui, X. et al. A graph-based contrastive learning framework for medicare insurance fraud detection. Front. Comput. Sci. 17, 172341 (2023). https://doi.org/10.1007/s11704-022-1734-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-022-1734-0

Search

Navigation