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

Song XIAO , Ting BAI , Xiangchong CUI , Bin WU , Xinkai MENG , Bai WANG

Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (2) : 172341

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Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (2) : 172341 DOI: 10.1007/s11704-022-1734-0
Artificial Intelligence
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A graph-based contrastive learning framework for medicare insurance fraud detection

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Song XIAO, Ting BAI, Xiangchong CUI, Bin WU, Xinkai MENG, Bai WANG. A graph-based contrastive learning framework for medicare insurance fraud detection. Front. Comput. Sci., 2023, 17(2): 172341 DOI:10.1007/s11704-022-1734-0

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References

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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

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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

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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

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

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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

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