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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2012))

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Abstract

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No.62162046, the Inner Mongolia Science and Technology Project under Grant No.2021GG0155, the Natural Science Foundation of Major Research Plan of Inner Mongolia under Grant No.2019ZD15, and the Inner Mongolia Natural Science Foundation under Grant No. 2019GG372.

Author information

Authors and Affiliations

  1. College of Computer Science, Inner Mongolia University, Hohhot, China

    Rixin Dong, Hanlin Liu, Xu Guo & Jiantao Zhou

  2. National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia, Hohhot, China

    Rixin Dong, Xu Guo & Jiantao Zhou

  3. Engineering Research Center of Ecological Big Data, Ministry of Education, Hohhot, China

    Rixin Dong, Xu Guo & Jiantao Zhou

  4. Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software, Hohhot, China

    Rixin Dong, Xu Guo & Jiantao Zhou

  5. Inner Mongolia Key Laboratory of Social Computing and Data Processing, Hohhot, China

    Rixin Dong, Xu Guo & Jiantao Zhou

  6. Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data, Hohhot, China

    Rixin Dong, Xu Guo & Jiantao Zhou

  7. Inner Mongolia Engineering Laboratory for Big Data Analysis Technology, Hohhot, China

    Rixin Dong, Xu Guo & Jiantao Zhou

Authors
  1. Rixin Dong
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  2. Hanlin Liu
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  3. Xu Guo
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  4. Jiantao Zhou
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Corresponding author

Correspondence to Jiantao Zhou .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Harbin Engineering University, Harbin, China

    Tong Wang

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. Tongji University, Shanghai, China

    Bowen Du

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dong, R., Liu, H., Guo, X., Zhou, J. (2024). Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_35

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  • DOI: https://doi.org/10.1007/978-981-99-9637-7_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9636-0

  • Online ISBN: 978-981-99-9637-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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