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Improving graph-based label propagation algorithm with group partition for fraud detection

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Abstract

Fraudulent user detection is a crucial issue in financial risk management. Due to the lack of labeled data and the reliability of labeling, label propagation algorithms (LPA) are effective solutions in this scenario. Most existing models only propagate the risk probabilities for individual users through feature level, while ignoring the real-world graph structure and the characteristics of gang crime. This paper improves the graph-based LPA through group partition, which can be directly implemented on the graph at hand with full consideration of the group information. The exhaustive experimental results testify the performance of our proposed model KGLPA over other off-the-shelf models and amend the insufficiency of feature-based LPA with higher reliability and stability to improve the detection of fraudulent users and secure the marketing budgets.

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Acknowledgements

This research is financially supported by The National Key Research and Development Program of China (grant number 2018YFC0807105), National Natural Science Foundation of China (grant number 61462073) and Science and Technology Committee of Shanghai Municipality (STCSM) (under grant numbers 17DZ1101003, 18511106602 and 18DZ2252300).

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Authors and Affiliations

  1. East China University of Science and Technology, Shanghai, China

    Jiahui Wang, Yi Guo, Xinxiu Wen & Zhihong Wang

  2. National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China

    Yi Guo

  3. Shanghai Engineering Research Center of Big Data & Internet Audience, Shanghai, China

    Yi Guo

  4. China Telecom BestPay Co., Ltd, Shanghai, China

    Zhen Li & Minwei Tang

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  1. Jiahui Wang
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  2. Yi Guo
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  3. Xinxiu Wen
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  4. Zhihong Wang
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  5. Zhen Li
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  6. Minwei Tang
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Correspondence to Yi Guo.

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Wang, J., Guo, Y., Wen, X. et al. Improving graph-based label propagation algorithm with group partition for fraud detection. Appl Intell 50, 3291–3300 (2020). https://doi.org/10.1007/s10489-020-01724-1

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  • DOI: https://doi.org/10.1007/s10489-020-01724-1

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