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Knowledge-Guided Fraud Detection Using Semi-supervised Graph Neural Network

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Abstract

Fraud detection is about finding unusual behaviors in the data, and it is essential for companies to detect fraudulent users to prevent unpredictable risks. The graph-based approaches model relationships into graphs for capturing the intricate characteristics of complex scenarios to detect fraudsters. However, it still faces the problem of data skew where labeled fraudsters are far fewer than unlabeled examples. Knowledge may help identify these unlabeled data, thus this paper combines domain knowledge with GNN and proposes aKnowledge-GuidedSemi-supervisedGraphNeuralNetwork, namelyKS-GNN, to address the problem of data skew. We utilize domain experts to design small amount of rules to roughly label unlabeled data as noisy and use a semi supervised method to train fraud detectors. By utilizing only 13 GFD rules conducted by domain experts, the performance of our method yields about 15% improvement over the state-of-the-art fraud detection methods CARE-GNN on banking transaction funds supervision datasets (BTFSD). Moreover, with some modification of the GFD rules on BTFSD, the performance of KS-GNN on other domain datasets such as IEEE-CIS Fraud Detection (https://www.kaggle.com/c/ieee-fraud-detection/data) and Yelp-Chi is also improved by about 5% on average compared with the state-of-the-art methods.

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Author information

Authors and Affiliations

  1. Military Science Information Research Center, Academy of Military Sciences, Beijing, China

    Yizhuo Rao, Chengyuan Duan, Jiajun Cheng, Yu Chen, Hongliang You, Qiang Gao & Xiao Wei

  2. Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing, China

    Xiaoguang Ren & Xianya Mi

  3. The Sixty-third Research Institute, National University of Defense Technology, Nanjing, China

    Zhixian Zeng

Authors
  1. Yizhuo Rao

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  2. Xiaoguang Ren

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  3. Chengyuan Duan

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  4. Xianya Mi

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  5. Jiajun Cheng

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  6. Yu Chen

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  7. Hongliang You

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  8. Qiang Gao

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  9. Zhixian Zeng

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  10. Xiao Wei

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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Wenjie Zhang

  2. Peking University, Beijing, China

    Lei Zou

  3. Zayed University, Dubai, United Arab Emirates

    Zakaria Maamar

  4. Swinburne University of Technology, Melbourne, VIC, Australia

    Lu Chen

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Rao, Y.et al. (2021). Knowledge-Guided Fraud Detection Using Semi-supervised Graph Neural Network. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13080. Springer, Cham. https://doi.org/10.1007/978-3-030-90888-1_29

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JPY 11439
Price includes VAT (Japan)
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JPY 14299
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