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A graph-powered large-scale fraud detection system

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Abstract

Graph-powered fraud detection is a common issue in various areas, such as e-commerce, banking, insurance and social networks, where data can be naturally formulated as graph structure. Especially in e-commerce, due to its large scale and enormous amount of real-time transactions over millions of merchandises, fraud detection has become an important and serious problem. The challenges lie in three aspects: sparse fraud samples, complex features in online transactions and extra-large scale of e-commerce data. To deal with above issues, in this paper, we propose an efficient graph-powered large-scale fraud detection framework. Concretely, we first present a heterogeneous label propagation algorithm to recall more potentially fraudulent samples for further model training; then, we design a novel multi-view heterogeneous graph neural network model to obtain more accurate fraud predictions; finally, a fraud pattern analysis approach is presented to discover hidden fraud groups. In addition, in order to improve the efficiency and scalability of our proposed fraud detection framework, we present a large-scale fraud detection system deployed on a general graph computing engine. We conduct experiments on two real-world datasets. Results show that the proposed graph-powered fraud detection framework achieves high accuracy and superior scalability on large-scale graph data.

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Notes

  1. https://www.taobao.com.

  2. Data Availability Statements: The Mooc data that supports the findings of this study is available from https://snap.stanford.edu/jodie/#datasets. The Taobao data that supports the findings of this study is not openly available due to commercial regularities, but may be partially open upon reasonable request and under the permission of Alibaba in the future.

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Acknowledgements

This paper is supported by the China Postdoctoral Science Foundation (2021M692957), and the National Natural Science Foundation of China (62172372).

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Authors and Affiliations

  1. Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, 310058, Zhejiang, China

    Zhao Li

  2. Zhejiang Lab, Hangzhou, 311121, Zhejiang, China

    Zhao Li, Biao Wang, Yilun Jin & Zenghui Xu

  3. Link2do Technology, Hangzhou, 311199, Zhejiang, China

    Jiaming Huang

  4. The University of Southern Queensland, Toowoomba, QLD, 4350, Australia

    Ji Zhang

  5. Central South University, Changsha, 410083, Hunan, China

    Jianliang Gao

  6. Southeast University, Nanjing, 214135, Jiangsu, China

    Yilun Jin

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  1. Zhao Li
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  2. Biao Wang
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Correspondence to Biao Wang.

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Li, Z., Wang, B., Huang, J. et al. A graph-powered large-scale fraud detection system. Int. J. Mach. Learn. & Cyber. 15, 115–128 (2024). https://doi.org/10.1007/s13042-023-01786-w

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  • DOI: https://doi.org/10.1007/s13042-023-01786-w

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