Abstract
Click Farming is fraudulent behaviors sponsored by malicious merchants to increase exposure by hiring fraudulent teams to place fraudulent orders, posing a serious threat to the operation of platforms. Traditional anti-fraud strategies are no longer applicable as they analyzed fraudulent behaviors individually and only rely on static statistical characteristics. In this paper, we propose a novel graph-based fraud detection framework deployed on JD.com composed of Dynamic Purchase Pattern learning (DPP) and Graph Neural Network with Similarities and Relations (GSR). Specifically, the DPP module is a feature extractor based on user click location sequences collected from websites. And the GSR module is a neighborhood sampling and aggregation algorithm for locating more accurate fraud groups and aggregating various information encoded by different types of subgroups. We conduct graph node classification experiments on a large-scale real-world dataset to verify the effectiveness of our framework, and the experimental results show that the DPP is able to capture more discriminative user patterns. Furthermore, GSR achieves the best performance compared to several state-of-the-art methods. Our method can be easily extended to other domains with the same problems as our task.
S. Wang and Y. Liu—Contribute equally to this work.
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Wang, S., Liu, Y., Zheng, C., Lin, R. (2022). Purchase Pattern Based Anti-Fraud Framework in Online E-Commerce Platform Using Graph Neural Network. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_9
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