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Fraud detection at eBay
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DOI：10.1016/j.ememar.2025.101277.png)
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Abstract
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Fraud detection is a key research topic for e-commerce, addressing challenges like dynamic heterogeneity and interlinked fraudulent patterns. Existing efforts include rule-based and machine learning systems, but graph-based approaches are increasingly critical. This paper presents the first systematic review of fraud detection in real-world e-commerce environment like eBay, leveraging multi-source data such as transaction logs and user behavior, dealing with challenges of information heterogeneity, scalability, graph dynamics, explainability, and adaptability. We also highlight eBay's efforts in designing explainable fraud detection systems with graph neural networks (GNNs) tailored to deployment needs and offer insights and recommendations for advancing research.
Keywords:
Graph neural networks
Transaction fraud detection
Explainability
User behavioral embedding
Click stream
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