Transaction frauds impose serious threats onto e-commerce. We present CLUE, a novel deep-learning-based transaction fraud detection system we design and deploy at JD.com, one of the largest e-commerce platforms in China with over 220 million active users. CLUE captures detailed information on users’ click actions using neural-network based embedding, and models sequences of such clicks using the recurrent neural network. Furthermore, CLUE provides application-specific design optimizations including imbalanced learning, real-time detection, and incremental model update. Using real production data for over eight months, we show that CLUE achieves over 3x improvement over the existing fraud detection approaches.
CITATION STYLE
Wang, S., Liu, C., Gao, X., Qu, H., & Xu, W. (2017). Session-Based Fraud Detection in Online E-Commerce Transactions Using Recurrent Neural Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10536 LNAI, pp. 241–252). Springer Verlag. https://doi.org/10.1007/978-3-319-71273-4_20
Mendeley helps you to discover research relevant for your work.