Session-Based Fraud Detection in Online E-Commerce Transactions Using Recurrent Neural Networks

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 10536; pp. 241 - 252
• Main Authors: Wang, Shuhao, Liu, Cancheng, Gao, Xiang, Qu, Hongtao, Xu, Wei
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Fraud detection; Recurrent neural network; Web mining
• ISBN: 9783319712727; 3319712721
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-71273-4_20

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.