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

  • Shuhao WangEmail author
  • Cancheng Liu
  • Xiang Gao
  • Hongtao Qu
  • Wei Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)

Abstract

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.

Keywords

Fraud detection Web mining Recurrent neural network 

Notes

Acknowledgement

We would like to thank our colleagues at JD for their help during this research. This research is supported in part by the National Natural Science Foundation of China (NSFC) grant 61532001, Tsinghua Initiative Research Program Grant 20151080475, MOE Online Education Research Center (Quantong Fund) grant 2017ZD203, and gift funds from Huawei.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shuhao Wang
    • 1
    Email author
  • Cancheng Liu
    • 2
  • Xiang Gao
    • 2
  • Hongtao Qu
    • 2
  • Wei Xu
    • 1
  1. 1.Tsinghua UniversityBeijingChina
  2. 2.JD FinanceBeijingChina

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