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

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.

[1]  Liqing Zhang,et al.  Credit Card Fraud Detection Using Convolutional Neural Networks , 2016, ICONIP.

[2]  Tong Zhang,et al.  Crowd Fraud Detection in Internet Advertising , 2015, WWW.

[3]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[4]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[5]  Anazida Zainal,et al.  Fraud detection system: A survey , 2016, J. Netw. Comput. Appl..

[6]  Chunyan Miao,et al.  A Fraud Resilient Medical Insurance Claim System , 2016, AAAI.

[7]  Nathalie Japkowicz,et al.  Beyond the Boundaries of SMOTE - A Framework for Manifold-Based Synthetically Oversampling , 2016, ECML/PKDD.

[8]  Stefano Zanero,et al.  BankSealer: A decision support system for online banking fraud analysis and investigation , 2015, Comput. Secur..

[9]  Che-Wei Huang,et al.  FrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection , 2015, KDD.

[10]  C. Bianchi,et al.  Risk, trust, and consumer online purchasing behaviour : a Chilean perspective , 2012 .

[11]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[12]  S. C. Kremer,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[13]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[14]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[15]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[16]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[17]  Victor S. Sheng,et al.  Thresholding for Making Classifiers Cost-sensitive , 2006, AAAI.

[18]  Damminda Alahakoon,et al.  Minority report in fraud detection: classification of skewed data , 2004, SKDD.

[19]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[20]  Angelos Stavrou,et al.  Click Fraud Detection on the Advertiser Side , 2014, ESORICS.

[21]  Oren Barkan,et al.  ITEM2VEC: Neural item embedding for collaborative filtering , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[22]  Vrizlynn L. L. Thing,et al.  Conditional Weighted Transaction Aggregation for Credit Card Fraud Detection , 2014, IFIP Int. Conf. Digital Forensics.

[23]  Gang Chen,et al.  Personal recommendation using deep recurrent neural networks in NetEase , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[24]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[25]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[26]  Alexandros Karatzoglou,et al.  Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations , 2016, RecSys.

[27]  Izak Benbasat,et al.  A Comprehensive Model of Perceived Risk of E-Commerce Transactions , 2010, Int. J. Electron. Commer..

[28]  Yong Liu,et al.  Improved Recurrent Neural Networks for Session-based Recommendations , 2016, DLRS@RecSys.

[29]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[30]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.