Session-Based Fraud Detection in Online E-Commerce Transactions Using Recurrent Neural Networks
| Autor: | Cancheng Liu, Gao Xiang, Wei Xu, Shuhao Wang, Hongtao Qu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science 02 engineering and technology E-commerce Machine learning computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Recurrent neural network Web mining 020204 information systems 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Incremental build model Artificial intelligence Session (computer science) business computer Database transaction |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712727 ECML/PKDD (3) |
| DOI: | 10.1007/978-3-319-71273-4_20 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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