Name
Title | ||
|---|---|---|
Session-Based Fraud Detection in Online E-Commerce Transactions Using Recurrent Neural Networks. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-71273-4_20 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Fraud detection,Web mining,Recurrent neural network | Data mining,Embedding,Web mining,Computer science,Recurrent neural network,Incremental build model,Database transaction,E-commerce | Conference |
Volume | ISSN | Citations |
10536 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 19 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuhao Wang | 1 | 20 | 2.54 |
| Cancheng Liu | 2 | 3 | 0.40 |
| Xiang Gao | 3 | 3 | 0.40 |
| Hongtao Qu | 4 | 3 | 0.40 |
| Wei Xu | 5 | 656 | 41.71 |