TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
June 18, 2019 ยท Declared Dead ยท ๐ Proceedings of the VLDB Endowment
"No code URL or promise found in abstract"
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Authors
Shaosheng Cao, Xinxing Yang, Cen Chen, Jun Zhou, Xiaolong Li, Yuan Qi
arXiv ID
1906.07407
Category
cs.LG: Machine Learning
Cross-listed
cs.CR,
stat.ML
Citations
65
Venue
Proceedings of the VLDB Endowment
Last Checked
3 months ago
Abstract
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world. The system is able to predict online real-time transaction fraud in mere milliseconds. We present the problem definition, feature extraction, detection methods, implementation and deployment of the system, as well as empirical effectiveness. Extensive experiments have been conducted on large real-world transaction data to show the effectiveness and the efficiency of the proposed system.
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