TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

Cao, Shaosheng, Yang, Xinxing, Chen, Cen, Zhou, Jun, Li, Xiaolong, Qi, Yuan

arXiv.org Machine Learning 

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.