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

arXiv: https://arxiv.org/abs/1906.07407 · HTML: https://mlsanity.com/paper/1906.07407/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 10 of 10)

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- [Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases](https://mlsanity.com/paper/2205.14664/) (2022)
- [AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes](https://mlsanity.com/paper/2404.09679/) (2024)
- [SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management](https://mlsanity.com/paper/2510.08948/) (2025)
- [Practical Anonymous Two-Party Gradient Boosting Decision Tree](https://mlsanity.com/paper/2605.26903/) (2026)
- [SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection](https://mlsanity.com/paper/2609.27287/) (2026)
- [Dual-Scale Relational Graph Transformers for Ecosystem-Aware Fraud Detection](https://mlsanity.com/paper/2610.04138/) (2026)

## Abstract (arXiv)

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.