TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 1; 2026: 2.
9 papers.
- 2022BRIGHT -- Graph Neural Networks in Real-Time Fraud Detectioncited by 15
- 2023Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Surveycited by 5
- 2024Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detectioncited by 3
- 2021SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Treecited by 2
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databasescited by 1
- 2024AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodescited by 1
- 2025SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Managementcited by 1
- 2026Practical Anonymous Two-Party Gradient Boosting Decision Treecited by 0
- 2026SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detectioncited by 0
- 2026SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection23 Sep 2026
- 2026Practical Anonymous Two-Party Gradient Boosting Decision Tree26 May 2026
- 2025SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management10 Oct 2025
- 2024AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes15 Apr 2024
- 2024Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection13 Feb 2024
- 2023Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey11 Mar 2023
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases29 May 2022
- 2022BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection25 May 2022
- 2021SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree21 Oct 2021