TitAnt: Online realtime transaction fraud detection in ant financial

  • Shaosheng Cao*
  • , Xin Xing Yang
  • , Cen Chen
  • , Jun Zhou
  • , Xiaolong Li
  • , Yuan Qi
  • *Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

42 Scopus citations

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.

Original languageEnglish
Pages (from-to)2082-2093
Number of pages12
JournalProceedings of the VLDB Endowment
Volume12
Issue number12
DOIs
StatePublished - 2018
Externally publishedYes
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: 26 Aug 201730 Aug 2017

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