# A Time Attention based Fraud Transaction Detection Framework

arXiv: https://arxiv.org/abs/1912.11760 · HTML: https://mlsanity.com/paper/1912.11760/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [The Importance of Future Information in Credit Card Fraud Detection](https://mlsanity.com/paper/2204.05265/) (2022)
- [Application of Deep Reinforcement Learning to Payment Fraud](https://mlsanity.com/paper/2112.04236/) (2021)
- [Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection](https://mlsanity.com/paper/2101.04285/) (2021)
- [Behavioral graph fraud detection in E-commerce](https://mlsanity.com/paper/2210.06968/) (2022)
- [SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management](https://mlsanity.com/paper/2510.08948/) (2025)

## Abstract (arXiv)

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security. In this work, we present a novel method for detecting fraud transactions by leveraging patterns from both users' static profiles and users' dynamic behaviors in a unified framework. To address and explore the information of users' behaviors in continuous time spaces, we propose to use time attention based recurrent layers to embed the detailed information of the time interval, such as the durations of specific actions, time differences between different actions and sequential behavior patterns,etc., in the same latent space. We further combine the learned embeddings and users' static profiles altogether in a unified framework. Extensive experiments validate the effectiveness of our proposed methods over state-of-the-art methods on various evaluation metrics, especially on recall at top percent which is an important metric for measuring the balance between service experiences and risk of potential losses.