paper-with-me

홈 › Papers

FraudTransformer: Time-Aware GPT for Transaction Fraud Detection

2025-09-28 · Gholamali Aminian, Andrew Elliott, Tiger Li, Timothy Cheuk Hin Wong, Victor Claude Dehon, Lukasz Szpruch, Carsten Maple, Christopher Read, Martin Brown, Gesine Reinert, Mo Mamouei arxiv

Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransformer, a sequence model that augments a vanilla GPT-style architecture with (i) a dedicated time encoder that embeds either absolute timestamps or inter-event values, and (ii) a learned positional encoder that preserves relative order. Experiments on a large industrial dataset -- tens of millions of transactions and auxiliary events -- show that FraudTransformer surpasses four strong classical baselines (Logistic Regression, XGBoost and LightGBM) as well as transformer ablations that omit either the time or positional component. On the held-out test set it delivers the highest AUROC and PRAUC.

📄 PDF Abstract BibTeX arXiv:2509.23712

Code (0)

등록된 구현이 없습니다.

Tasks

Fraud Detection

Similar Papers 제목 키워드 기반

Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection

2025-06-26 · Zhi Zheng, Bochuan Zhou, Yuping Song

Cryptocurrency transaction fraud detection faces the dual challenges of increasingly complex transaction patterns and severe class imbalance. Traditional methods rely on manual feature engineering and struggle to capture…

Anomaly DetectionFeature EngineeringFraud DetectionGraph Anomaly Detection+1

Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer

2023-07-11 · Yue Tian, Guanjun Liu

How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs)…

Fraud DetectionGraph Neural NetworkRepresentation Learning

TMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network

2026-06-16 · Rohit Tewari, Shubhankar Shilpi, Navin Chhibber, Devendra Singh Parmar 외 arxiv

In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities. To address these issues,…

Graph Neural NetworkContrastive LearningFraud Detection

The Importance of Future Information in Credit Card Fraud Detection

2022-04-11 · Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini

Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively and avoid further frauds. Since human ve…

Fraud Detection

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

2019-06-18 · Shaosheng Cao, Xinxing Yang, Cen Chen, Jun Zhou 외

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…

Fraud Detection