GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs
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Abstract (arXiv)
Credit card fraud has significant implications at both an individual and societal level, making effective prevention essential. Current methods rely heavily on feature engineering and labeled information, both of which have significant limitations. In this work, we present GraphGuard, a novel contrastive self-supervised graph-based framework for detecting fraudulent credit card transactions. We conduct experiments on a real-world dataset and a synthetic dataset. Our results provide a promising initial direction for exploring the effectiveness of graph-based self-supervised approaches for credit card fraud detection.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 17 Jul 2024.
Builds on
9 of 22 references.
References · 9
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
- 2020Self-supervised Learning: Generative or Contrastivecited by 694
- 2021Self-supervised Learning is More Robust to Dataset Imbalancecited by 123
- 2018Learning graphs from data: A signal representation perspectivecited by 119
- 2021Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detectioncited by 67
- 2017SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Sparkcited by 15
- 2022From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approachcited by 13
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Citing papers per year: 2026: 1.
1 paper.