# Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

arXiv: https://arxiv.org/abs/2412.18287 · HTML: https://mlsanity.com/paper/2412.18287/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [PairNorm: Tackling Oversmoothing in GNNs](https://mlsanity.com/paper/1909.12223/) (2019)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews](https://mlsanity.com/paper/1303.4402/) (2013)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation](https://mlsanity.com/paper/1803.11029/) (2018)
- [DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding](https://mlsanity.com/paper/1709.04696/) (2017)
- [Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification](https://mlsanity.com/paper/1801.09927/) (2018)
- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://mlsanity.com/paper/2305.08740/) (2023)

## Built on by (most cited first, 26 of 26)

- [ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks](https://mlsanity.com/paper/2503.16693/) (2025)
- [Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention](https://mlsanity.com/paper/2504.08183/) (2025)
- [Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models](https://mlsanity.com/paper/2503.14411/) (2025)
- [Dynamic Bundling with Large Language Models for Zero-Shot Inference on Text-Attributed Graphs](https://mlsanity.com/paper/2505.17599/) (2025)
- [FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations](https://mlsanity.com/paper/2506.11635/) (2025)
- [Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection](https://mlsanity.com/paper/2606.28134/) (2026)
- [Robust Partial-Label Learning by Leveraging Class Activation Values](https://mlsanity.com/paper/2502.11743/) (2025)
- [Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection](https://mlsanity.com/paper/2509.20339/) (2025)
- [Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detection](https://mlsanity.com/paper/2605.28524/) (2026)
- [SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection](https://mlsanity.com/paper/2606.08146/) (2026)
- [SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection](https://mlsanity.com/paper/2501.12430/) (2025)
- [Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities](https://mlsanity.com/paper/2502.10750/) (2025)
- [Effective High-order Graph Representation Learning for Credit Card Fraud Detection](https://mlsanity.com/paper/2503.01556/) (2025)
- [CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision](https://mlsanity.com/paper/2508.12278/) (2025)
- [Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection](https://mlsanity.com/paper/2510.05562/) (2025)
- [Graph Neural Network with One-side Edge Sampling for Fraud Detection](https://mlsanity.com/paper/2601.06800/) (2026)
- [Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud](https://mlsanity.com/paper/2601.11073/) (2026)
- [Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection](https://mlsanity.com/paper/2603.03106/) (2026)
- [Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling](https://mlsanity.com/paper/2603.03662/) (2026)
- [Graph-Based Fraud Detection with Dual-Path Graph Filtering](https://mlsanity.com/paper/2604.14235/) (2026)
- [Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning](https://mlsanity.com/paper/2608.02168/) (2026)
- [Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection](https://mlsanity.com/paper/2608.15768/) (2026)
- [DynGraphAgentBench: A Benchmark for Agentic Lifecycle Control in Dynamic Graph Anomaly Detection](https://mlsanity.com/paper/2609.33980/) (2026)
- [Behavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoning](https://mlsanity.com/paper/2609.34211/) (2026)
- [Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes](https://mlsanity.com/paper/2609.38424/) (2026)
- [Dual-Scale Relational Graph Transformers for Ecosystem-Aware Fraud Detection](https://mlsanity.com/paper/2610.04138/) (2026)

## Abstract (arXiv)

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.