# Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention

arXiv: https://arxiv.org/abs/2504.08183 · HTML: https://mlsanity.com/paper/2504.08183/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation](https://mlsanity.com/paper/2412.18287/) (2024)
- [Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion](https://mlsanity.com/paper/2502.03664/) (2025)
- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications](https://mlsanity.com/paper/2412.18222/) (2024)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [Heterogeneous Graph Auto-Encoder for CreditCard Fraud Detection](https://mlsanity.com/paper/2410.08121/) (2024)

## Built on by (most cited first, 8 of 8)

- [Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment](https://mlsanity.com/paper/2508.09399/) (2025)
- [Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling](https://mlsanity.com/paper/2509.19032/) (2025)
- [Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering](https://mlsanity.com/paper/2510.15436/) (2025)
- [Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks](https://mlsanity.com/paper/2511.12122/) (2025)
- [Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition](https://mlsanity.com/paper/2508.12565/) (2025)
- [Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns](https://mlsanity.com/paper/2512.03696/) (2025)
- [A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents](https://mlsanity.com/paper/2605.01143/) (2026)
- [Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks](https://mlsanity.com/paper/2606.28923/) (2026)

## Abstract (arXiv)

This study proposes a credit card fraud detection method based on Heterogeneous Graph Neural Network (HGNN) to address fraud in complex transaction networks. Unlike traditional machine learning methods that rely solely on numerical features of transaction records, this approach constructs heterogeneous transaction graphs. These graphs incorporate multiple node types, including users, merchants, and transactions. By leveraging graph neural networks, the model captures higher-order transaction relationships. A Graph Attention Mechanism is employed to dynamically assign weights to different transaction relationships. Additionally, a Temporal Decay Mechanism is integrated to enhance the model's sensitivity to time-related fraud patterns. To address the scarcity of fraudulent transaction samples, this study applies SMOTE oversampling and Cost-sensitive Learning. These techniques strengthen the model's ability to identify fraudulent transactions. Experimental results demonstrate that the proposed method outperforms existing GNN models, including GCN, GAT, and GraphSAGE, on the IEEE-CIS Fraud Detection dataset. The model achieves notable improvements in both accuracy and OC-ROC. Future research may explore the integration of dynamic graph neural networks and reinforcement learning. Such advancements could enhance the real-time adaptability of fraud detection systems and provide more intelligent solutions for financial risk control.