Real-Time Transaction Fraud Detection via Heterogeneous Temporal Graph Neural Network

Hang Nguyen, Hang Nguyen, Bac Le, Bac Le

2025

Abstract

As digital transactions grow in prevalence, the threat of fraud has become a critical challenge for businesses and individuals. Fraudsters increasingly employ sophisticated tactics, disguising malicious activities as legitimate behavior, which renders traditional detection methods inadequate. This paper introduces a real-time fraud detection framework leveraging Heterogeneous Temporal Graph Neural Networks (HTGNN) to address these challenges. The proposed approach constructs a heterogeneous temporal graph from transaction data and employs a neural network architecture that integrates spatial, temporal, and semantic information. This allows for a comprehensive representation of transactions, entities, and their dynamic interactions over time. Unlike static approaches, our method captures the temporal evolution of behaviors, ensuring deeper insights into fraudulent patterns. The framework is designed to enhance detection accuracy while maintaining computational efficiency for real-time applications. Through rigorous experimentation and analysis, we expect to demonstrate that the proposed HTGNN framework significantly outperforms existing techniques in identifying fraudulent transactions, ultimately contributing to more robust and effective fraud detection systems.

Paper Citation

in Harvard Style

Nguyen H. and Le B. (2025). Real-Time Transaction Fraud Detection via Heterogeneous Temporal Graph Neural Network. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 364-375. DOI: 10.5220/0013161300003890

in Bibtex Style

@conference{icaart25,
author={Hang Nguyen and Bac Le},
title={Real-Time Transaction Fraud Detection via Heterogeneous Temporal Graph Neural Network},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={364-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013161300003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Real-Time Transaction Fraud Detection via Heterogeneous Temporal Graph Neural Network
SN - 978-989-758-737-5
AU - Nguyen H.
AU - Le B.
PY - 2025
SP - 364
EP - 375
DO - 10.5220/0013161300003890
PB - SciTePress