Version 1
: Received: 8 November 2024 / Approved: 8 November 2024 / Online: 8 November 2024 (11:37:16 CET)
How to cite:
Li, N.; Xu, L.; Zhang, X.; Zou, J. Research on Fincial Fraud Detection based on Deep Graph Neural Network. Preprints2024, 2024110609. https://doi.org/10.20944/preprints202411.0609.v1
Li, N.; Xu, L.; Zhang, X.; Zou, J. Research on Fincial Fraud Detection based on Deep Graph Neural Network. Preprints 2024, 2024110609. https://doi.org/10.20944/preprints202411.0609.v1
Li, N.; Xu, L.; Zhang, X.; Zou, J. Research on Fincial Fraud Detection based on Deep Graph Neural Network. Preprints2024, 2024110609. https://doi.org/10.20944/preprints202411.0609.v1
APA Style
Li, N., Xu, L., Zhang, X., & Zou, J. (2024). Research on Fincial Fraud Detection based on Deep Graph Neural Network. Preprints. https://doi.org/10.20944/preprints202411.0609.v1
Chicago/Turabian Style
Li, N., Xuyang Zhang and Jianke Zou. 2024 "Research on Fincial Fraud Detection based on Deep Graph Neural Network" Preprints. https://doi.org/10.20944/preprints202411.0609.v1
Abstract
Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such acts not only disrupt the order of the financial market, but also harm social and economic development, and breed other illegal and criminal acts. With the proliferation of the internet and online payment methods, many fraudulent activities and money laundering have shifted from offline to online, posing a significant challenge for regulators. In this work, we proposed a novel detection model by utilizing the graph neural network. Specifically, the general model structure includes the combination of Graph Convolutional Network (GCN) and Graph Attention Network (GAT), which can effectively capture the complex relationships and features in the financial transaction network. By building a multilayer graph neural network, the model can perform deep learning on the implicit patterns between nodes, thereby improving the accuracy of fraud detection. Experimental analysis results show that the performance of the proposed method on multiple public datasets is better than that of traditional methods, showing its potential in practical application.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
