Research on Graph Neural Network Algorithms for Financial Anomaly Detection

In recent years, advanced digital technologies like big data, blockchain, and artificial intelligence have rapidly developed. While digital fintech has played an increasingly important role in society, benefiting development of the financial industries, it has also escalated anomaly risks. There is...

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Bibliographic Details
Published in2023 International Conference on Networks, Communications and Intelligent Computing (NCIC) pp. 18 - 23
Main Authors Lu, Xin, Duan, Chengge, Xie, Anqi, Bao, Menghu, Wang, Minze, Zhong, QiLong
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.11.2023
Subjects
Computational modeling
DGraph
Feature extraction
Finance
Financial anomalies
Graph neural networks
MLP
Prediction algorithms
Predictive models
Technological innovation
Training
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Summary:In recent years, advanced digital technologies like big data, blockchain, and artificial intelligence have rapidly developed. While digital fintech has played an increasingly important role in society, benefiting development of the financial industries, it has also escalated anomaly risks. There is an urgent need to apply new digital technologies to traditional finance for anomaly prevention. Although numerous anomaly detection algorithms exist, financial anomaly detection in complex contexts remains theoretical. Given this, we compared three graph neural networks (GCN, GraphSAGE, GAT) against MLP model for anomaly prediction on the DGraph dataset. and the experiments prove that the graph neural network results all outperform the fully connected network.
DOI:10.1109/NCIC61838.2023.00009