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2024 | OriginalPaper | Buchkapitel

Attempt of Graph Neural Network Algorithm in the Field of Financial Anomaly Detection

verfasst von : Hengli Feng, Anqi Xie

Erschienen in: Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology

Verlag: Springer Nature Singapore

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Abstract

In recent years, the development of mobile internet and big data has propelled the digital transformation of the finance industry, enabling inclusive finance across society. Meanwhile, financial fraud detection technologies need continuous updates and improvements to combat new fraud tactics. Currently, the development of anomaly detection techniques in the traditional finance industry lags behind the pace of financial digitalization. In addition, emerging technologies lack practical application in financial scenarios. Therefore, we test the anomaly detection performance of two graph neural networks, GCN and GAT, on the DGraph dataset and compare with the MLP model. Experiments demonstrate that graph neural networks outperform the fully connected network and achieve good performance on financial anomaly detection.

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Titel: Attempt of Graph Neural Network Algorithm in the Field of Financial Anomaly Detection
verfasst von: Hengli Feng
Anqi Xie
Verlag: Springer Nature Singapore
Buch: Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology
Print ISBN: 978-981-9727-56-8

Electronic ISBN: 978-981-9727-57-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2757-5_65

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