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Media type:
E-Book
Title:
Enhancing Financial Fraud Detection with Hierarchical Graph Attention Networks
:
A Study on Integrating Local and Extensive Structural Information
Description:
Cutting-edge machine learning techniques, particularly the hierarchical graph attention network (HGAT), emerge as a promising approach for financial fraud detection. The proposed approach includes encoding adjacency matrices for capturing local relationships and utilizing multi-head self-attention to propagate structural attributes across multiple layers. Node embeddings are generated by the HGAT model, which integrates both local and extensive structural information through multi-head self-attention. Through learning intricate inter-entity relationships, the HGAT model can effectively identify potential financial risks. Experiments performed on a publicly available financial report dataset demonstrate the superior performance of our model compared to the existing methods in detecting financial risks