User financial default prediction is crucial for credit risk management. The proposed MotifGNN method effectively captures motif-based graph structures for accurate predictions, outperforming existing methods on public and industrial datasets.
The paper introduces the challenges in traditional default prediction methods and proposes a novel approach using motifs in graph neural networks. By incorporating both lower-order and higher-order structures, the model achieves superior performance on various datasets.
The study highlights the importance of considering social relations in financial default prediction and demonstrates the effectiveness of the proposed MotifGNN method through extensive experiments.
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by Daixin Wang,... a las arxiv.org 03-12-2024
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