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Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis

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arxiv 2410.04283 v1 pith:77JLNFZ3 submitted 2024-10-05 cs.LG

Mengfang Sun , Wenying Sun , Ying Sun , Shaobo Liu , Mohan Jiang , Zhen Xu This is my paper · ORCID
classification cs.LG
keywords creditfeaturesriskconvolutionaldatahybridmodelborrowers
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a novel approach to credit risk prediction by employing Graph Convolutional Neural Networks (GCNNs) to assess the creditworthiness of borrowers. Leveraging the power of big data and artificial intelligence, the proposed method addresses the challenges faced by traditional credit risk assessment models, particularly in handling imbalanced datasets and extracting meaningful features from complex relationships. The paper begins by transforming raw borrower data into graph-structured data, where borrowers and their relationships are represented as nodes and edges, respectively. A classic subgraph convolutional model is then applied to extract local features, followed by the introduction of a hybrid GCNN model that integrates both local and global convolutional operators to capture a comprehensive representation of node features. The hybrid model incorporates an attention mechanism to adaptively select features, mitigating issues of over-smoothing and insufficient feature consideration. The study demonstrates the potential of GCNNs in improving the accuracy of credit risk prediction, offering a robust solution for financial institutions seeking to enhance their lending decision-making processes.

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Cited by 4 Pith papers

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  4. Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data

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