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Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment

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arxiv 2409.17909 v1 pith:X5T73O46 submitted 2024-09-23 q-fin.RM cs.CLcs.LG

Bingyao Liu , Iris Li , Jianhua Yao , Yuan Chen , Guanming Huang , Jiajing Wang This is my paper · ORCID
classification q-fin.RMcs.CLcs.LG
keywords graphenterpriseindicatorsmodelcreditdatanetworkneural
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper takes the graph neural network as the technical framework, integrates the intrinsic connections between enterprise financial indicators, and proposes a model for enterprise credit risk assessment. The main research work includes: Firstly, based on the experience of predecessors, we selected 29 enterprise financial data indicators, abstracted each indicator as a vertex, deeply analyzed the relationships between the indicators, constructed a similarity matrix of indicators, and used the maximum spanning tree algorithm to achieve the graph structure mapping of enterprises; secondly, in the representation learning phase of the mapped graph, a graph neural network model was built to obtain its embedded representation. The feature vector of each node was expanded to 32 dimensions, and three GraphSAGE operations were performed on the graph, with the results pooled using the Pool operation, and the final output of three feature vectors was averaged to obtain the graph's embedded representation; finally, a classifier was constructed using a two-layer fully connected network to complete the prediction task. Experimental results on real enterprise data show that the model proposed in this paper can well complete the multi-level credit level estimation of enterprises. Furthermore, the tree-structured graph mapping deeply portrays the intrinsic connections of various indicator data of the company, and according to the ROC and other evaluation criteria, the model's classification effect is significant and has good "robustness".

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Cited by 3 Pith papers

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    cs.CV 2024-11 reject novelty 3.0 of 10

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  2. Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches

    cs.LG 2024-11 reject novelty 2.0 of 10

    MhERGAN couples MCMC-corrected GAN ensembles with MHLoss fine-tuning for few-shot learning, but the reported gains are small and under-validated.

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

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    A self-training CNN on 10,000 labeled CIFAR-10 images reaches 0.897 accuracy, but missing implementation details and baseline comparisons make the result unverifiable.

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