mlsanityData to 7 Oct 2026

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

arXiv 2409.17909 ↗First posted 23 Sep 2024q-fin.RM cs.CL cs.LGcited by 9

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Abstract (arXiv)

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".

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 23 Sep 2024.

Builds on

8 of 20 references.

References · 8

  1. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 88
  2. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
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  6. 2024A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methodscited by 12
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  8. 2024An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentationcited by 10

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Cited by year

Citing papers per year: 2024: 9.

9 papers.

  1. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  2. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  3. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  4. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  5. 2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
  6. 2024Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environmentscited by 7
  7. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
  8. 2024Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Managementcited by 6
  9. 2024Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systemscited by 5