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

• URL: http://arxiv.org/abs/2409.17909v1
• Date: Mon, 23 Sep 2024 22:41:54 GMT
• Title: Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment
• Authors: Bingyao Liu, Iris Li, Jianhua Yao, Yuan Chen, Guanming Huang, Jiajing Wang
• Abstract summary: 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.
• Score: 7.33330721560182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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".

Related papers

• Training A Foundation Model to Represent Graphs as Vectors [24.592499205332413]
  This paper aims to train a graph foundation model that is able to represent any graph as a vector preserving semantic information.<n>We provide a theoretical generalization bound to support the effectiveness of the proposed model.<n>The experimental results of few-shot graph classification and graph clustering show that our model outperforms strong baselines.
  arXiv  Detail & Related papers  (2026-02-04T06:06:28Z)
• NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction [4.743574336827573]
  Graph representation methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships.<n>Existing graph models specifically designed for stock prediction often suffer from excessive complexity and poor generalization.<n>We propose a long-term stock prediction task and develop a Node-level Graph Attention Network (NGAT) specifically tailored for corporate relationship graphs.
  arXiv  Detail & Related papers  (2025-07-02T13:59:46Z)
• Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs [1.5998200006932823]
  This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network. By building an end-to-end joint model, this paper achieves efficient recognition and reasoning of entities and relationships.
  arXiv  Detail & Related papers  (2024-11-19T16:23:49Z)
• Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification [5.000404730573809]
  Graph Neural Networks (GNNs) have attracted increasing attention in recent years. Recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph. We propose a novel framework for semi-supervised classification.
  arXiv  Detail & Related papers  (2022-12-04T06:17:11Z)
• GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs [72.51835626235368]
  We argue that when comparing two graphs, the distribution of node structural features is more informative than global graph statistics. We present GraphDCA - a framework for evaluating similarity between graphs based on the alignment of their respective node representation sets.
  arXiv  Detail & Related papers  (2022-02-08T14:19:19Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Spectral Embedding of Graph Networks [76.27138343125985]
  We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure. The embedding is based on a generalized graph Laplacian, whose eigenvectors compactly capture both network structure and neighborhood proximity in a single representation.
  arXiv  Detail & Related papers  (2020-09-30T04:59:10Z)
• Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
  We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects. To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
  arXiv  Detail & Related papers  (2020-07-08T19:48:19Z)
• Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection [35.23956785670788]
  We present a novel adaptive graph convolutional network with attention graph clustering (GCAGC) We develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. We evaluate our proposed GCAGC method on three cosaliency detection benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-13T09:35:59Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.