Graph convolutional network with dynamic gating LSTM improves corporate credit default prediction
Year: 2026
Type: article
Abstract: Traditional corporate credit evaluation models often treat enterprises as isolated entities and rely on static financial data, making it difficult to capture dynamic risk evolution and inter-enterprise risk transmission. This study proposes a Graph-fused Multi-Modal Dynamic Gating Network (GMDG-Net) that integrates three information modalities: internal financial and market data, external online public opinion text, and an enterprise association graph constructed from equity pledge relationships. A graph convolutional network learns spatial risk transmission patterns from the association graph. A word vector model extracts sentiment features from public opinion text. A structural adaptive gating long short-term memory network dynamically fuses these multi-modal temporal features and adjusts contribution weights of different information sources in real time. Experiments using a corporate stock pledge default prediction dataset (2017–2022, 12,384 samples) show that GMDG-Net achieves an accuracy of 0.913 and F1-score of 0.851, significantly outperforming baseline models including support vector machine (0.842), standard LSTM (0.889), and graph convolutional network alone (0.872). Multi-modal feature fusion experiments confirm that adding public opinion features increases accuracy by 2.2%, and adding graph structural features further increases accuracy by 1.8%. The model maintains robust performance under imbalanced sample conditions (weighted F1-score 0.897). This framework provides financial institutions with a more accurate and forward-looking credit risk assessment tool.
Source: Scientific Reports
Authors: Tingting Li, Yingli Wu
Institutions: Henan Finance University, Jiaxing Nanhu University
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7225452716