融合财务数据、文本与企业关系图谱，提升新三板企业风险预测准确率。
Multi-Channel Graph Neural Network for Financial Risk Prediction of NEEQ Enterprises
- 设计三通道图神经网络，分别处理数值、文本和关系数据。
- 在7731家新三板企业上测试，各项指标均显著优于传统方法。
- 适合金融监管、投资决策者使用，具实际应用价值。
随着中国多层次资本市场的不断发展，全国中小企业股份转让系统（NEEQ），即“新三板”，已成为中小型企业的重要融资平台。然而，由于规模有限且财务抗风险能力较弱，许多挂牌企业面临较高的财务困境风险。为解决此问题，我们提出一种多通道深度学习框架，整合结构化财务指标、文本披露信息及企业关系数据，实现对企业财务风险的全面预测。具体而言，设计了三通道图同构网络（GIN），分别处理数值型、文本型和图结构输入数据。各模态特异性表示通过基于注意力的机制与门控单元融合，以增强模型鲁棒性与预测精度。在涵盖7731家真实新三板企业的数据集上实验表明，该模型在AUC、精确率、召回率和F1分数等指标上显著优于传统机器学习方法及单模态基线模型。本研究为中小企业风险建模提供了理论与实践支持，并为金融监管机构与投资者提供了数据驱动的风险评估工具。
原文摘要 · Abstract (English)
With the continuous evolution of China's multi-level capital market, the National Equities Exchange and Quotations (NEEQ), also known as the "New Third Board," has become a critical financing platform for small and medium-sized enterprises (SMEs). However, due to their limited scale and financial resilience, many NEEQ-listed companies face elevated risks of financial distress. To address this issue, we propose a multi-channel deep learning framework that integrates structured financial indicators, textual disclosures, and enterprise relationship data for comprehensive financial risk prediction. Specifically, we design a Triple-Channel Graph Isomorphism Network (GIN) that processes numeric, textual, and graph-based inputs separately. These modality-specific representations are fused using an attention-based mechanism followed by a gating unit to enhance robustness and prediction accuracy. Experimental results on data from 7,731 real-world NEEQ companies demonstrate that our model significantly outperforms traditional machine learning methods and single-modality baselines in terms of AUC, Precision, Recall, and F1 Score. This work provides theoretical and practical insights into risk modeling for SMEs and offers a data-driven tool to support financial regulators and investors.
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