Multi-Channel Graph Neural Network for Financial Risk Prediction of NEEQ Enterprises

TL;DR

This paper introduces a multi-channel graph neural network that combines financial indicators, textual data, and enterprise relationships to improve risk prediction for NEEQ-listed SMEs, outperforming traditional methods.

Contribution

It presents a novel Triple-Channel Graph Isomorphism Network that fuses multiple data modalities for enhanced financial risk prediction of SMEs.

Findings

Model achieves higher AUC, Precision, Recall, F1 scores than baselines.

Multi-modality fusion improves prediction robustness.

Experimental validation on real-world data confirms effectiveness.

Abstract

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.