Deep Learning-Based Intelligent Early Warning System for Corporate Financial Risk

Abstract

With the advancement of computer technology, corporate financial risk control systems face challenges related to complex data processing and delays in risk identification. This paper proposes an intelligent risk control method based on a hybrid LSTM-GNN (Long Short-Term Memory-Graph Neural Network) model, utilizing LSTM to capture the temporal characteristics of financial indicators and GNN to model inter-enterprise relationship networks. A distributed microservice architecture based on Spring Cloud was constructed to enable real-time financial data processing and risk early warning. Experimental results show that this method achieved a risk identification accuracy of 91.2% ± 0.2% on the validation set, representing a significant improvement over single-model approaches. The system employs a multi-level early warning mechanism, identifying risk events an average of 8.5 trading days in advance, with an API response time under 100 ms and system availability reaching 99.9%. These findings provide a novel technical solution for corporate financial risk prevention and control, offering significant practical value.