Dynamic graph neural networks for financial crisis modeling and blocking in large-scale enterprise groups

Facing the problem of dynamic financial risk propagation under the trend of enterprise group development, this paper proposes a graph neural network modelling method based on distributed training. Aiming at the shortcomings of traditional models that are difficult to capture the dynamic nonlinear multi-hop features of risk contagion, the study constructs a dual-path dynamic learning framework: firstly, a time-varying graph structure is established to characterise the topological evolution of the enterprise association network, and a multi-layer dynamic graph convolution network is used to realise the extraction of nonlinear risk propagation modes; and then, a GPU cluster-driven distributed training architecture is designed to break through the computational bottleneck of large-scale graph data. The experimental results show that the model improves 15%–20% over the sub-optimal model in the three core metrics of AUC-ROC (0.93), Precision@K (0.85) and F1-score (0.88). Combined with the targeted blocking strategy generated by interpretability analysis, the crisis node abatement rate reaches 72% at the 10th time step, forming a closed loop of management from risk prediction to proactive intervention. This study expands the application paradigm of graph neural networks in the field of financial risk control, and provides a whole-process solution for preventing systemic financial risks.