Industrial Financial Risk Prediction Model Based on Graph Neural Network and Knowledge Graph Inference

With the increasing complexity of the global financial market and the surge in data volume, how to effectively predict and avoid industrial financial risks has become a key issue that financial institutions and regulators need to address. Traditional prediction methods are often limited by the limitations of data representation and the ability of models to deal with complex relationships, while graph neural networks (GNNs) and knowledge graphs (KGs) provide a new perspective and means to solve these problems. This study constructs a KG covering a wide range of industrial financial information, which not only contains the direct correlation between industries but also incorporates rich background knowledge and semantic information. A novel GNN architecture is designed, which can make full use of the structured information in the KG to learn the potential correlations and dependencies between industries. Experimental results show that compared with the existing industrial financial risk prediction models, the proposed model has significant advantages in forecasting accuracy, stability and generalization ability. Especially when dealing with complex and changeable industrial financial data, the model can more accurately capture potential risk factors and provide timely and reliable risk early warning for financial institutions. The industrial financial risk prediction model based on GNN and KG reasoning proposed in this study provides a new and effective solution for industrial financial risk prediction. The model not only improves the accuracy and robustness of the forecast but also provides financial institutions with more intelligent and refined risk management tools.