Construction and optimization of cross-industry financial anomaly prediction model based on transfer learning

This study focuses on the problem of enterprise financial anomaly prediction. Aiming at the limitations of traditional models in cross-industry scenarios, a cross-industry financial anomaly prediction model based on transfer learning is proposed. The model integrates domain adaptive mechanism and multi-source heterogeneous data fusion strategy, aiming at breaking through the limitation of data heterogeneity between different industries on model migration effect and improving the generalization ability of the model in target industries. In the model, the Domain-Adversarial Neural Network (DANN) is used to align the feature space of the source domain and the target domain, and the graph neural network (GNN) is used to process heterogeneous data, mining abnormal propagation paths across entities, and a dynamic migration strategy is introduced to optimize the adaptability of the model in the target domain. The experimental results show that the weighted F1-score of the proposed model is improved by 12.0% and 14.2% respectively in the target industries such as medical services and financial technology, showing significant performance advantages. In addition, the model can quickly adapt to and recover its performance in the face of data distribution deviation, with good real-time adaptability and stability. This study provides low-cost risk control tools for small and medium-sized enterprises, and also provides new ideas and methods for regulators to build a unified cross-industry risk monitoring framework.