Model Design Analysis.

This study aims to improve the identification of potential credit risks in unstructured financial texts. It addresses the core problem of financial text analysis and credit risk assessment by proposing a hybrid model that combines the generative semantic understanding of Generative Pre-trained Transformer-4 (GPT-4) with the enhanced feature extraction of Bidirectional Encoder Representations from Transformers (BERT). To overcome the limitations of traditional methods—such as weak contextual reasoning in long texts, insufficient recognition of industry-specific terminology, and implicit credit risk expressions—the model incorporates a financial dictionary enhancement module and a named entity recognition (NER) component. GPT-4 is leveraged for prompt-based generation to extract latent risk information from complex texts, including annual reports. A dual-model semantic fusion mechanism with attention weighting constructs a multi-level risk assessment system that integrates contextual understanding, industry adaptability, and interpretability. Experiments on multiple publicly available financial datasets and real-world annual reports demonstrate the model’s effectiveness. Results show that the proposed approach outperforms representative baseline models in accuracy, adaptability, and interpretability. This work carries both theoretical and practical significance for research at the intersection of financial technology and natural language processing.