Abstract
The ability to assess and manage corporate credit risk enables financial institutions and investors to mitigate risk, enhance the precision of their decision-making, and adapt their strategies in a prompt and effective manner. The growing quantity of data and the increasing complexity of indicators have rendered traditional machine learning methods ineffective in enhancing the accuracy of credit risk assessment. Consequently, academics have begun to explore the potential of models based on deep learning. In this paper, we apply the concept of combining Transformer and CNN to the financial field, building on the traditional CNN-Transformer model's capacity to effectively process local features, perform parallel processing, and handle long-distance dependencies. To enhance the model's ability to capture financial data over extended periods and address the challenge of high-dimensional financial data, we propose a novel hybrid model, TCN-DilateFormer. This integration improves the accuracy of corporate credit risk assessment. The empirical study demonstrates that the model exhibits superior prediction accuracy compared to traditional machine learning assessment models, thereby offering a novel and efficacious tool for corporate credit risk assessment.