Abstract:

The challenge of accurately assessing credit risk has grown with the complexity of financial markets and the availability of big data. Traditional models often fall short of capturing dynamic risk factors. The problem is addressed by proposing the Factor-Enhanced Additive Neural Network （FAAN） model， combining big data dimensionality reduction techniques with deep learning approximation to improve accuracy and interpretability in credit risk measurement.The main research question is designing an interpretable deep learning model that outperforms existing methods in measuring corporate credit risk. The FAAN model tackles this by integrating factor-based dimensionality reduction to remove noise and deep learning to capture complex non-linear relationships. It is more effective than traditional models like linear regression and popular interpretable machine learning methods （e.g.， GAM， EBM）.Empirical analysis using real-world data shows that the FAAN model： （1） consistently outperforms external credit ratings and other models across various metrics， demonstrating superior generalization； （2） highlights price indicators as crucial dynamic factors that enhance credit risk detection； and （3） confirms that combining dimensionality reduction with deep learning significantly improves credit rating quality.It contributes to research on interpretable deep learning and corporate credit risk， offering a powerful tool for dynamic risk assessment and credit rating practices， advancing both academic understanding and practical applications.

Key words: credit risk, interpretable deep learning, FAAN