Credit Risk Scoring in the Age of AI: A Systematic Comparison of Traditional, ML, and DL Models Based on Accuracy, Stability, and Interpretability
This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring, and Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability.