The traditional approach to credit risk modeling has relied heavily on deterministic predictive AI, but modern machine learning techniques can offer enhanced accuracy if appropriately applied.

A significant challenge with advanced models is their lack of interpretability; constraints must be integrated during development to align model outcomes with human intuition.

Generative AI holds promise for automating operational processes and generating synthetic data, which can be vital in scenarios lacking historical macroeconomic data.

While the benefits of AI in credit risk modeling are substantial, potential risks - including biases, legal concerns, and data confidentiality - must be carefully managed to protect against misuse.