Comparing Credit Risk Estimates in the Gen-AI Era
It addresses credit risk scoring for financial applications, but the results are incremental as they confirm limitations rather than offering new solutions.
This study compared credit score modeling techniques, finding that current generative AI models underperform traditional methods regardless of integration strategy, with no concrete performance numbers provided.
Generative AI technologies have demonstrated significant potential across diverse applications. This study provides a comparative analysis of credit score modeling techniques, contrasting traditional approaches with those leveraging generative AI. Our findings reveal that current generative AI models fall short of matching the performance of traditional methods, regardless of the integration strategy employed. These results highlight the limitations in the current capabilities of generative AI for credit risk scoring, emphasizing the need for further research and development before the possibility of applying generative AI for this specific task, or equivalent ones.