Comparing Credit Risk Estimates in the Gen-AI Era
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Abstract (arXiv)
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.
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References · 8
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2020Metrics for Multi-Class Classification: an Overviewcited by 143
- 2024DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systemscited by 37
- 2023Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Modelscited by 24
- 2023Fairness of ChatGPTcited by 20
- 2023Fairness of ChatGPT and the Role Of Explainable-Guided Promptscited by 13
- 2024Evaluating AI fairness in credit scoring with the BRIO toolcited by 3
- 2016On the equivalence between Kolmogorov-Smirnov and ROC curve metrics for binary classificationcited by 1
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