# Comparing Credit Risk Estimates in the Gen-AI Era

arXiv: https://arxiv.org/abs/2506.07754 · HTML: https://mlsanity.com/paper/2506.07754/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems](https://mlsanity.com/paper/2407.10701/) (2024)
- [Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models](https://mlsanity.com/paper/2310.00566/) (2023)
- [Fairness of ChatGPT](https://mlsanity.com/paper/2305.18569/) (2023)
- [Fairness of ChatGPT and the Role Of Explainable-Guided Prompts](https://mlsanity.com/paper/2307.11761/) (2023)
- [Evaluating AI fairness in credit scoring with the BRIO tool](https://mlsanity.com/paper/2406.03292/) (2024)
- [On the equivalence between Kolmogorov-Smirnov and ROC curve metrics for binary classification](https://mlsanity.com/paper/1606.00496/) (2016)

## 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.