Foundation Models in Credit Risk Prediction

This paper is a research article that evaluates the role of foundation models in credit risk prediction, highlighting their significance in enhancing predictive modeling practices. The study focuses particularly on TabPFN, a pretrained model designed for tabular data, and benchmarks it against existing machine learning techniques in predicting probability of default (PD) and loss given default (LGD). The research outlines the methodology employed for evaluation, which includes various datasets and performance indicators to assess the effectiveness of pretraining in addressing challenges associated with credit scoring. The findings suggest that TabPFN shows improved performance compared to its competitors, especially in smaller datasets, and does not require hyperparameter tuning, resulting in reduced computational costs. The article also discusses the ongoing verification of results to ensure the robustness of findings and the implications for model interpretability. The results aim to benefit both academics and practitioners in the realm of credit risk management.