Foundation Models for Credit Risk Prediction: A Game Changer?

TL;DR

This paper evaluates the effectiveness of foundation models for credit risk prediction, demonstrating their superior performance, especially in small-data scenarios, compared to traditional models.

Contribution

It benchmarks recent tabular foundation models against existing techniques across multiple credit risk tasks and datasets, highlighting their out-of-the-box performance and advantages in small-data settings.

Findings

Foundation models generally outperform competitors across datasets and tasks.

They show significant improvements when dataset size decreases.

Models perform well without hyperparameter tuning, reducing computational costs.

Abstract

Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses. Extensive research has introduced new modeling techniques, complemented by large-scale benchmarking studies consolidating the state-of-the-art. Today, quasi-standards such as gradient-boosting models paired with SHAP explainers have emerged, yet continuous improvement of risk models remains a top priority. Concurrently, rapid advancements in AI, most notably large language models, have disrupted predictive modeling paradigms. Foundation models, pretrained on extensive datasets from diverse domains, have demonstrated remarkable performance by leveraging prior knowledge. While prevalent in natural language processing and computer vision, foundation models for tabular data have only recently emerged. We conjecture that pretraining on out-of-domain data is particularly beneficial in small-data settings, such as SME lending or specialized corporate portfolios, and may help address longstanding challenges including low default portfolios and class imbalance. This paper benchmarks recently proposed tabular foundation models against a broad set of competitors, including established and advanced machine learning techniques, across two core tasks: PD and LGD modeling. Our evaluation encompasses various datasets, performance indicators, and experimental conditions. We find that tabular foundation models generally perform best across datasets and tasks. Moreover, they offer significant improvement in predictive performance as dataset size shrinks. These results are remarkable given that the models are tested out-of-the-box, without hyperparameter tuning, ensuring ease of use and mitigating computational costs.