Predictive models are central to credit risk management, where the accuracy of default-probability and loss estimates affects the profitability of lending and the stability of the financial system. Decades of benchmarking research have consolidated the state of the art, with gradient-boosting models often representing the strongest machine-learning benchmark, while linear and logistic models remain important regulatory and industry reference models. The recent advent of tabular data foundation models introduces a new paradigm for predictive modeling. Rather than fitting a model to a target dataset, they use in-context learning to integrate information from the target dataset with prior knowledge acquired through large-scale pretraining on diverse collections of (synthetic) tabular data. We conjecture that access to information beyond the target data is beneficial in small-data settings and may help address longstanding challenges, including low-default portfolios. Whether this promise holds in practice remains an open question. This paper benchmarks state-of-the-art tabular foundation models against a broad set of classical and advanced machine learning competitors on two core tasks: Probability of Default (PD) and Loss Given Default (LGD) modeling. We evaluate $33$ classification methods across $14$ PD datasets and $27$ regression methods across $7$ LGD datasets, spanning a range of performance indicators and experimental conditions. The most recent tabular foundation models, in particular TabPFN-3 and TabICLv2, achieve the strongest average results, with the largest gains in small-sample and strongly imbalanced settings. These results are notable because these models are used with their pretrained parameters fixed and without downstream hyperparameter optimization or task-specific parameter updates.

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