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REVIEW 3 major objections 5 minor 1 cited by

Foundation Models for Credit Risk Prediction: A Game Changer?

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read Pretrained tabular foundation models, used with zero task-specific training, match or beat tuned gradient-boosting and deep-learning rivals on both probability-of-default and loss-given-default benchmarks, and their relative advantage grows

desk verdict Solid first credit-risk benchmark of tabular foundation models; the average-performance claim holds, but the headline small-data advantage is not statistically established. read the letter →

arxiv 2605.18147 v2 pith:CTSB7GAT submitted 2026-05-18 cs.LG

Bart Baesens , Andreas Goethals , Stefan Lessmann , Simon De Vos , Cristián Bravo , David Martens , Victor Medina-Olivares , Christophe Mues
show 5 more authors
Maria Oskarsdóttir Seppe vanden Broucke Tony Van Gestel Tim Verdonck Wouter Verbeke
This is my paper · ORCID
classification cs.LG MSC 68T0768T0591G40
keywords creditriskmodelingprobabilityofdefaultlossgiventabularfoundationmodelsprior-datafittednetworkszero-shotlearningbenchmarkingsmall-data
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether a new family of models—tabular foundation models, pretrained on synthetic data and applied without task-specific training—can beat the tuned machine-learning pipelines that currently set the standard in credit-risk practice. Across 14 probability-of-default datasets and 7 loss-given-default datasets, it finds that these zero-shot models generally rank first: TabICL leads default prediction on average AUC and wins 25.7% of folds, while TabPFNv2 leads loss prediction on R² and wins 45.7% of folds. The paper also claims that these models improve most relative to competitors as datasets get smaller, which matters for SME lending, low-default portfolios, and new products. If true, lenders could deploy strong models faster and with less computational overhead, while keeping a consistent method across PD and LGD.

What carries the argument

The load-bearing mechanism is the prior-data fitted network (PFN): a transformer pretrained on large numbers of synthetic tabular datasets sampled from priors over data-generating processes, so that inference on a new dataset is a single forward pass over labeled and unlabeled rows—no gradient updates, no tuning. The paper also relies on TabPFNv2's regression variant, which discretizes the continuous LGD target into an ordinal piecewise-constant distribution, a design well suited to the zero-one-inflated, bimodal loss distributions in the data. The evaluation machinery is a standardized cross-validation protocol with hyperparameter optimization for all non-PFN baselines, plus PAMA win rates,

What would settle it

A controlled subsampling experiment on one large retail default dataset: take the same portfolio, draw training sets of 500, 1,000, 2,000, 5,000, and 10,000 observations, and compare TabICL/TabPFNv2 against tuned boosting baselines. If the tuned baselines match or exceed the foundation models at small sizes once dimensionality, default rate, and feature semantics are held fixed, the paper's small-data claim is refuted. For LGD, the same test should include at least two or three zero-shot regressors, since the paper evaluates only one.

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Extended reading notes

Core claim

The paper's central claim is empirical: in a five-fold cross-validation benchmark spanning 14 PD and 7 LGD datasets, tabular foundation models generally outperform state-of-the-art alternatives even when used out-of-the-box with no hyperparameter tuning. TabICL achieves the highest average AUC (0.7517) and the best fold-level win rate (25.7%) for PD, with foundation models collectively top in 44.3% of folds; TabPFNv2 achieves the highest average R² (0.4455) and wins 45.7% of folds for LGD. A global non-parametric ranking test rejects the null of equal performance, though pairwise post-hoc significance is limited—especially for LGD, where only seven datasets make multiple-comparison-corrected

Load-bearing premise

The small-data conclusion stands only if dataset size itself, not some correlated property of the small datasets (different loan products, feature counts, or default rates), is what drives the foundation models' relative advantage.

Editorial extensions

If this is right

  • For probability-of-default modeling, a zero-shot model (TabICL) can match or beat tuned gradient boosting on discrimination, so a lender could ship a competitive scorecard without per-portfolio hyperparameter search.
  • For loss-given-default, TabPFNv2 shows that framing regression as discretized ordinal classification can handle the bimodal, zero-one-inflated loss distribution better than tuned tree ensembles and deep regressors on these data.
  • In small-data settings—SME lending, low-default portfolios, new products—the relative advantage of foundation models is largest, suggesting they are most useful where conventional models have the least signal.
  • Operationally, removing tuning and retraining cuts time-to-model and compute, and allows one consistent model family across PD and LGD, which could simplify validation and monitoring.
  • The global statistical test rejects equal performance among the 29 PD / 22 LGD methods, but pairwise significance is scarce; the practical ranking of the top methods should be treated as suggestive rather than definitive per-pair proof.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: because the LGD leg rests on a single foundation model (TabPFNv2), the strongest testable extension is to run additional zero-shot regressors and standard LGD econometric baselines (e.g., two-part and beta models) on the same seven datasets; if the lead survives, the regression-side claim is much firmer.
  • Editorial extension: the paper's own learning curves show TabPFNv2's R² declining as training rows pass about 8,000, with the authors unsure why; a focused study of whether the pretrained prior becomes a liability on larger in-domain samples, and whether ensembling a PFN with a GBM recovers the loss, would sharpen the small-data thesis.
  • Editorial extension: a broader reading is that if zero-shot tabular models keep improving, the default workflow could shift from tuning a GBM per portfolio to validating a foundation model out of the box—but calibration, feature attribution, and stability under economic regime shifts would need dedicated study before that shift is safe.
  • Editorial extension: the small-data result could be tested more cleanly by holding dataset origin constant—subsampling a single large retail portfolio to many sizes and comparing methods—which would separate the size effect from confounds like default rate and dimensionality.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper benchmarks tabular foundation models (TFMs) against a broad panel of classical, tree-based, and deep learning methods for two credit-risk tasks: probability of default (PD) classification and loss given default (LGD) regression. Using 14 PD and 7 LGD datasets, five-fold cross-validation, and a wide range of metrics, it reports that TabICL achieves the highest average AUC and the highest PD fold-level win rate (PAMA 25.7%), while TabPFNv2 achieves the highest average R² and wins 45.7% of LGD folds. Friedman tests reject global equivalence of methods, but only 22/406 PD and 0/231 LGD pairwise comparisons are significant after Holm correction. The paper further claims that TFMs offer particularly large gains in small-data settings, based on Spearman correlations between method rank and dataset size and on learning curves obtained by subsampling large datasets. The conclusion is that TFMs generally outperform state-of-the-art alternatives and are especially attractive for small credit portfolios.

Significance. The study is a broad, mostly self-contained empirical benchmark in an area where independent evidence is scarce. Its strengths include the use of a unified preprocessing/evaluation framework (TALENT), out-of-the-box evaluation of TFMs against tuned baselines, multiple performance metrics, and standard statistical procedures (Friedman, Holm-corrected Wilcoxon, PAMA). If the headline claims held, the paper would be a valuable reference for credit-risk modelers and would lend support to the 'foundation models are a game changer' narrative. As it stands, the two central claims—'generally perform best' and 'significant improvement as dataset size shrinks'—outrun the evidence: pairwise significance is weak, especially for LGD, and the small-data analysis is confounded. With appropriate revisions and more cautious wording, the paper could still make a useful contribution.

major comments (3)
  1. [§5.4, Figs. 8–11; Abstract] The headline claim that TFMs 'offer significant improvement in predictive performance when the size of the dataset ... grows smaller' is not established. The Spearman correlations in Figs. 8–9 are computed across heterogeneous datasets without adjusting for confounds: e.g., small PD datasets (PD8, PD10) have 20/14 features and high default rates, whereas the large PD14 has 2,986 features; among LGD datasets, the small Base Model/Base Modelisation have 202/256 features while the large HELOC has 8. The learning-curve experiment removes between-dataset confounds only by random row subsampling, which preserves feature spaces and default incidence; it does not reproduce the structure of SME/low-default portfolios. Moreover, Fig. 10 appears to compare TFMs mainly against XGBoost and logistic regression, not the strongest GBM baselines named in the text (CatBoost, LightGBM), and Fig. 11 shows a
  2. [§5.3; Tables 4–5; Abstract] The conclusion that TFMs 'generally perform best across datasets and tasks' is based on average metrics and PAMA, but the pairwise statistical evidence is weak: only 22/406 PD comparisons and 0/231 LGD comparisons are significant after Holm correction. PAMA is descriptive, and the Friedman test only establishes that some global differences exist, not that the top method is significantly better than the runner-up. Given that the best TFM and the best GBM differ by about 0.0023 AUC in PD and 0.0054 R² in LGD, the word 'best' is an overstatement. Please report effect sizes/confidence intervals for the top pairs and either temper the claim to 'competitive on average' or provide a more powerful analysis, especially for LGD where no pairwise difference survives correction.
  3. [§5.2, Table 5] The LGD component of the benchmark evaluates only one foundation model (TabPFNv2), yet the paper's abstract and conclusion speak of 'tabular foundation models generally perform best' across tasks. A single model is a thin basis for the 'paradigm shift' claim in regression. The average R² gap is also very small (0.4455 vs. 0.4401 for CatBoost), so the practical significance is unclear without per-dataset breakdowns. Please either add more regression-capable TFMs or restrict the generalizing claim to the particular model evaluated.
minor comments (5)
  1. [Figures 2–7] In the supplied manuscript, these figures appear as unreadable sequences of '/uni...' glyphs. If this is a rendering artifact, the final version must show actual axes, labels, and legends; otherwise the visual evidence cannot be inspected.
  2. [§5.4, Figs. 10–11 captions] The text says the learning curves include CatBoost, FTT, LightGBM, and XGBoost, but the figure captions list only TabICL/TabPFN/Logistic Regression/XGBoost and TabPFNv2/Linear Regression/XGBoost. Please clarify which curves are actually plotted and why the strongest GBM baselines are absent.
  3. [§4.3] The abstract emphasizes that TFMs are used 'out-of-the-box, without hyperparameter tuning', while §4.3 describes hyperparameter optimization for the benchmarked methods. Please clarify explicitly that HPO applies only to non-TFM baselines, so readers do not infer an asymmetric protocol.
  4. [General] No code or data availability statement is included. For a benchmark paper of this scope, a reproducibility appendix or repository link would substantially increase its value.
  5. [References] Several references are duplicated (e.g., Shwartz-Ziv and Armon 2022; Hurlin et al. 2018) and some entries have inconsistent formatting. A careful reference cleanup is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: benchmark results are read directly from held-out metrics against externally pretrained TFMs; the size correlation in §5.4 is correlational, not forced by construction.

full rationale

This paper is an empirical benchmark, not a derivation chain: it computes AUC/R2 of out-of-the-box tabular foundation models (TabPFN, TabPFNv2, TabICL, MITRA, TabPFN-Real) against tuned baselines on 14 PD and 7 LGD datasets under 5-fold CV. The TFMs were pretrained by external groups (Hollmann, Müller, Hutter et al.; Qu et al.), and the baselines are independently tuned via Optuna; none of the present authors' prior work justifies the central comparison. The two headline claims ('tabular foundation models generally perform best across datasets and tasks'; 'they offer significant improvement in predictive performance when the size of the dataset ... grows smaller') are read directly off held-out test metrics (Tables 4–5, Figures 8–11, PAMA analysis), with no fitted parameter that is later renamed a prediction and no equation that reduces the output to an input. The §5.4 small-data claim rests on a Spearman correlation across heterogeneous datasets and on learning curves obtained by row-subsampling; both are descriptive statistics with identifiable confounders (dimensionality, default rate, data origin), so the causal size interpretation is a validity risk, not a circularity. The paper itself flags the fragility of the LGD learning-curve evidence ('It is unclear why TabPFNv2’s performance declines at this point'), and §5.3 honestly reports that no LGD pairwise comparison survives Holm correction. Self-citations (Lessmann et al. 2015, Gunnarsson et al. 2021, Loterman et al. 2012, Óskarsdóttir & Bravo 2021, etc.) are confined to the literature review and one dataset source (PD14); none is load-bearing for the benchmark result, and no uniqueness theorem or ansatz is imported from the authors' own work. The paper even disclaims methodological novelty ('we do not advance deep tabular data learning methodology'). The strongest critique—that the size effect is correlational rather than causal—is an external-validity concern, which belongs under correctness risk, not circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new theoretical entities; its conclusions depend on benchmark design choices and dataset representativeness. The key unstated premise is that rank-correlations across heterogeneous datasets and subsampled learning curves are sufficient evidence for a causal small-data advantage.

free parameters (3)
  • HPO budget = 20 Optuna trials per fold
    Baseline methods are tuned with this budget; TFMs run zero-shot. The comparison is conditional on this chosen tuning budget, which the authors did not vary.
  • F1-optimal decision threshold = per fold, on validation
    Used to convert PD probabilities into labels for threshold metrics; not central to AUC/R² comparisons but affects the reported accuracy/F1/MCC numbers.
  • Learning-curve sample sizes = 500 to 15,000 observations
    Used in §5.4 to demonstrate the small-data advantage; the choice of range and the selected datasets shape that conclusion.
assumptions (5)
  • domain assumption The 14 PD and 7 LGD datasets are representative of credit risk modeling portfolios
    The generalization 'TFMs generally perform best for credit risk' rests on this convenience/proprietary sample of datasets; there is no formal sampling frame.
  • domain assumption Spearman correlation between method rank and dataset size isolates the effect of sample size
    §5.4 does not control for dataset dimensionality, default rate, feature semantics, or data origin, so the observed correlation may be confounded.
  • domain assumption Subsampling large datasets to 500–15,000 rows simulates genuine small-data portfolios
    Figures 10–11 subsample large retail datasets; true SME/low-default portfolios may have different structure, missingness, and distribution shift.
  • domain assumption Standard metrics (AUC, R², MSE, etc.) capture what matters for credit risk decisions
    No profit-based or regulatory-cost measures are used; the authors list EMPC and LGD two-part baselines as future work, so the practical relevance is assumed rather than demonstrated.
  • standard math TALENT framework and Optuna implementations are correct and leak-free
    Preprocessing parameters are learned per training fold (§4.2), but without released code the implementation cannot be independently verified.

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Cite this review

Pith. "Pith review of Foundation Models for Credit Risk Prediction: A Game Changer?." pith.science (2026). https://pith.science/paper/CTSB7GAT

@misc{pith2026260518147,
  author       = {Pith},
  title        = {Pith review of: Foundation Models for Credit Risk Prediction: A Game Changer?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CTSB7GAT}},
  note         = {Machine review of arXiv:2605.18147}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2605.18147 by the authors.

Figure 1
Figure 1. Distribution of the target variable in the LGD datasets. [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. reports the average performance of classification methods in terms of AUC, across the five folds of all PD datasets. A first observation is that TabICL, one of the foundation models considered here, achieves the best performance overall. Although the observed performance differences are small in absolute terms, it is notable that— without any training or hyperparameter optimization—a TFM outperforms widely credited … view at source ↗
Figure 3
Figure 3. Average [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Probability of maximal AUC (PAMA) analysis for PD datasets. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Probability of maximal R 2 (PAMA) analysis for LGD datasets. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Win/Loss ratio matrix for the PD benchmark (N = 14 datasets). Cell text gives the W/L count from the row method’s perspective; cell color encodes the net win rate (W − L)/N - blue: row method wins more often, red: row method loses more often. An asterisk (*) denotes a …
Figure 7
Figure 7. Figure 7: Win/Loss ratio matrix for the LGD benchmark (N = 7 datasets). Cell text gives the W/L count from the row method’s perspective; cell color encodes the net win rate (W − L)/N - blue: row method wins more often, red: row method loses more often. An asterisk (*) denotes a …
Figure 8
Figure 8. Figure 8: Spearman rank correlation between a method’s performance rank and PD dataset size (number of observa [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Spearman rank correlation between a method’s performance rank and LGD dataset size (number of obser [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: PD learning curves: average AUC across PD datasets with more than 15,000 observations as the number [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: LGD learning curves: average R 2 across LGD datasets with more than 15,000 observations as the number of randomly sampled training observations increases from 500 to 15,000. Curves are shown for TabPFNv2, Linear Regression, and XGBoost. 18 [PITH_FULL_IMAGE:figures/fu…

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Is TabPFN the Silver Bullet for Insurance Pricing?

    q-fin.RM 2026-05 unverdicted novelty 6.0 of 10

    TabPFN does not consistently outperform GLM or XGBoost for motor insurance pricing, shows longer inference times, and is sensitive to in-context set size.

Reference graph

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Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.