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Is TabPFN the Silver Bullet for Insurance Pricing?

Author

Listed:
  • Bruno Deprez
  • Wouter Verbeke
  • Tim Verdonck

Abstract

Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machines as the leading machine learning alternative. Tabular foundation models (TFMs) present a fundamentally different inference paradigm. By pre-training on large collections of synthetic datasets, TFMs enable inference on new data through in-context learning, without any dataset-specific fitting or hyperparameter tuning. This paper presents a first empirical evaluation of TabPFN for motor insurance pricing, benchmarking it against GLM and XGBoost on two publicly available MTPL datasets. Our results show that TabPFN does not consistently outperform established baselines, exhibits substantially longer inference times, and is sensitive to the size of the in-context training set. While tabular foundation models represent a promising direction, particularly in data-scarce settings, their current performance does not offer a viable replacement for established actuarial methods.

Suggested Citation

  • Bruno Deprez & Wouter Verbeke & Tim Verdonck, 2026. "Is TabPFN the Silver Bullet for Insurance Pricing?," Papers 2605.22892, arXiv.org, revised May 2026.
    • Handle: RePEc:arx:papers:2605.22892
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    References listed on IDEAS

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    1. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149.
    2. Denuit, Michel & Hainaut, Donatien & Trufin, Julien, 2020. "Effective Statistical Learning Methods for Actuaries II : Tree-Based Methods and Extensions," LIDAM Reprints ISBA 2020035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Noah Hollmann & Samuel Müller & Lennart Purucker & Arjun Krishnakumar & Max Körfer & Shi Bin Hoo & Robin Tibor Schirrmeister & Frank Hutter, 2025. "Accurate predictions on small data with a tabular foundation model," Nature, Nature, vol. 637(8045), pages 319-326, January.
    4. Freek Holvoet & Katrien Antonio & Roel Henckaerts, 2023. "Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff," Papers 2310.12671, arXiv.org, revised Jan 2025.
    5. Freek Holvoet & Katrien Antonio & Roel Henckaerts, 2025. "Neural Networks for Insurance Pricing with Frequency and Severity Data: A Benchmark Study from Data Preprocessing to Technical Tariff," North American Actuarial Journal, Taylor & Francis Journals, vol. 29(3), pages 519-562, July.
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