Is TabPFN the Silver Bullet for Insurance Pricing?

Title:Is TabPFN the Silver Bullet for Insurance Pricing?

Abstract:Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models (GLMs), 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 preprocessing, fitting, or hyperparameter tuning. This paper presents a first empirical evaluation of TabPFN for motor insurance pricing, benchmarking two versions of TabPFN against GLM and XGBoost on two publicly available motor third-party liability (MTPL) datasets. Our results show that TabPFN does not consistently outperform established baselines: it falls behind the GLM on claim frequency, where its performance depends on the size of the in-context training set, but it is competitive on claim severity, where data are scarcer. Its inference times are substantially longer. Although tabular foundation models are promising, particularly in data-scarce settings, TabPFN is not yet ready to replace established actuarial methods for claim frequency.

Risk Management (q-fin.RM); Machine Learning (cs.LG)

arXiv:2605.22892 [q-fin.RM]

(or arXiv:2605.22892v3 [q-fin.RM] for this version)

https://doi.org/10.48550/arXiv.2605.22892 Focus to learn more arXiv-issued DOI via DataCite

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