Is TabPFN the Silver Bullet for Insurance Pricing?
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 (M...
| Authors | Bruno Deprez, Wouter Verbeke, Tim Verdonck |
|---|---|
| Primary category | q-fin.RM |
| Published | 21 May 2026 |
| Last updated at source | 9 October 2026 |
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