Is TabPFN the Silver Bullet for Insurance Pricing?
This study empirically tested TabPFN, a tabular foundation model, for motor insurance pricing against established methods like GLM and XGBoost using French and Belgian datasets. Findings show TabPFN does not consistently outperform baselines, suffers from slow inference times that grow with context size, and has high variance. The conclusion is that it is not yet suitable as a replacement for core pricing tasks.