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Scaling Laws, Tabular Data and Actuarial Ratemaking Models
A research paper investigates whether deep learning scaling laws apply to actuarial ratemaking using real motor insurance data, finding that data scaling behavior varies significantly across model families — with TabM outperforming Transformers and MLPs — and that architectural design and loss objectives matter more than raw parameter count.
Sep 4, 2026
google/tabfm-1.0.0
Google Research released TabFM, a zero-shot foundation model for tabular data that claims to perform classification and regression without fine-tuning or hyperparameter search by treating training examples as context.
Published Jul 4, 2026 · Analyzed Jul 6, 2026