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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
Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting
A research paper introduces an experimental multi-agent AI system for straight-through underwriting of small commercial insurance policies, claiming superior performance in complex, information-scarce scenarios compared to single-LLM and naive RAG baselines.
Jul 10, 2026