ML methods applied to insurance pricing — GBMs, neural networks, embedding models, foundation models, and the recurring question of whether the complexity is justified by the lift. 6 articles.
LLMs can extract structured data from surveys, flag non-standard construction in loss adjuster notes, and rate categorical variables at scale. They cannot reliably assess claim ...
Avanzi, Richman, and Wüthrich reformulate individual claims reserving as a Markov Decision Process. We explain why it matters, what it actually does, and when a UK reserving act...
Laub, Pho and Wong's ANAM paper enforces smoothness and monotonicity architecturally, not as penalties. Here is what the mechanism actually is, why it matters more than the benc...
A practical comparison of CatBoost and XGBoost for UK personal lines insurance pricing — categorical handling, Tweedie support, and why we default to CatBoost.
Shared-trunk neural model for frequency-severity dependence in UK motor pricing. Explicit dependence testing where two-part GLMs assume independence - Python.