When an LLM Beats a Statistical Model, and When It Doesn't
Take a car insurance pricing model, the kind of tool that's been doing this job since the 1970s, and instead of feeding it a tidy row of numbers, rewrite that same row as a sentence. Driver age, region, vehicle power, bonus-malus score (an insurer's shorthand for a driver's claims history), all of it, poured into a fixed text template that reads like an underwriting checklist. Then run that text through a large language model (LLM), grab the embedding it produces, a single numeric vector standing in for the whole passage, and hand that to the old pricing model in place of the features an actuary would normally hand-build.
That's what two researchers did in a preprint posted in June 2026, not yet peer-reviewed. It worked. The embedding-fed version predicted claim frequency better than the industry-standard model, at least when there wasn't much data to go on. Give it more data,...
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