Posts on what we actually think.
Essays on conformal prediction, social inflation, and where generative AI helps and where it doesn't. Written by the team. Slowly.
When AI Should Say It Doesn't Know
Language models are built to generate plausible text, not calculate risk. When an AI offers a single dollar figure for a complex claim without quantifying its own doubt, it is not predicting. It is guessing.
From Reactive to Proactive Claims Management
The difference between a reactive claims organization and a proactive one is the trigger for reserving. If you wait for a plaintiff demand or a deposition to understand your exposure, you are already losing the case.
Latency vs Depth: Engineering Real-Time Case Analysis
Processing thousands of unstructured claims documents requires an architectural choice between speed and depth. Building a system that actually informs reserves means accepting that text extraction is slow and math is fast.
Training on Known Outcomes
Large language models are built to talk, not to calculate risk. Relying on them to predict claims outcomes conflates reading comprehension with mathematical forecasting.
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