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.
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.
How Predictive Analytics Changes the Claims Operating Model
Claims departments are losing millions to delayed escalation and reactive reserving. Changing the operating model requires moving from instinct to calibrated, case-level forecasting.
Calibration Monitoring in Production
A single point prediction for an insurance settlement is a lie. We monitor the boundaries of our predictions, ensuring a 30 percent probability of nuclear escalation means exactly 30 out of 100 cases will explode.
Benchmarking Litigation Outcome Prediction
A point prediction for a complex liability claim is mathematically meaningless. True litigation forecasting requires separating the extraction of text from the calculation of risk, delivering calibrated ranges rather than brittle guesses.
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