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.
Predicting the Outcome of Disputes: What Claims Leaders Need
Setting initial reserves based on gut instinct and a quick skim of a file is a liability. In an era of social inflation and litigation funding, claims leaders need calibrated forecasts, not wait-and-see guesswork.
Ingesting Thousands of Pages Per Claim Without Losing Signal
A claim file is a chaotic data swamp of pleadings, medical records, and emails. Extracting the structural reality of a case from this mess requires treating ingestion as an engineering discipline, not a generic text-parsing task.
Conformal Prediction for Claims: Ranges, Not Point Guesses
A machine learning model that predicts a precise settlement dollar amount for a casualty claim is lying to you. Litigation is probabilistic, and your forecasting models must mathematically respect that reality.
Why Day-One Reserves Are Systematically Wrong
Setting an initial reserve is an exercise in institutional guesswork. Adjusters are forced to pick a number before the facts materialize, creating a compounding cycle of misallocated defense spend and missed negotiation leverage.
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