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.
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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