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.
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.
Calibrated Reserves: A Better Day-One Number
Setting the initial reserve is often an exercise in defensive guessing. The alternative is a calibrated range that reflects the actual distribution of risk from day one.
Fitting Into an Existing Claims Workflow
Claims adjusters live in their core systems. Forecasting litigation risk requires an architecture that operates entirely in the background and delivers calibrated answers exactly where the work actually happens.
Why Traceability Beats Accuracy Alone
A model that spits out a perfect prediction with zero explanation is a liability in a high-stakes claim. Trust requires knowing exactly which medical record or pleading drove the math.
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