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.
Measuring Calibration: Why We Publish Error Rates
A claim prediction is useless if you do not know how often the model is wrong. Publishing error rates forces a transition from guessing to actual risk management.
Where Defense Spend Is Wasted
The root cause of misallocated defense spend is not exorbitant hourly rates. It is the failure to understand the true exposure of a claim on day one, forcing carriers to fund procedural skirmishes while plaintiffs build damages.
Security and Data Handling for Sensitive Claim Records
Handing thousands of pages of raw medical and legal records to a third-party AI pipeline is a CISO's nightmare. Building a forecasting platform for insurance claims requires treating data as a liability and engineering for pessimism.
Neural-Symbolic Models for Legal Outcome Prediction
Generative AI is a text engine, not a crystal ball. To forecast litigation outcomes accurately, you must fundamentally separate the act of reading a claim file from the mathematics of predicting its cost.
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