Writing on prediction, claims, and the science behind the platform
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.
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.
Litigation Funding and the New Math of Claim Exposure
Third-party capital has fundamentally altered the incentives driving bodily injury claims. When the plaintiff's side plays a portfolio game to maximize returns, defense strategies built on historical payout curves become obsolete.
Designing for Traceability: Every Forecast Links to Evidence
A forecast is useless to a claims professional if they cannot defend it. Traceability requires engineering the system to link every predicted outcome directly to the source document that generated it.
Generation Is Not Prediction
Large language models are built to produce plausible text, not accurate forecasts. Confusing a statistical parrot for a mathematical pricing engine is a fast way to misprice your entire claims portfolio.
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.
Inside the Canotera Pipeline: From Case File to Forecast
Processing multi-gigabyte case files requires strict architectural boundaries. We separate the language models that read medicals from the mathematical models that calculate settlement ranges.
Why LLMs Can't Predict Legal Outcomes
A language model generates text that looks like an answer. It does not calculate probabilities based on historical claim geometries. Confusing the two is a fast way to misprice your reserves.
AI as a Building Block in Insurance Analytics
Generative AI makes large, complex legal documents accessible to purpose-built predictive algorithms — when integrated correctly into the analytics pipeline.
Can You Trust LLMs to Predict the Outcome of an Insurance Claim?
Language models generate verbal predictions that sound sensible — but they were trained to sound logical, not to be mathematically sound.
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