As a player in the AI industry, we are firm believers in the value it can deliver — when integrated correctly into analytics pipelines at insurance firms.
You can't trust chatbots to generate reliable predictions. But you can leverage generative AI to make large, complex, and intricate legal documents accessible to purpose-built predictive algorithms. That's exactly what we do.
And the value goes beyond a rough estimate of possible damages. We've built mathematical modeling into the entire dispute-prediction pipeline, so every prediction ships with a calibrated error measurement — not just a single number, but a set of analytics you can reason with.
This makes Canotera a building block in the decision chain at every stage: when taking on a case, throughout its evolving life cycle, and all the way to closure.
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Related articles.
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.
Geometric Machine Learning on Resolved Cases
Large language models are word guessers, not calculators. To predict the financial outcome of a lawsuit, you must separate the extraction of text from the mathematics of risk.
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.
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