Can you trust LLMs to predict the outcome of an insurance claim?
The answer is obviously no — and this isn't a limitation of the technology. It's an intrinsic property.
Language models generate verbal predictions that sound sensible. But they were trained to sound logical, not to be mathematically sound. And they can always hallucinate to support their own reasoning — which means you can never fully trace how they got there.
At Canotera, we draw a hard line. Generative AI ingests the data. For the prediction itself, we use time-tested, theory-backed geometric machine learning models trained on millions of cases with known outcomes — delivering numerical predictions you can trust, paired with traceable reasoning from fine-tuned proprietary models.
LLMs are extraordinary at language. But generation is not prediction. When accuracy matters, you need proper algorithms.
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Large language models are built to talk, not to calculate risk. Relying on them to predict claims outcomes conflates reading comprehension with mathematical forecasting.
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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.
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