AI for Insurance

How AI Works — Insurance Edition

AI capabilities and limitations explained through insurance examples. Context windows for claims files, policy comparison, submission review, actuarial judgment boundaries, and data privacy for policyholder PII and PHI.

You don't need to build the model — you need to direct it

This module is not a machine learning lecture. You will never need to train a neural network or write a line of code.

What you need is a working mental model — an understanding of what AI can do with your submissions, your claims files, and your policy forms. And equally important, an understanding of where it will confidently produce output that is wrong, incomplete, or dangerously misleading.

Think of AI the way you think about a new hire from a top university with an insurance degree: well-read, fast, and eager — but lacking the judgment that comes from years of reviewing losses, negotiating with brokers, and seeing how claims actually develop over time. They need your direction, your context, and your review.

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Which is the best analogy for how you should think about AI in your insurance practice?