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Comment by mysterydip | Hacker News Reader

Not to marginalize your sister's job, but couldn't that essentially be training for an "AI system" (NN or otherwise), and over time it will reach similar conclusions with increasing accuracy? Maybe they're doing that at her work already behind the scenes.

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An AI system could probably get close, but the act of capturing all the variables might cost the insurance companies more then the current system, which involves a lot of 'gut feelings' about what is important or not for a policy.

There is also licensing involved, in my sister's case she had to earn a CPCU before she could do here job on her own.

Thanks for the info, and yeah licensing was something I hadn't even considered but could be a big liability for a company relying on machines (side note: wonder how the self driving cars handle this?)

>which involves a lot of 'gut feelings' about what is important or not for a policy.

Edit: I see you answered this same question below. Whoops.

Can you give some examples?

I thought it's pretty well understood that the "gut feelings" of experts have been and will continue to be outperformed by algorithms for these sorts of tasks. My imagination is failing trying to come up with something data-based that an agent would see and a computer couldn't.

You fundamentally overestimate the current and near future capabilities of machine learning.

My naive view from being a customer of various insurance companies is that there's a series of lookup tables and charts that does things like "male age 18-25, no history of smoking, here's your rate", with some room for variance based on other factors (some linear or log scale for family history or blood test results, for example). On the surface, those all seem like logical computer functions to do, and as an extension things that would work well training a NN off of after starting with the basic lookup tables. Can you go into more detail on what I'm overestimating?

That's the most trivial form of term life insurance that you're referring to.

What table would you consult to come up with a rate for E&O and liability insurance for the CEO of Uber? Remember your goal is to make money.

That's a great point. Even if it was say 99% accurate, that 1% would likely be the outliers that make or cost the most.

Agreed that 1% could cost or make the most... now do you trust a human or computer to make the final decision? My guess: Humans will end up costing more.

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