As with many lines of work, the cool part may be a very small part of their day.

With insurance, there's probably an already entrenched way to think about the data that's been there for decades, as well as regulations that help to entrench it.

It might also be the case that the business problem isn't risk analysis at all, as one might expect. It may be finding investments for the float that provide a sensible return within the regulatory remit is harder than figuring out how much needs to be charged for dinging someone's car.

Correct on all points. There is also the issue that the pricing models themselves need to be able to stand up to some form of regulatory scrutiny (depending on the state and the nature of the insurance product).

That's a really good point. Discovering patterns and correlations is an actuary's only competency.

Which may mean that all the statistical techniques ML would use are fairly rudimentary for them.