Assuming that insurance is correctly priced, buying a policy in any form is negative expected value. The companies that write such policies would slowly bleed dry if that wasn't the case. However, that's irrelevant unless you have the bankroll to withstand tail risk. Given that most people don't, as a society, you pretty much have to make things like auto liability insurance compulsory.

If you're a "good driver," as defined by a lack of tickets and accidents, you get discounts. There's really no way to tell if you're _actually_ a "good driver" because "good driver" is a hidden variable, and the only observables from an insurance standpoint are tickets or accidents. I know horrible drivers who never have accidents and avoid tickets via luck or skill. Conversely, you can be a good driver who's unlucky enough to have multiple accidents in a short period of time. Outcomes = baseline truth + random noise.

Most corporate data grabs bad news, but I'm ok with it when it comes to pricing insurance (outside of health). If I run a business that has rock solid, well enforced policies in place to mitigate risk, I don't want the insurance company's priors over my company's sector driving the premium. I only want to pay for coverage on idiosyncratic risk - the risk that remains after addressing everything that I can. Same goes for driving habits. Things like health are almost entirely idiosyncratic risk (ergo, let's agree that doing this for health is a bad thing), but when that's not the case pricing policies using true states (the baseline) and not observables (baseline + noise) puts you as the policy buyer more in control, not less.

That said, I have major concerns over the implementation. A breach is all but inevitable, so there needs to be a means of updating models online, and rules against data retention. It's also bad news if flawed models or corner cases incorrectly price someone out of a policy and there's no sanity check. Unfortunately, that's not too different from the actuarial status quo.