Let me try to rewrite what you are saying in-line with my opinion, hopefully without any loss in translation. You are saying that statistics precision is dependent on model quality, and a better model yields a precision good enough to be usable.
You are correct. My problem with that approach is twofold:
1) There is no clear definition of "good enough";
2) The balancing mechanism between improving model accuracy and simplifying the model (for predictability/market competitiveness) involves actors with very unbalanced bargaining power (insurance companies vs very small subsets of customers)
If I can state the problem in another way: When you use a very refined statistical model for insurance premiums you are basically creating dynamic pricing for insurance services. Dynamic pricing hurts competition in the market through the development of an exaggerated bargaining power by one side of the market. Consumers would be worse off. What they gain in justice of their pricing (I get lower premiums because I'm a safe driver), they lose in bargaining power (via fragmentation of the consumer market).
