As developers of these systems, we need to be careful of how we might apply superficial correlations like these, so that we don't cause harm and burden to those who happen to be caught up in them through no fault of their own.
As a side note, I happen to have a 10+ year old Hotmail account that I use for signing up to services. My Gmail address is only given out to real people. Personally, I view it as a testament to my diligence that I have managed to give my email out to hundreds of websites, several of which have had database breaches, and still only see one or two unwanted emails per week.
Of course insurance companies are first and foremost trying to maximize their profits by charging everyone just slightly more than their expected payouts . That also means that their profits go up when they get better at modeling someone's risk profile and then charge them more. The whole business model of insurance depends on treating people differently, even if they are different due to no fault of their own.
 corollary: if you have enough money, you shouldn't buy insurance (expected loss), but insurance companies (expected profit)
The Expected loss with insurance is higher than without for two reasons:
* The insurance company must make a profit
* You are much more likely to go bankrupt than the insurance company. Bankruptcy limits the payout.
The only time you should take insurance is when you know something the insurance company doesn't. For example, you know how very dangerous your house electrics are, while they don't. Take that thinking too far and it's called "insurance fraud" though.
Or those from a certain zip code who are charged more? 
Or those who drive a certain car model who are charged more? 
How is email address any different?
Well, for one, correlation != causation. Period.