Several opposing forces:
--folks have plenty of fender benders these days everyone being in a rush with crowded roads, cell phones ringing, texts arriving and appointments that need to be made in addition to other activities while rushing on the roads.
--After the fender bender, they want their car to be returned to pristine condition, perfectly matched paint with no distortions in the metal when looked at under sunlight, very expensive to repair
--People desire cars with all kinds of functionality that rely up many sensors, electrics and computers to control it all, again expensive to repair.
Our automobile insurance premiums need to be high enough in aggregate to pay for all of these very expensive repairs. It doesn't take all that bad of an accident to total a car these days. And insurance companies do not have a perfect crystal ball that allows them to accurately predict who will be having these costly accidents. License points, age, credit score, miles driven annually, telematics, etc. can be helpful in predicting who is more likely to have these accidents, but it is still a toss at a dart board to some degree. Insurance companies use the information they have and rely on the law of averages over many policyholders.
--folks have plenty of fender benders these days everyone being in a rush with crowded roads, cell phones ringing, texts arriving and appointments that need to be made in addition to other activities while rushing on the roads.
--After the fender bender, they want their car to be returned to pristine condition, perfectly matched paint with no distortions in the metal when looked at under sunlight, very expensive to repair
--People desire cars with all kinds of functionality that rely up many sensors, electrics and computers to control it all, again expensive to repair.
Our automobile insurance premiums need to be high enough in aggregate to pay for all of these very expensive repairs. It doesn't take all that bad of an accident to total a car these days. And insurance companies do not have a perfect crystal ball that allows them to accurately predict who will be having these costly accidents. License points, age, credit score, miles driven annually, telematics, etc. can be helpful in predicting who is more likely to have these accidents, but it is still a toss at a dart board to some degree. Insurance companies use the information they have and rely on the law of averages over many policyholders.