Kenn Dahl says he has always been a careful driver. The owner of a software company near Seattle, he drives a leased Chevrolet Bolt. He’s never been responsible for an accident.
So Mr. Dahl, 65, was surprised in 2022 when the cost of his car insurance jumped by 21 percent. Quotes from other insurance companies were also high. One insurance agent told him his LexisNexis report was a factor.
LexisNexis is a New York-based global data broker with a “Risk Solutions” division that caters to the auto insurance industry and has traditionally kept tabs on car accidents and tickets. Upon Mr. Dahl’s request, LexisNexis sent him a 258-page “consumer disclosure report,” which it must provide per the Fair Credit Reporting Act.
What it contained stunned him: more than 130 pages detailing each time he or his wife had driven the Bolt over the previous six months. It included the dates of 640 trips, their start and end times, the distance driven and an accounting of any speeding, hard braking or sharp accelerations. The only thing it didn’t have is where they had driven the car.
On a Thursday morning in June for example, the car had been driven 7.33 miles in 18 minutes; there had been two rapid accelerations and two incidents of hard braking.
Moving from 64 to 65 also moves you to a different age bracket, I would guess that this is the main reason he saw a general rise on his insurance cost from all the other insurance companies.
True, but the insurance agent told him the spyware report was a factor.
Age buckets are so archaic
I disagree, they’re effective and a reasonably privacy-friendly way of predicting risk. Younger people are generally more aggressive drivers than older people, and older people generally have worse reactions than younger people. It’s one of the strongest indicators for driving behavior before an infraction is recorded.
I don’t like it either, but it’s better imo than using one of those driving meters.
So I’m not against using age, but binning it coarsely is the issue when it can be handled much more granularly.
64-65 is probably a negligible amount of risk increase, but 64-69 is going to be much bigger. Looking at younger ages the effect is more extreme where they’re probably charging late 20’s drivers more because they’re pooled with low 20’s.
Anyway, on average it probably works out the same, but in practice I never bin data where I can avoid it, since you get better information looking at it as a continuous range.
Ah, makes sense. I’m guessing that their data sources bin ages as well, so there could be issues in moving to a continuous range.
I wish the whole thing was more transparent.
I think they totally have the computer power to use an hyper parametric model with each age as own variable. A problem this could had, is that they are not going to be enough older adults to accurately assess the risk of them and the model could end showing that 80yo’s are better drivers than 30yo’s.
You can use regression splines or lowess to locally weight the areas with low data based on what you do know, it keeps your parameter count down but still performs well even at the tails.