There are dozens of examples in both personal and commercial lines like boilers, furnaces and other machines outfitted with sensors, connectivity and Internet of Things attributes, but few are as SEO-friendly as driverless cars, so that's what we'll address here.

The one thing all of these examples share is that they create what we call "non-approximated data." If that catches on as a buzzword, remember that you heard it here first.

Determining risk

Today when you write a risk, you need to get their driver's score, the number of points the driver has, the vehicle type, and then you approximate the risk. Actuaries are good at doing this. They have years of experience and wonderful math. But when insurers have access to non-approximated data — data about the actual risk, the actual drivers, what they're doing and where they're actually going when they drive, such as from telematics devices — they have a much clearer picture of what they're insuring, what it should cost, and how profitable those drivers are likely to be. That's a model and a method that increasingly will be applied to other markets as well.