Insurance telematics, also called usage-based insurance, increasingly is used to fight crime and mitigate losses, according to a new report from Celent titled “Innovation in Focus: The Great Telematics Experiment.” However, the research and consulting firm says, experts still debate whether people will choose coverage that’s based on tracking-devices recording how they drive.
Celent says insurance telematics has evolved and lists four different models:
- The silent witness model. This form of insurance telematics, in which a tracking device signals a vehicle’s location, has been around longest. It has helped combat theft and lower loss potential by increasing the likelihood of recovering stolen vehicles or offering evidence during legal disputes involving the car.
- The simple measurement model. Measurement is akin to pay-as-you-drive auto insurance programs, in which devices track how far and when a vehicle is driven, which reduces risk by encouraging driving less or driving at off-hours. “This type of policy often suits drivers who need a car but don't drive frequently, ” Celent said.
- The behavior-based model. This model examines how the car is driven, evaluating speed, braking, cornering speed, etc. It often adopts some of the benefits and approaches in the other models but really focuses “on providing feedback to the driver, improving their awareness and driver skill and thus preventing or mitigating losses,” Celent said.
- The context Information model. A newer model of usage-based insurance that Celent said could include information on road surfaces, weather and accident or fraud activity data.
But Celent said there are two competing views among auto insurance experts as to whether customers will adopt insurance telematics.
One camp believes in the “null hypothesis,” which holds that current policies are adequate and that telematics programs appeal to just a small niche of drivers. They say new drivers or those with poor histories endeavoring to demonstrate that they are good risks could use telematics-based car insurance as a first rung on the ladder. “Here, we assume that customers don’t want insurers spying on their movements, that capturing the data is expensive, and that the data doesn’t provide significant advantages in pricing and underwriting,” Celent says.
The other camp believes in the “self-selection hypothesis,” in which good drivers will flock to cheaper telematics-based programs and see their premiums lowered. Bad drivers, not be eligible for discounts, will stick with classic policies. As a result, classic policies will become less profitable over time as they include more bad drivers than good.
If the self-selection model begins to prevail, Celent says it should be visible in key performance indicators signs would include increasing numbers of poor drivers on the books, decreasing numbers of good drivers and the need to increase premiums.
Celent also said that that the adoption rates of telematics policies will vary across markets.
See also: 58% of Americans Unsure What Pay-As-You-Drive Insurance Is
