The evolving ecosystem of connected car insurance, explored by Siegfried Mortkowitz. [Ins.Mortkowitz.2016.09.23]
Motor insurance has been in a continual state of flux since telematics and digital technology created usage-based insurance (UBI). Now, with personal transport and the automobile poised at the threshold of a major disruption, insurers will again be forced to adapt their product and the way they calculate risk.
Equipped with more sensors and autonomous systems, cars will (presumably) be safer and, as they become capable of communicating with other vehicles on the road as well as with a connected infrastructure, or smart grid, new risk profiles will have to be drawn up.
“I can’t see how that wouldn’t affect the risk profile,” says Jim Levendusky, vice-president, telematics, at Verisk Insurance Solutions. “One would think that cars would be getting safer, so the number of claims would drop. It’s not inconceivable that certain cars, depending on their on-board systems, would have a lower risk profile than the cars that lack the systems.”
In the UK, some insurers are already offering premium discounts of up to 10% to drivers of cars equipped with autonomous emergency braking systems (AEBs). However, as Levendusky explains, US drivers will have to wait to receive similar benefits.
“In the UK market pricing is largely unregulated,” he says, “but in the US it’s heavily regulated and on an individual state level, so pricing has to be determined by actuarial evidence. So, in the States, there has to be enough proof. For ADAS there has to be an actuarial basis for that discount, which means we will have to have enough ADAS-equipped vehicles on the road for a certain amount of time with enough corresponding loss experience to show the correlation. So if ADAS, and particularly automatic braking, reduces losses and reduces loss expense, I can easily see insurers lowering their premiums for those cars.”
And as more autonomous systems are added to the car, the more important a factor the vehicle will be in calculating risk, Levendusky says. “I think that what may very well happen over time is the contribution of the driver to the risk profile may decline and the contributions of the vehicle characteristics to the risk exposure will increase.”
Connected cars with vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) capabilities will also change the way insurers look at risk, especially how they regard geography. Levendusky said: “Regarding geography, today insurers look at loss costs associated with location. Roughly speaking, geographic locations with higher densities of cars and road have more opportunities for collision. That’s the implication of geography today but the implications of geography in the future may be the presence or absence of the smart grids being able to support the app.”
Another technological innovation, already available in some cars, is intermodal transport capability. This will impact on another important UBI risk calculation factor, the amount of time a driver spends at the wheel. “Intermodal may lower the usage of the car, which typically lowers the risk profile and exposure,” Levendusky says. “Pay-as-you-drive policies are geared for individuals who live in urban areas and rely mainly on mass transit and use the cars occasionally. There are already insurance products tailored for that segment of the market.”
Intermodal transport is just one way technological innovations and changing consumer attitudes to car ownership are transforming how people travel and, therefore, how risk is calculated. Car- and ride-sharing are rapidly growing in popularity, particularly among the so-called Millennials, and may require new actuarial models.
According to Jared Smollik, actuarial director, personal auto/umbrella product development at ISO Solutions, “Rating for ride- and car-sharing may be incorporated into rating in a variety of ways, and insurers are already providing coverage and rating for these risks. In general, we can look to commercial auto insurance for examples of analogous coverage and then adapt the rating as appropriate for private passenger risks. Over time, experience for these risks will help to determine whether any adjustments need to be made.”
Services that provide car-sharing programmes, where private passenger vehicles are shared by multiple drivers, are similar to car rental services, he explains, while ride-sharing services are similar to taxi services. “Commercial auto rating for both of these are long-established, so one way to incorporate these activities into rating is to determine how much of a vehicle's use is for car- or ride-sharing and adjust rating accordingly.”
Vehicle use would be determined by tracking the number of miles driven during these activities with a smartphone app or by keeping odometer records. “Then these can be expressed as a percentage of the total miles driven by the vehicle during the policy period,” Smollik says. “Rating may then be a hybrid of the appropriate methodologies based on these proportions and the amount of coverage being provided during each activity.”
One unexpected development of the enormous amount of data generated by connectivity is the development of a motor insurance product that could give traditional motor insurance a modern upgrade. The idea, according to Duncan Anderson, managing director and global leader, risk consulting and software, at Willis Towers Watson, is to offer a motor insurance product based on ambient location data, available from many sources, and which is collected “even before the driver has thought about buying an insurance policy”.
This ambient, or passive, location data is available to many organisations through their digital networks, Anderson explains. “You have an increasing number of motor manufacturers with vehicles with tracking devices in them. Many have telematics devices built into the car. You have telecommunications companies who know fairly accurately from the mast triangulation where you’ve been. And you have companies like Apple and Google who know an awful lot from the operating systems on their mobile phones where you’ve been. And also any app provider, if the app has the relevant permissions, can be harvesting information about where the user has been.”
From a driver’s location, Anderson explains: “You can assess how much they drive, where they drive, time of day, types of roads, etc. This can be quite granular and is a fair chunk of what is needed to understand risk and you would have the traditional rating factors as well. But you would not have detailed driving characteristics, such as hard braking, or turning, or acceleration. It’s not as accurate [as real-time UBI data] but it’s still pretty powerful at distinguishing a profitable policyholder from an unprofitable policyholder.”
This data, Anderson maintains, could place these organisations, whether carmaker, telecom or app provider, in a powerful position to assess risk before a policy is sold. “They could sell this data to an insurance company or, more interestingly, if they have a strong consumer brand, they could start distributing insurance to their own customers,” he says.
Carmakers would be in a particularly strong position because of the streams of data their connected models produce. Consumers interested in purchasing a motor insurance policy would not necessarily be buying a UBI policy. “So you could see a world where a motor manufacturer identifies the top 20% of their customers and says, ‘Dear customer, would you like a 30% discount from these insurance companies?’” Anderson says, noting: “If you’re being offered the option of getting a big discount for doing nothing and having a traditional policy, then a lot of people who couldn’t be bothered about having a telematics policy might just be interested in having a big discount.”
The biggest obstacle to the success of this product is potentially negative consumer reaction to being informed that these various businesses have been collecting data on where they have been and have held on to it. Anderson admits this is a substantial barrier, particularly since most of these companies have brands to protect.
“The owners of this data are not primarily interested in selling insurance,” he says. “Their interest is in selling mobile phones or cars. The thought of anything spooking customers away is extremely concerning to them. So they will tread very carefully.”