The successful pairing of UBI and AI will be something that benefits both insurance companies and customers. For insurers, AI’s assessment capabilities will result in more efficient predictions about levels of risk, allowing for premiums to be adjusted accordingly.
For customers, AI’s more precise measurement of driving data and behavior can produce financial rewards. “There’s a very significant correlation between good driving and lower risk of accident and, therefore, lower premiums,” says McElhaney.
AI can determine precisely how well someone drives to inform a fairer premium. “This would enable insurance customers to buy the exact insurance they need and pay exactly the right price,” says Mittal.
There could be incentives for poor drivers too, if AI and UBI can be combined with user-friendly smart devices. “The next evolution of this is creating a feedback loop so that, if you’re not that good of a driver, you get a recommendation like, ‘You tend to brake pretty hard at stop signs. If you anticipated them a bit more, you would lower your risk of an accident and — by the way — lower your premium as well,’” says McElhaney.
What’s Holding Up Wider UBI Implementation?
There’s promise for developing UBI and AI at scale, but implementation is still a work in progress, especially outside of auto insurance. “A lot of people are still kind of unsure how AI is going to wind up playing out in insurance,” says Paul Carroll, editor in chief of Insurance Thought Leadership.
There remain several challenges. Among them is the question of how insurers should roll out UBI in their products — especially if it doesn’t seem like an organic fit. How do you make it work with farm insurance, for example, or natural disaster insurance?
There are also regulatory challenges around the sophisticated (and unseen) analyses AI makes. “Regulators have to understand how you are actually calculating premiums, and with machine learning, it’s very hard to decompose the calculations so that a regulator can sit down and say, ‘OK, I understand this,’” says McElhaney.
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Mittal agrees. An inability to fully understand the technology could result in overly broad regulations. “Regulatory constraints could also slow the pace of UBI adoption and innovation, particularly for personal lines and individual coverages,” he says.
As with any technology that collects and observes user data, there are customer concerns to consider too. “There are consumers who really just don’t like the idea of being surveilled,” says McElhaney. They may also simply not care about a new, technologically advanced iteration of insurance enough to opt in.
“Another barrier for UBI and AI may be consumer interest,” says Mittal. “Unless applications of UBI are simple, easy to use and understand, and save the consumer money or provide other value to consumers, adoption may not be as quick as technological development.”
Next Steps for Widespread Adoption of UBI
Despite those challenges, UBI is on a path full of potential. “As these technologies are adopted further, there’s no limit to the innovations that may arise,” even if some of those innovations have yet to be realized, says Mittal.
The future isn’t just about innovation, however. Experts believe the key to customer adoption of AI-powered UBI is helping customers understand its value. “The unlock is explainability,” McElhaney says.
But explainability is about more than just how it works, or how the insured might benefit. It’s also about the game-changing paradigm shift the predictive abilities of AI-powered UBI could yield.
As Carroll says: “You’re not just paying to make customers whole after some losses occurred, you’re actually preventing that loss.”
