The successful combination of UBI and AI will benefit both insurance companies and customers. For insurers, AI’s valuation capabilities will translate into more effective predictions of risk levels, allowing premiums to be adjusted accordingly.
For customers, the more accurate measurement of driving data and behavior by AI can yield financial rewards. âThere is a very significant correlation between good driving and lower accident risk and therefore lower premiums,â says McElhaney.
AI can determine precisely how far a person drives to inform a fairer premium. âThis would allow insurance customers to buy the exact insurance they need and pay exactly the right price,â says Mittal.
There could also be incentives for poor drivers, if AI and UBI can be combined with user-friendly smart devices. âThe next evolution is to create a feedback loop so that, if you’re not a good driver, you get a recommendation like, ‘You tend to brake pretty hard at stop signs. If you anticipated them a little more, you would lower your risk of an accident and, by the way, lower your premium as well, âexplains McElhaney.
What is holding up the implementation of larger UBI?
The development of UBI and AI on a large scale is promising, but implementation is still ongoing, especially outside of auto insurance. âA lot of people still don’t know how AI is going to end up playing in insurance,â said Paul Carroll, editor of Insurance Thought Leadership.
Several challenges remain. Among them, the question of How? ‘Or’ What insurers should roll out UBI in their products, especially if it doesn’t look like an organic fit. How do you make it work with agricultural insurance, for example, or natural disaster insurance?
There are also regulatory challenges around the sophisticated (and novel) analyzes performed by AI. âRegulators need to understand how you actually calculate premiums, and with machine learning it’s very difficult to break down the calculations so that a regulator can sit down and say, ‘OK, I get it,’â says McElhaney.
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Mittal agrees. Failure to fully understand the technology could result in overly general regulations. âRegulatory constraints could also slow the pace of UBI adoption and innovation, especially for personal insurance and individual coverage,â he says.
As with any technology that collects and observes user data, customer concerns must also be taken into account. âThere are consumers who just don’t like the idea of ââbeing watched,â McElhaney says. They may also simply not care about a new insurance iteration that is technologically advanced enough to enroll.
âAnother hurdle for UBI and AI can be consumer interest,â says Mittal. âUnless UBI’s applications are simple, easy to use and understand, and save consumers money or provide other value to consumers, adoption may not be. as fast as technological development. “
Next steps for widespread adoption of UBI
Despite these challenges, UBI is on a path full of potential. âAs these technologies are adopted more, there is no limit to the innovations that can occur,â although some of these innovations have yet to be realized, says Mittal.
However, the future is not just about innovation. Experts believe the key to customer adoption of AI-powered UBI is to help customers understand its value. âUnlocking is explainability,â McElhaney says.
But explainability is not limited to how it works or how the insured might benefit from it. It is also the revolutionary paradigm shift that the predictive capabilities of AI-powered UBI could produce.
As Carroll says, âYou don’t just pay to keep customers unharmed after some loss occurs, you actually avoid that loss. “