Nov 16 2021
Data Analytics

How Artificial Intelligence Could Revolutionize the Insurance Industry

Insurers are prepping for the era of usage-based insurance.

Insurance has traditionally operated on generalities and assumptions about people and behavior to determine coverage for customers. Young people are more likely to get in car accidents than more experienced drivers and pay higher premiums for their car insurance, even though that generalization fails to hold for every individual.

The introduction of usage-based insurance, however, has created an effective means to provide the right insurance for the right price — based on specifics, not assumptions. Now, UBI is about to gain a power-up through the robust computation capabilities of artificial intelligence. Here’s a look at how AI-driven UBI might impact the industry.

What Is Usage-Based Insurance?

“UBI is like a traditional insurance product with one particular difference: It’s tied to something that’s measured on a recurring basis, which then informs how the premium is adjusted,” says Doug McElhaney, a partner at McKinsey.

It’s been an option from auto insurers for some time, using in-vehicle telematics to monitor miles traveled and driving behavior. However, advancing technology is pushing UBI into new areas; for example, water leak sensors and thermostats informing homeowner’s insurance rates, or weather conditions and load types informing commercial trucking insurance.

AI has become a critical component of the insurance industry’s future because of its ability to make sense of the data the increased use of UBI will produce.

“To properly harness the overwhelming amount of data being produced by UBI and other Internet of Things solutions that capture information multiple times per second, AI is going to be critical for analyzing huge data sets and looking for correlations and anomalies to help process in scale,” says Nitin Mittal, an expert in AI at Deloitte.

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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.”

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