How machine learning makes skydiving insurance more palatable

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Is it feasible for insurers to offer skydiving insurance as a targeted, at-the-moment product for more adventurous policyholders? Underwriters’ heads may spin at the very thought of analyzing the risks in jumping out of airplanes for fun. Yet, it’s possible that through emerging technologies such as artificial intelligence, enhanced through machine learning, such targeted offerings may be a possibility.

That’s one takeaway from a new report from Spiros Margaris, a venture capitalist and thought leader in the digital financial services space. Machine learning, a subset of artificial intelligence, enables applications or algorithms to adapt to new data, essentially reprogramming themselves with little or no human intervention. AI and machine learning “not only have the potential to automate huge amounts of work currently done by humans, they also present new opportunities for engaging and servicing customers,” Margaris states. “Through machine learning algorithms, computers can analyze enormous amount of data, find hidden insights from past experiences, and then use the information to continuously adapt to or prepare for new situations and predict outcomes. It is the machine learning algorithms that make AI-enabled machines more “intelligent” over time by learning from data.”

What’s important to understand here is that machine learning will not replace valuable human talent within insurance organizations, but will supplement and enhance their capabilities to deliver products and services. In the process, the scope of products and services may extend well beyond the limits of insurance as we’ve known it.

Consider how machine learning could potentially reshape insurers’ offerings, as outlined by Margaris in the report:

Underwriting insurance. Underwriters may have a whole new picture of risk, based on the data analytics machine learning provides. “Machine learning algorithms can digest the huge amount of data that is involved in pricing a policy, setting prices more objectively and rapidly than humans ever could,” Margaris says. “Insurers need to be able to customize insurance coverage premiums based on individual customer requirements at the appropriate time. Pay-as-you-go policies will permit companies to insure customers when, for example, they want to become engaged in a sports activity, like parachute jumping, for which they are not yet insured. The insurance gap for people that are not insured but would like to be for a specific moment or period is a huge opportunity.”

Claims and fraud: Machine learning can make a huge difference in what Margaris calls “two of the most expensive business factors in the insurance sector.” Machine learning can improve claims handling and better detect potential instances of fraud. Not only will claim processes be enhanced through the use of AI and machine learning, insurers will also realize enormous savings by eliminating false claims or fraud. With time and more data being gathered by insurers, client claims will be addressed faster and more efficiently, thereby elevating customer satisfaction.”

Telematics and autonomous cars: Auto insurance will never be the same. First, there is telematics, which needs to leverage AI and machine learning algorithms. Next up are autonomous or self-driving cars, currently being piloted, may become commonplace in the near future, and machine learning will play multiple roles in enabling their development and use. For starters, these vehicles “need cameras, sensors, and computing power to gather and transmit a huge amount of data in order to provide a safe ride,” Margaris states. This data will be valuable to insurers in assessing risks and appropriate products or services. While rising adoption diminish the need for accident insurance (hopefully), there will be new types of opportunities for the property & casualty sector for new types of policies to insure this new mode of transportation.

Life insurance. Data flowing in from wearables and other devices will play a role in life insurance policy design. ‘The data amassed from these smart devices can be employed by insurers, for instance, to reward wearers of the devices with lower insurance premiums and entice others to live a better life style so they can also take advantage,” says Margaris.

Insurance is a people-intensive business, and will always remain so. But AI and machine learning may help provide better and far clearer pictures of potential risk, based on the continuous experiences of the industry. In the end, it means greater responsiveness and responsibility to customers, and that’s always a good thing.

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Artificial intelligence Machine learning Underwriting