How Insurers Can Monetize Big Data

For communities caught in the path of a ferocious hailstorm, the damage can be substantial.

Now imagine if you owned or worked at an insurance company and you had hundreds, or even thousands, of potential customers in the storm’s path. Suppose you had access to big data showing how many of those people used one of the popular weather apps?

That’s a huge opportunity to connect with potential customers as they deal with the storm emergency. It’s just one of many possibilities for big data use and that gets insurance people excited.

“The take on that (hail) warning by people who have an app is enormous. It’s huge,” said Paul Zikopoulos, vice president of big data analytics for IBM. “And the cost is double-digit millions in terms of (potential) claims.”

“There are enormous benefits for insurance,” he added.

Zikopoulos is one of the leading big-data experts in the field, and he speaks on the topic around the world. He will speak today to close out the three-day Insured Retirement Institute’s Marketing Summit 2016 in West Palm Beach, Fla.

Everybody Has a Cell Phone

His weather app scenario is an example of using mobilization data to your advantage, Zikopoulos explained. In layman’s terms, everyone has a cell phone, so use the technology to make connections.

He named three additional areas in which insurance carriers can embrace big data:

• This one is simple: The two biggest events that prompt the purchase of life insurance are the birth of a child and the death of parents. Both events are often widely shared on social media.

While this might seem like a dicey area to make contact, Zikopoulos said that concern isn’t so warranted.

“It’s very big brother for me to tell you ‘Well, you just announced you had a kid on Instagram and now I’m going to proactively reach out to you to make sure your insurance is up to date,’” Zikoplouos said. “That feels weird. Yet that information is all getting shared for free.”

• Big data can help insurance companies with catastrophic modeling so they can hedge their risk more accurately.

“I work with a lot of insurance companies that would have no idea how a storm with winds between 30 and 90 mph could have an impact on claims,” Zikopoulos said. “So how do I go and simulate those types of claims?”

• Fraud Detection. Big data can help insurance companies move away from the “pay and chase” model.

“We do some due diligence upfront, pay you and then try and figure out if you fraud it,” Zikopoulos said.

Big data has the potential to tighten the noose on fraud from start to finish and make it less of a frenetic pursuit prone to a heavy percentage of failure, he explained.

Usage-based insurance is an idea that is growing beyond pilot programs in several areas. Big data likely will be the driving force behind its spread across the insurance industry in the very near future, said Zikopoulos, who lives in Ontario.

“Insurance companies should get ready to get disrupted by pay-per-use insurance,” he said. “It could be daily. It could be by kilometers. It could be hourly.”

The only remaining refinement needed is “how do you get big data out of a little side project all the way to a culture that permeates every line of business within the company?” he asked.

How Do You Get Started?

The primary obstacle for carriers is not an aversion to big data, quite the opposite. Everyone has eagerly embraced the concept and has a big-data strategy, Zikopoulos said.

“The problem in a lot of places is they jumped on the hype word of big data and they became science projects instead of real things,” he said.

Starting simple, with the data you already have, is step one for insurance companies.

“My insurance company here in Canada … could know so much more about me than they already know,” Zikopoulos said. “They could leverage their relationship with the broker to know even more. They could pull in information when I go to their website and I click and I go and research stuff on what I’m looking for to try and tailor a new offer to me or to try and understand what my needs are.”

Cross referencing that personal data with “open data,” such as garbage collection data, crime data, weather data and economic growth activity, can have unlimited usefulness.

For instance, carriers can sharpen the underwriting risk factors for millions of dollars of coverage they issue.

“Some of those could be as simple as, when are people more apt to talk about insurance? Is it a certain time of year, is it certain days?” Zikopoulos said.

The key is to get started with simple steps by using data at hand. Studies show that every dollar invested in analytics returns $8 to $14, Zikopoulos said.

“You don’t step up to the plate and have to win by hitting nonstop home runs,” he said.

InsuranceNewsNet Senior Editor John Hilton has covered business and other beats in more than 20 years of daily journalism. John may be reached at [email protected].

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