Swiss Re is using predictive analytics and a team of data and behavioral scientists to close a growing protection gap in the U.S. life insurance market.

The reinsurer estimates the average household is underinsured by $378,000, creating a $20 trillion need for coverage among 70 million families. Its life insurance sales unit, led by SVP and head of new solutions group JJ Carroll, is working to increase product relevance for prospective customers and reduce friction associated with the underwriting process.

Swiss Re headquarters in Zurich, Switzerland
Swiss Re headquarters in Zurich, Switzerland

“The team was really formed to address an opportunity in the protection gap,” Carroll says. “We have embraced reality that new methods of technology are bringing great change.”

Carroll’s team, assembled three years ago, is developing an instant-issue platform that delivers life policies to customers without fluid testing. The predictive modeling software is currently being tested by one of Swiss Re’s clients, she says.

JJ Carroll, Swiss Re
JJ Carroll, Swiss Re

Completed analytics projects include initiatives focused on risk selection, policyholder behavior analysis and fraud detection. Swiss Re also developed an intelligent matching system to recommend core products to customers similar to what they would experience shopping on Amazon.

Each project was finished by Carroll’s team of 10 actuaries, project managers and anthropologists, as well as third-party vendors and Swiss Re’s data scientists available to business units on request. The scientists specialize in machine learning, statistical modeling and natural language processing. “They work on the intersection of tech and business,” Carroll said.

She considers her biggest challenge on the job phrasing business questions in ways that can be solved through analytics; then layering behavioral sciences with the technology to address enterprise and customer concerns.

“We’ve seen huge evolution in tech. In general, data availability is growing,” said Carroll, offering the emergence of biometrics and natural language processing as examples. “We are evaluating different companies that work in these spaces.”

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