How an Insurer Predicts Your Client's Future Now

Samantha Chow wants to help life insurers size up their clients better, and faster, when they’re applying for coverage. As the life, annuities and health markets lead at EIS Group, a San Francisco-based software company, she connects the people in charge of underwriting clients with the technology specialists in charge of improving the underwriting process.

Chow, who joined EIS in 2020, has a bachelor’s degree from Columbus State University and a master’s degree in business from the University of Phoenix. She started out as a field automation specialist with Aflac, in 1997. She later held other research and analytical posts at Aflac, The Klages Group, Lebhar-Friedman, New York Life and Aite Group.

We asked Chow how she sees the state of life insurance underwriting now, in the wake of all of the changes brought on by advances in underwriting data sources and technology.

THINKADVISOR: Can an insurer really assess a life insurance applicant’s risk level with “just seven quick questions”?

SAMANTHA CHOW: For a lot of people, the data streams that go into this system come from reflexive questioning. A handful of health questions get broken down into many little health questions. You may start out with seven questions, but those can lead to more, in-depth follow-up questions, and a total of upwards of 50, or even 100, separate questions.

That’s where the value of in-depth reflexive questioning comes in. A reflexive-questioning-based system starts by asking, “Have you ever been diagnosed with cancer?” If you answer “no,” you go to the next question.

If you answer “yes,” the system asks, “How long ago?” and “What kind of cancer?” and you may see more dropdowns.

One question in a dropdown might be, “Did you have to have chemotherapy?” If so, the system asks, “How many times?” and “How often do you get scans?”

That’s where a lot of the data is coming from.

In addition, insurers are now using more third-party data than ever and third-party predictive models, which produce health scores to support automated processing.

What types of outside data streams are flowing in?

Some of the newer carriers are doing true accelerated underwriting, with more third-party data streams — vendors like MIB, Motor Vehicle Records and LexisNexis Risk Solutions — used for identity verification, or prefilling some application sections.

There are all sorts of reinsurers that offer data sources or algorithms. Those algorithms also take into consideration the prescription databases, MIB data and Motor Vehicle Records data, and your Fair Credit Reporting Act, or FCRA data, including data from LexisNexis and other vendors.

Companies like SelectX can help with the reflexive questioning, as well as with algorithms to support the reflexive questioning. You can also have a third-party deal with data elements from reinsurers, like RGAX, Hannover Re or Swiss Re’s Magnum. Milliman has all kinds of data; they can get down to the ZIP Code level and do predictive analytics around that.

So the streams of data coming in are all going to help the insurers evaluate the risk or associate the applicants to a risk classification, if the streams are used correctly.

What kinds of additional data streams are you adding or thinking about adding, and why?

Think about accelerated underwriting versus automated underwriting versus simplified issue versus fully underwritten medical underwriting. Simplified issue is typically a couple of yes/no questions; automated acceptance/automated decline is a bit deeper. It might have more reflexive questioning, but it still results in a yes or no.

Then, outside of automated, you’ve got pure medical underwriting, which may have some reflexive questioning. Pure medical underwriting programs collect blood, urine, body mass index readings, and those kinds of things. You’re going to have a paramedical exam.

And then you have accelerated underwriting, which is where a lot of the data streams come into play.