Accelerated underwriting analysis shows increased outreach to Black, Hispanic life insurance applicants

Life expectancy in the U.S. declined in 2020 and 2021, and that decline posed challenges for the life insurance market. The COVID-19 pandemic disrupted traditional agent-consumer interactions, making it necessary for the industry to adopt digital solutions such as accelerated underwriting more quickly.

Data scientists from LexisNexis Risk Solutions examined mortality trends before and after the pandemic, especially as those trends pertained to underserved populations. The findings were presented during the Society for Insurance Research’s Annual Conference this week in Cincinnati.

The pandemic “led to carriers working with their agents in the entire transaction workflow of insurance, to make that experience better and quicker for the consumer,” said Matthew Stull, LexisNexis director of data science.

LexisNexis analyzed a life database of 50 million applicants – with a focus on the combination of medical and nonmedical underwriting data. From there, they developed a Risk Classifier that merges an insurance applicant’s medical information and nonmedical information to allow life insurers to more accurately rate risk, help competitively price policies and offer a better customer experience without the need for an in-person medical exam.

As an example of combining medical and nonmedical information, Stull provided a hypothetical life insurance applicant who has hypertension and holds a professional license.

“We crossed two variables that you might see in an underwriting life insurance application:  hypertension and the fact that someone has a professional license. And when I say professional license, I don't mean a doctor or a lawyer. It can also mean a licensed plumber, a licensed carpenter license, a licensed manicurist – we treat all of them the same. And what we see is that someone may have hypertension but if they’re showing responsibility in other areas of their life, it helps them manage the hypertension. And it improves the mortality risk.”

Helping close the life insurance gap

The purpose for developing these risk models is for carriers to have an easier time reaching the middle market and help close the life insurance gap, Stull said.

The Risk Classifier assigns individuals a risk score showing those who are most likely to be approved for a life insurance policy. The better the mortality the score, the more likely the applicant will be approved.   Then, LexisNexis Risk Solutions used Bayesian Improved Surname and Geocoding to estimate the race or ethnicity of individuals within this study population.   Using the Risk Classifier, the percentage of the good-scoring population that is classified as Black has increased by an average of 53%, and the percentage of good-scoring population that is classified as Hispanic has increased by 38% since 2019, according to a LexisNexis analysis. Stull said those high percentage increases were the biggest surprise resulting from the research.

“We’re showing the benefit this Risk Classifier has and how it benefits the consumer – they get a price quicker, the quote goes faster,” Stull said. “But the bigger benefit that we see is with the outreach to historically underserved communities.”

Susan Rupe is managing editor for InsuranceNewsNet. She formerly served as communications director for an insurance agents' association and was an award-winning newspaper reporter and editor. Contact her at Susan.Rupe@innfeedback.com. Follow her on Twitter @INNsusan.

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Susan Rupe is editor in chief, magazine, for InsuranceNewsNet. She formerly served as communications director for an insurance agents' association and was an award-winning newspaper reporter and editor. Contact her at susan.rupe@innfeedback.com.