Global Insurance Risks Start With The Good, Bad And Ugly Of Big Data

DES MOINES, Iowa -- Coming out of the COVID-19 pandemic is presenting a crossroads of sorts for insurers: continue with aggressive integration of digital and big data usage? Or retreat to a conservative, risk-averse mindset?

That dilemma of sorts provided gist for much of the debate this morning at the Global Insurance Symposium. On the one hand, the opportunities to reshape the industry are enormous.

For example, Discovery Limited, a South African-founded financial services company, is achieving great success using technology to connect with clients on mental and physical health goals.

"They created a way to engage policyholders to become healthier. They are rewarding them for doing the right things," said Matteo Carbone, founder and director of the IoT Insurance Observatory.

The John Hancock Vitality Program, first launched in 2015, is a similar effort. Customers can earn savings of up to 15% on premiums and valuable rewards for the everyday things they do to stay healthy, like exercising, eating well and getting regular checkups.

But there is still some hesitancy on the data methods insurers are using to do contact-free underwriting.

Big Data Dangers

The pandemic shifted the focus to technology and big data usage. Insurers loosened restrictions on accelerated underwriting, electronic signatures and medical exams. Social distancing and lockdowns required it if insurers wanted to do any businesses.

And insurers found out that the sky did not cave in on them. In fact, policies were sold with stable underwriting. However, getting to that comfort level required a hefty reliance on third-party information and big data. It wasn't always good, said Gina Guzman, vice president and chief medical director at Munich Re US.

"The amount of medical literature that was produced with COVID was overwhelming," she said. "We saw a lot of research papers that were being produced ahead of print, where they weren't fully vetted ahead of when they were put out. So they weren't peer reviewed, which is the normal process for medical literature."

When it comes to big data algorithms and artificial intelligence, regulators are concerned.

There are numerous examples of how big data algorithms discriminate against communities of color. For example, a 2018 study by Consumer Reports and ProPublica found disparities in auto insurance prices between minority and white neighborhoods that could not be explained by risk alone.

Unintentional Discrimination

ProPublica and Consumer Reports examined auto insurance premiums and payouts in California, Illinois, Texas and Missouri, and found that insurers were charging premiums that were on average 30% higher in ZIP codes where most residents are minorities than in whiter neighborhoods with similar accident costs.

Courts have consistently ruled that “redlining” based on race is illegal. Some consumer advocates say insurers might not even know of many cases of “unintentional” discrimination if a computer algorithm is producing it.

"More data doesn't necessarily mean it's good data," Guzman said. "We need to find alternative ways to look at underwriting that are costs cost efficient, and it has to be data that the regulators will see as relevant and non-discriminatory."

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]. Follow him on Twitter @INNJohnH.

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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]. Follow him on Twitter @INNJohnH.