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By rethinking predictive analytic models now, insurers can become more resilient to not just the immediate challenge of COVID-19, but also to the much longer-tail impact of climate change and other unforeseen risk-driving events. (Credit: Rymden/Shutterstock.com) By rethinking predictive analytic models now, insurers can become more resilient to not just the immediate challenge of COVID-19, but also to the much longer-tail impact of climate change and other unforeseen risk-driving events. (Credit: Rymden/Shutterstock.com)

No big data sets, predictive analytics or machine learning could have predicted the current state of affairs. COVID-19, of course, captures the imagination. This makes it easy to forget the enormous insured losses from natural disasters this year, as well. So with the world in such a state of flux, what does this mean for predictive analytics used by insurers?

In the pre-pandemic world, insurers were making huge investments in their predictive analytics capabilities. For example, a report by Willis Towers Watson last year found that 70% of life insurers surveyed in North America were deploying predictive analytics.

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