Insurance companies are in the risk business. Their customers pay them for financial protection against all sorts of accidents and tragedies: hit-and-run car damage, illness and death. Until recently, though, insurers had a hard time precisely determining how much to charge policy-holders, because it was difficult to reliably distinguish between high-risk and low-risk applicants.
Their customers, meanwhile, endured a frustratingly slow application process. It’s not that insurance companies lacked sufficient data with which to build refined risk prediction models; they simply lacked the tools to store and analyze it. And so, for many years, they would analyze subsets of datasets and then essentially cross their fingers. Those days are gone. Thanks to big data analytics, insurers can digitize and mine the information they’ve amassed for decades in order to glean predictive insights.
Author: Mae Rice
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