They’ve been hearing the call for ages, and have shown characteristic restraint while other industry segments have been taking the big data plunge. Could it be that life insurers are getting ready to sail towards big data’s rocky shoals? According to an April 2016 report from Willis Towers Watson, they are.
Willis Towers Watson recently surveyed North American Life Insurance CFOs about their current and planned use of big data and predictive analytics. Over half of those responding confessed that they “know a little or understand the basics around big data and predictive analytics,” but none of the participants considered themselves to be experts on those topics. Only 8 percent of those polled stated that they are currently using big data and predictive analytics to support decision-making. 62 percent of indicated that they plan to use big data and predictive analytics in the decision-making process across multiple business functions – in two years.
That timeframe reflects an awareness of the many storms life insurers will face as they chart the course for achieving their big data goals. Over 70 percent viewed infrastructure limitations as their biggest obstacle, and 54 percent said that data availability and quality is a significant challenge.
Survey authors Elinor Friedman and Jack Gibson found that 50 percent of respondents plan to use big data and predictive analytics for improving risk selection. Unsurprisingly, a quarter of those surveyed mentioned using predictive analytics to target profitable customers as their biggest goal. Other goals included developing customer loyalty and increasing cross-sell opportunities, as well as improving overall profitability and customer experience. You can download the Willis Towers Watson survey infographic here.
Earlier this year, Jennifer Overhulse turned a critical eye towards insurers professing their readiness to embrace big data. In Eating The Big Data Elephant, she writes, “Meanwhile, the big data behemoth is growing into the proverbial elephant in the room. The problem is no longer just incorporating this data; analyzing it and acting on it are equally incomprehensible.”
As the world becomes more deeply connected, insurers must incorporate more and more data streams into the risk assessment process. “Cue the analytics software and reporting solutions, neither of which alone will make a legacy system more able to digest information from new data sources for rating and underwriting purposes,” Overhulse says. The ability to use social data to improve underwriting accuracy and risk assessments will be an enormous boon to the industry; however, the unstructured nature of the data makes its adoption extremely complex.
In Overhulse’s view, insurers must be willing to destroy old models if they are serious about exploiting big data’s riches. “By breaking the traditional data collection and utilization mold as it relates to risk assessment, insurers can integrate social data with core administration systems, making unstructured social data both accessible and actionable across all industry segments and lines of business,” she says. “By capitalizing on the explosion of social data as a resource for better insurance risk assessment, insurers can improve underwriting, streamline the claims investigation process, decrease loss costs and potentially make insurance relevant to a whole new generation of insurance consumer.”
True enough. But the question remains – are life insurance firms ready to smash their comfortable vessels on the rocks to make brilliant use of big data’s volume, velocity and variety?
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