Data quality and data architecture are now recognized in insurance and most other industries as the fuel needed to address our biggest challenges. Building the digital core of any organization only works when you have the data quality to act on it.
In this edition of Insurance News Analysis, Abbey Compton and I talk about how insurers are using data to adjust to the shifting risk landscape. At Davos, there was a lot of discussion about discontinuing investment and underwriting in the coal industry both to meet net-zero targets and to keep premiums from rising. As insurers consider underwriting for clean energy alternatives, they won’t have the same actuarial data or loss history they have long used for fossil fuel businesses.
As heat-related illnesses and deaths rise with global temperatures, we are seeing community-based organizations emerge to collect data at hot spots where the risk of illness and injury may be unusually high. Insurers across the spectrum of Life, Workers Comp, and Group & Voluntary Benefits may be looking to these public-private partnerships to help customers mitigate and manage heat-related risks.
In my last blog, I outlined three levels of insurance industry data analytics. A new survey of European insurers shows that 80% of those using predictive analytics report positive business impacts. But they also say they struggle with in-house facilities stretched by large volumes of data. This is also a workforce challenge, as insurers seek the kind of talent needed to do data analytics work.
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