Can AI improve the predict and prevent paradigm?
AI risk assessments may offer more advantages because they give insureds the tools and information necessary to monitor and improve their properties.
Most insurers have switched from the traditional adage of “detect and repair” to a “predict and prevent” mindset, with a focus on home maintenance, such as checking the roof for broken shingles, and disaster preparedness efforts like creating defensible space around the home. Some insurers are turning to artificial intelligence (AI) to improve their predict and prevent models. This switch may increase access to coverage for insureds in hard markets, lower premiums and prevent insurers from having to withdraw coverage in high-risk states.
“The majority of insureds want to be proactive about risk management or maintenance of their home or commercial building,” said David Tobias, general manager of insurance at Nearmap, which specializes in 3D datasets, geospatial tools and aerial imagery. “There [are] so many tools now available through the industry. How we monitor risk, look at the brush fire defensible space, things of that nature, roof quality – they haven’t made it through to insurance yet.”
AI Risk Assessments
AI can help insurers improve their predict and prevent metrics with incredible accuracy, leveraging vast datasets and complex algorithms to tailor policies to the insured, according to the Forbes Technology Council. Machine-learning AI models can unearth trends and form predictions that a human analyst may not because these systems apply statistical analysis to data to find patterns. Machine-learning technology adapts to understand information and models analyst behavior without human instruction, but it doesn’t replace the human touch in insurance.
The rapid increase in catastrophic weather events has escalated claims and losses but also made the “predict” portion of this model more complex. Repair losses are increasing exponentially, and there’s not enough capital to continue covering these damages in the long term, which is why companies like ZestyAI are honing in on property risk assessment with AI technologies.
“Part of the reason why it has been challenging historically for carriers is that the risk for each peril depends on a variety of factors,” said Kumar Dhuvur, co-founder and head of product at ZestyAI which makes several property insights products and climate risk models. “Some of them are climate-related. Some of them topography-related… It’s hard for carriers to go, and a; figure out which factors matter for each peril, b; source that data from so many different places, and c; model it very well so that they’re able to predict it.”
Partner with policyholders
Risk assessments with AI tools may have more advantages because they give insureds the tools and information necessary to monitor and improve their property and may foster a stronger relationship between insurers and policyholders. “There’s a tremendous opportunity to actually engage in a positive way with insureds to help them be a partner in predict and prevent,” said Tobias.
“They [regulators] all want to make modeling less of a black box, and they want this information to eventually go to the insured… We not only provide a score for a property that tells you whether it’s high or low risk, we actually tell you what factors for that specific property contributed to that high score or low score,” said Dhuvur. He explains that some properties with a lot of vegetation may get flagged for higher wildfire risk, but policyholders can adjust the score by addressing the issues flagged in the report. “Now a homeowner has incentive and the knowledge to be able to take action.”
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