Non-weather water damage is a top peril eroding profitability for insurers, and traditional risk models fall short. This guide explores how AI-powered, property-level data can improve underwriting, pricing accuracy, and proactive risk mitigation.
Non-weather water damage is the fourth costliest peril in homeowners' insurance, with claim severity up 80% over the past decade. Traditional models that rely on broad geographic data often miss the key drivers, leading to mispriced policies and rising loss ratios.
This guide explores how AI-powered risk modeling helps insurers break that cycle. With predictive analytics and property-level data, carriers can segment risk with greater precision, improve pricing accuracy, and proactively mitigate future claims.
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