ZestyAI’s AI Water Risk Model Approved in Five States for Homeowners Insurance

ZestyAI has received regulatory approval for its AI-driven water risk model, Z-WATER™, for use in underwriting and rating homeowners insurance in Illinois, Indiana, Iowa, Louisiana, and Wisconsin. This model addresses the rising issue of non-weather water losses, which have become the fourth-costliest peril in homeowners insurance with average claim losses exceeding $13,000. These losses stem from interior water damage such as burst pipes and hidden leaks, which have increased claim severity by 80% over the past decade. Traditional rating methods have struggled to accurately assess these risks due to their reliance on broad territory-level or age-based data, overlooking key property-specific factors. Z-WATER leverages advanced computer vision technology applied to aerial imagery along with property-level data, permitting history, localized climate information, and infrastructure context to provide a detailed risk assessment. This approach enables insurers to predict both the frequency and severity of non-weather water claims with up to 18 times greater accuracy than traditional models. Consequently, insurers can align coverage more closely with individual home vulnerabilities, set precise property-specific rates, and implement targeted inspections and mitigation strategies, such as installing smart water sensors. The approval of Z-WATER in these five states complements ZestyAI's broader regulatory achievements across five major perils, including wildfire, hail, wind, and storm. The company’s Z-PROPERTY™ solution also holds extensive state-level approvals, offering insurers and reinsurers detailed, parcel-level analytics with transparency that meets regulatory standards. By integrating machine learning, computer vision, and regulatory-grade transparency, ZestyAI’s platform supports risk assessment, underwriting, rating, reinsurance, and compliance workflows, helping carriers improve accuracy and performance. ZestyAI’s technology aims to enhance insurer decision-making by providing actionable insights that reduce cross-subsidization and improve portfolio performance. The integration of verified insurer loss data and climate science into their models reflects a data-driven approach aligned with regulatory expectations for fairness and clarity. This AI-based innovation contributes to the ongoing digital transformation within the property and casualty insurance industry, particularly in managing emerging and evolving risk factors in the homeowners segment.