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ZestyAI's AI Risk Models Achieve 500 Regulatory Approvals Across 44 States

ZestyAI, a provider of AI risk models for the property and casualty insurance industry, announced it has surpassed 500 regulatory approvals in 44 U.S. states. The approvals cover major property perils like wildfire and severe storms.

14 October 2026
ZestyAI's AI Risk Models Achieve 500 Regulatory Approvals Across 44 States

San Francisco, CA โ€“ October 14, 2026 โ€“ ZestyAI, the Risk Decision Platform for the property and casualty insurance industry, announced today its risk models have received over 500 regulatory approvals across 317 carrier filings. This expansion brings ZestyAI's regulatory footprint to 44 states, covering perils responsible for a majority of U.S. property losses, including wildfire, severe storm, hail, and fire and water damage.

The milestone arrives as insurers and regulators navigate maintaining coverage availability amid increasing weather volatility. ZestyAI's models assess individual properties rather than relying on broad geographic averages. This approach allows for better recognition of mitigation efforts and more accurate risk-based pricing, offering carriers and regulators enhanced visibility into risk assessment.

Each approval signifies a state Department of Insurance's acceptance of a ZestyAI model for use in rating or underwriting processes. The approvals span both personal and commercial property lines, reflecting new adoptions and expanded use of existing models across states, perils, and programs. Notably, this includes the first AI model approved for wildfire risk pricing in California, a market where property-specific intelligence is supporting new capacity.

"Five hundred approvals represent the industry deciding, one rate filing at a time, that AI belongs in how insurance risk is underwritten, priced, and reinsured," said Kumar Dhuvur, Founder and Chief Product Officer at ZestyAI. The company actively collaborates with regulators as standards for AI in insurance take shape, ensuring transparency and explainability in its models.

Original source: prnewswire.com