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ZestyAI today announced that the Connecticut Insurance Department (CID) has formally approved its Roof Quality solution for use in residential property underwriting.

CID conducted a comprehensive third-party actuarial review of ZestyAI’s model, evaluating methodology, data integrity, and regulatory compliance against its rigorous standards.

What CID’s Approval Means for Carriers

Part of Z-PROPERTY™ , the Roof Quality model enables insurers to assess and price roof risk with unmatched accuracy.

By combining 3D property analysis, high-resolution aerial imagery, and AI trained on extensive real-world data, the platform replaces subjective or incomplete assessments with objective, property-specific intelligence.

The model classifies roofs into five standardized condition levels, helping insurers assess property risk with greater precision. It distinguishes between surface-level wear and structural issues, flagging meaningful signs of deterioration such as missing shingles, tarps, or water pooling.

Bryan Rehor, Director of Regulatory Affairs at ZestyAI, said:

“Roof condition is one of the strongest predictors of loss, yet historically one of the hardest to assess without costly inspections. This approval affirms the accuracy, fairness, and transparency of our approach and reflects our broader commitment to aligning innovation with consumer protection.”

How ZestyAI’s Roof Quality Model Improves Risk Assessment

ZestyAI maintains active engagement with the National Association of Insurance Commissioners (NAIC) and state-level departments to ensure its models meet evolving standards for fairness, transparency, and consumer protection.

By proactively filing through in-house Rating and Advisory Organizations, ZestyAI ensures its models meet the strictest regulatory standards before reaching the market. 

ZestyAI’s models are trusted by regulators and insurers to assess risk in the nation’s most climate-exposed regions with Z-FIRE, ZestyAI’s wildfire risk model, approved by regulators in all western states. Its Severe Convective Storm suite is approved in 16 states across the Midwest, Great Plains, and South.

ZestyAI today announced a new partnership with Farmers and Mechanics Mutual Insurance Company of West Virginia (FMIWV) to strengthen underwriting accuracy and improve risk-based pricing.

Why Accurate Roof Data Matters More Than Ever

Roof claims are the leading driver of insurance losses, yet many carriers still depend on self-reported data, which can be unreliable, or physical inspections, which are costly and hard to scale. Recent research shows that 15% of roofs are at least eight years older than reported, highlighting the need for more reliable, data-driven alternatives.

To address long-standing challenges in accessing reliable property insights, FMIWV selected Z-PROPERTY™.

Why FMIWV Chose Z-PROPERTY

Dan Otto, Senior Vice President and Chief Financial Officer at FMIWV, said:

"After evaluating several options in recent years, we chose ZestyAI based on their strong coverage and ease of implementation. Getting started using the technology was easy. We’re particularly focused on using their property insights to provide additional underwriting information for new business and renewals.”

ZestyAI’s advanced insights help FMIWV:

• Identify high-risk properties early to prioritize mitigation and prevent losses
• Enhance risk selection and pricing precision with objective, property-level data
• Reduce inspection costs and turnaround time by minimizing the need for on-site assessments
• Streamline straight-through processing for low-risk properties to improve efficiency and speed to bind
• Reduce premium leakage by aligning pricing with actual exposure

“FMIWV is showing how regional carriers can lead with data-driven underwriting that improves operations and elevates the customer experience,” said Attila Toth, Founder and CEO of ZestyAI.

“By grounding decisions in reliable, property-level data, they’re improving efficiency, reducing risk, and raising the bar for underwriting and rating precision.”

Wildfire Risk Is Rising Nationwide‍

Wildfire seasons are getting longer, more destructive, and harder to predict—and they’re no longer just a Western U.S. concern. From the Southeast to the Midwest, wildfire risk is emerging in places many insurers haven’t traditionally watched.

What the Latest Data Reveals About Wildfire Exposure

Drawing from the latest national datasets and insights from ZestyAI’s Z-FIRE™ model, this visual guide to wildfire risk in the U.S. shows:

• New wildfire hotspots: Discover where risk is rising fastest.
• Mitigation gaps: Learn how a lack of defensible space is putting thousands of homes in danger across the country.
• Top risk drivers: See how features like overhanging trees and wooden roofs are fueling destruction in high-risk areas.

Download Free Infographic

BONUS: You’ll also get access to our latest online event with IBHS and Western Fire Chiefs Association, The LA Fires in Focus: What Worked, What Didn’t, What’s Next for Insurers.

At ZestyAI, we’re continually enhancing our platform to help insurers better understand property risk, strengthen underwriting precision, and streamline operations. Over the last few months, our team has launched a series of updates that make our AI-powered solutions even more powerful and accessible.

From improvements in roof geometry and manufactured home modeling to expanded wildfire data on home hardening and structural vulnerability, plus faster APIs and faster APIs, these updates reflect our deep commitment to product excellence and customer success.

Smarter Risk Modeling for Manufactured Homes

Updated Manufactured Home Model in Z-PROPERTY

We’ve released an upgraded model for manufactured homes within Z-PROPERTY, trained on a broader set of imagery sources to improve data coverage and model accuracy.

This new version reduces the false positive rate, giving carriers greater confidence when evaluating mobile home risk—a critical upgrade for lines of business that rely on nuanced property insights.

Better Roof Geometry Analysis with the Roof Facet Model

Enhanced Accuracy and Speed in Roof Modeling

As part of our Digital Roof product, the Roof Facet model has been refined to deliver sharper roof geometry insights with faster processing times. This enhancement is especially valuable for customers scoring large portfolios, improving both speed and data quality.

Expanded Wildfire Risk Data Coverage

Compliance Pre-Fill Now Covers 99%+ of Wildfire Properties

We’ve expanded our Compliance Pre-Fill solution to support all wildfire-prone states, with property-level coverage now exceeding 99%. New on-demand access to critical wildfire mitigation features—including enclosed eaves, six-inch vertical clearance for siding, and noncombustible fencing—allows underwriters and compliance teams to make more accurate decisions with current data and zero added latency.

3D That Tells the Whole Story

Access to Richer 3D and Historical Imagery

ZestyAI now provides access to a broader and deeper catalog of high-resolution and historical imagery, powered by best-in-class sources of imagery. These enhancements improve underwriting workflows and allow insurers to “time travel” and evaluate how a property’s condition has changed over time. 

This capability is especially valuable in regulatory environments that require proof of property degradation before policy changes are made. Historical imagery also gives underwriters a fuller view of prior conditions, providing context for past underwriting decisions.

Performance Gains Across All APIs

Reduced Latency and Improved API Response Times

We’ve optimized our APIs to significantly reduce latency, improving the speed and reliability of data delivery across all ZestyAI products. Whether batch scoring, running real-time underwriting, or executing renewal workflows, you’ll experience faster performance and greater efficiency—saving valuable time across your organization.

A New and Improved Upload Experience in Z-PORTFOLIO

Redesigned Upload Page for Streamlined Workflow

We’ve redesigned the Z-PORTFOLIO upload experience to make portfolio submissions more intuitive and efficient. Instructions and templates are now centralized in one place, and users can specify the purpose of the upload—such as renewal or dislocation analysis—to unlock deeper insights into usage patterns. This update enhances the self-service experience and helps customers extract maximum value from portfolio analyses.

Driving Continuous Innovation in Insurance Risk Intelligence

These product updates are more than just technical enhancements; they’re part of a larger mission to transform property risk assessment through AI. By improving model precision, reducing friction in workflows, and delivering richer, more current data, we’re helping insurers stay ahead of emerging risks and changing market demands.

If you’d like to learn more about any of these updates or request a demo, reach out to your Customer Success Manager or contact us below.

The Silent Peril Reshaping Homeowners Insurance

Non-weather water damage rarely makes headlines, but it’s quietly eroding profitability across the country.
It is now the fourth costliest peril in homeowners insurance, and claim severity has increased 80% in the last decade—a trend that’s accelerating even as frequency remains relatively flat.

Traditional risk models struggle to capture the early warning signs behind these losses, leading to mispriced policies, undetected exposure, and rising volatility for carriers.

Want the full analysis? Download the complete “Winning the Fight Against Non-Weather Water Losses” guide.

Why Loss Severity Keeps Rising

Aging homes and overlooked system failures

Many of the most expensive losses stem from aging plumbing, deteriorating materials, and slow-burn failures that often go undetected until damage is significant.

Frequency is flat—severity is not

Loss patterns suggest that while the number of events hasn’t surged, the financial impact of each event has—a signal that traditional models are not capturing the right property-level predictors.

The Property Features Most Predictive of Water Losses

The overlooked attributes that traditional models miss

Standard territory- or age-based assessments often ignore the property-specific details that meaningfully influence water loss risk, including:

• supply line material and age
• plumbing configuration
• occupancy patterns
• system maintenance and upgrades
• moisture exposure and prior loss indicators

These factors vary widely between neighboring homes—yet most models treat them as identical.

Where Traditional Underwriting Falls Short

ZIP-code and age-based proxies mask true risk

Legacy approaches rely heavily on broad territory-level assumptions that overlook structural vulnerabilities and system conditions.

Limited visibility creates mispriced policies

Without property-level insight, high-risk homes are often underpriced while lower-risk homes subsidize them—driving loss ratio volatility over time.

Get deeper insights on the drivers of water loss severity in our full guide → “Winning the Fight Against Non-Weather Water Losses”

How AI and Property-Level Data Are Changing the Landscape

AI models trained on real-world claims data can identify early signals of potential water loss by analyzing the interaction between:

• plumbing systems
• property attributes
• historical patterns
• material degradation
• repair history

This enables carriers to segment risk accurately, adjust pricing, and reduce preventable losses—long before small issues turn into major claims.

What Homeowners Actually Understand About Water Risk

Misconceptions around coverage and prevention

ZestyAI’s research shows that many policyholders:

• misunderstand what is and isn’t covered
• underestimate how much damage water can cause
• rarely take preventive actions unless prompted

This disconnect creates an opportunity for carriers to strengthen education, mitigation, and customer engagement.

Steps Carriers Can Take Today

Improve segmentation and rating accuracy

Property-level signals enable more precise risk tiers and more stable long-term portfolios.

Strengthen mitigation and reduce loss severity

Insights help identify which homes are at elevated risk and where targeted mitigation can reduce exposure.

Enhance underwriting workflows with explainable insights

Transparent, explainable AI helps underwriters understand the key drivers behind elevated risk—supporting both decision-making and regulatory review.

Get the Full Guide

Our new research paper, Winning the Fight Against Non-Weather Water Losses, breaks down the trends reshaping this growing peril—and the strategies carriers can use to get ahead of it.

Access the Guide

ZestyAI announced that its Severe Convective Storm suite, including Z-HAIL™, Z-WIND™, and Z-STORM™, has received regulatory approval from the Ohio Department of Insurance.

With the addition of Ohio, ZestyAI’s Severe Convective Storm suite is now approved for use in 16 states, covering key high-risk markets across the Midwest, Great Plains, and South.

Ohio Faces Rapidly Rising Storm Losses

Ohio has experienced 36 billion-dollar loss storm events over the past five years alone, surpassing the total from the previous two decades, which saw just 33 events, according to NOAA’s National Centers for Environmental Information (NCEI). Severe convective storms, including hail, wind, and tornadoes, were the driver, contributing to over 57% of the state’s total weather-related damages since 1980.

Traditional Models Miss Critical Property-Level Differences

ZestyAI’s AI-driven platform predicts the likelihood and severity of claims from severe convective storms at the individual property level by analyzing the interaction of local climatology with property-specific characteristics. In contrast, most risk assessment models today rely on broader territory or ZIP code-level evaluations, overlooking critical property-level factors. 

Each model is built and validated on extensive real-world claims data and delivers transparent explanations of the key drivers behind every risk score, helping carriers make more accurate underwriting and rating decisions.

Key capabilities include:

• Z-HAIL: Predicts hail damage risk and claim severity using property-specific attributes like roof complexity, historical losses, and accumulated damage, identifying which homes are most likely to file a claim, even within the same neighborhood.
• Z-WIND: Combines AI-generated 3D analysis of roof condition, complexity, and potential failure points with local climatology to deliver pivotal insights into property-specific wind vulnerability and severity.
• Z-STORM: Predicts the frequency and severity of storm damage claims, examining the interaction between climatology and the unique characteristics of every structure and roof.

Regulatory Approval Reflects a Shift Toward Precision Underwriting

“Too often, storm risk is priced using rough proxies instead of accurate property insights,” said Kumar Dhuvur, Founder and Chief Product Officer at ZestyAI.

“Regulatory approval in Ohio affirms the industry’s shift toward precision underwriting and rating—and opens the door to smarter, risk-aligned decisions and fewer preventable losses in one of the nation’s most important insurance markets.