ZestyAI Product Insights

Winter Storm Fern has evolved into a historic catastrophe for the U.S. insurance industry. Between January 23-27, 2026, the storm shattered records by placing over 230 million Americans under severe winter alerts, with a death toll of 85 as of February 3rd. 

Preliminary industry estimates place insured losses at $6.7 billion, potentially making Fern the third-costliest U.S. winter storm on record, trailing Elliott (2022) and Uri (2021). The crisis is far from over. The National Weather Service warns of a "historic duration" of extreme cold, with temperatures 15 to 25 degrees below average, that continues to hamper mitigation efforts.

For carriers, Fern is a complex, multi-peril challenge. Claims teams are navigating a surge of freeze-related losses, ice-driven structural damage, and widespread business interruptions across 34 states. 

To understand the stakes, one needs to look no further than February 2021, when Winter Storm Uri brought Texas to its knees and generated over $11 billion in insured losses from a single state. Fern’s footprint is broader, and its secondary effects are still unfolding.

The Cold Hard Numbers from Storm Uri: Why Claims Explode Below 5°F

Our analysis of the 2021 Storm Uri reveals a striking relationship between temperature deviation and claim frequency for the non-weather water and freeze perils. Using data from multiple carriers, we tracked daily claim rates against minimum temperatures: before, during, and after the storm window (February 11-20, 2021).

The results show how rapidly falling temperatures can transform a routine winter pattern into a systemic loss event, allowing us to monitor the market’s response in real-time as conditions deteriorated, peaked, and normalized.

The results are dramatic:

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Figure 1: Daily claim rates (blue line) surged 126X above the baseline in a temporal spike as temperatures (orange line) plunged below the 20-year average (dashed green line) during Winter Storm Uri.

The chart reveals a clear inverse relationship: as minimum temperatures dropped from the mid-40s°F to below 5°F, daily claim rates didn’t just rise, they increased 126X, from a baseline of 0.04% to 0.46% at the peak. This dramatic surge underscores the significant consequences of extreme cold events on insurance liability.

Figure 2: ZestyAI’s Z-WATER™ demonstrated an 11X increase in claim frequency between ‘Very High’ and ‘Very Low’ risk tiers during Winter Storm Uri

We used ZestyAI’s Z-WATER™ to segment the property-specific non-weather water risk across the 10-day storm window. Z-WATER™ is a risk model that accounts for how plumbing design, local climate, and infrastructure reliability interact to drive non-weather water and freeze losses. By capturing real-world dynamics, such as temperature swings that stress pipes and electrical grid failures that amplify claims, the model delivers a scientifically grounded view of property-level risk.

The results were definitive: properties that Z-WATER™ scored as ‘Very High’ risk filed 26 claims per 1,000, compared to just 2.2 claims per 1,000 for those scored as ‘Very Low’, an 11X increase in claim frequency.

This accurate segmentation reveals a clear path to managing volatility. Z-WATER™ provides a deep understanding of a home’s resilience across the full spectrum of loss mechanisms, from everyday plumbing failures to expensive outlier events like Storms Uri and Fern. By enabling precise intra-territory risk splitting, the model allows carriers to price and underwrite more reliably, ensuring premiums reflect the true risk profile while protecting the portfolio against systemic losses.

The January 2026 Storm: History Rhyming?

While we can already see the immediate impact of Winter Storm Fern, the primary difference between Fern and Winter Storm Uri is the duration of the freezing event itself, rather than any changes in how quickly policyholders are filing their claims.

As shown in Figure 1, NWW claims rise rapidly as temperatures fall and taper off quickly once conditions normalize. The risk in prolonged cold events lies in how long properties stay below the Plumbing Design Temperature; the longer the freeze, the greater the likelihood of systemic plumbing failure.

During Winter Storm Uri, extended sub-freezing conditions significantly increased the number of days in which vulnerable properties were exposed to frozen pipe failures, driving aggregate losses to historic levels. Fern is now exhibiting a similar duration profile, with sub-freezing conditions persisting for up to 10 consecutive days across parts of the Northeast. The National Weather Service has warned this “could be the longest duration of cold in several decades,” raising the likelihood of elevated losses even if individual claims remain tightly clustered in time.

For carriers, the warning signs are already flashing:

• The Power Failure Multiplier: During the storm's peak, over 1 million customers lost power. In the South, where homes lack the heavy thermal insulation of northern properties, a power outage is the primary driver of catastrophic pipe bursts. Without active heating, a property can reach the "burst threshold" within hours.
• The $30,000 Claim Severity Benchmark: Recent State Farm data underscores the high stakes of these events. Winter water damage claims totaled over $628 million, with the average claim payment now exceeding $30,000. For carriers, this high per-claim severity means even a moderate frequency surge can quickly erode Q1 margins.
• Regional Fragility in the South: While the initial assessments are still surfacing, early industry estimates for privately insured losses from Winter Storm Fern puts the damage at $4 billion to $7 billion. With Texas and Tennessee identified as the hardest-hit states, carriers are facing a "Uri-style" scenario in which infrastructure wasn't designed for a 10-day deep freeze.

From Reactive to Predictive: Solving the $6.7 Billion Freeze Risk Equation

The 2021 Texas freeze taught us that traditional approaches to freeze risk are highly insufficient. Many properties that experienced burst pipes were in areas that rarely see extended freezing temperatures, meaning they lacked adequate winterization. 

This is where predictive analytics becomes essential. By modelling the interaction between property-level vulnerabilities and local temperature thresholds, carriers can better identify which properties are most vulnerable to freeze events before the damage actually occurs.

Key Risk Drivers Identified in Our Latest Analysis:

• The Design Mismatch: The greatest risk isn't just the cold; it's the sudden change in temperature. Properties in states like Texas or Tennessee face a higher risk because they are built to release heat, not trap it. They lack the heavy insulation and deep-buried pipes needed to survive a 10-day freeze.

• The Power Grid Vulnerability: Our analysis shows that areas prone to power outages face a compounded risk. In the South, a home’s primary defense is its heating system so when the power fails and the heater stops, the "burst threshold" can be reached in just a few hours.

• Building Vulnerabilities: Our analysis shows that older homes and properties with plumbing routed through exterior walls are disproportionately represented among $30,000 non-weather water losses.

The Bottom Line for Carriers

The 2021 Texas freeze was a pivotal moment for the industry, generating more than 500,000 claims and $11.2 billion in insured losses in a single state. Today, Winter Storm Fern represents an even broader systemic threat, with weather alerts impacting 230 million people across more than 30 states.

While the final tally for Fern is still developing, the data is already clear: temperature shocks drive claims at exponential rates. With early industry assessments estimating privately insured losses between $4 billion and $7 billion, it is evident that the prolonged duration and geographic anomaly of extreme weather events are the primary drivers of this volatility.

For carriers looking to protect their Q1 margins, predictive analytics are no longer a luxury; they are a requirement. By analyzing property-level characteristics, regional vulnerabilities, and historical temperature deviations, you can move from reactive claims handling to proactive risk management. 

The question isn't whether another major freeze will occur, but whether your portfolio is prepared for the next 126-fold surge.

Learn More About Z-WATER 

ZestyAI’s Z-WATER™ provides the industry’s most granular view of interior water risk, helping carriers accurately and reliably assess properties in areas prone to temperature shock events. By analyzing detailed property-level characteristics alongside historical weather patterns and regional risk factors, our advanced models predict the likelihood of Non-Weather Water (NWW) and freeze claims as well as their associated severity. This deeper level of analysis empowers carriers to make smarter pricing decisions before the next major storm hits.

Methodology: Analysis based on aggregated claims from multiple Texas carriers during Winter Storm Uri (February 2021). Temperature data reflects mean daily minimums across the exposure footprint, weighted by ZIP Code to account for geographic density. The claim/exposure ratio was calculated by dividing daily claims by the average policy-day exposure.

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¹CNN Weather, "More than 230 million people under alerts for potential ice, heavy snow and extreme cold," January 2026. [link]

²Fox News, "Noem coordinates with Mississippi officials as state recovers from deadly winter storm," January 2026. [link] 

³Insurance Innovation Reporter, “KCC Estimates $6.7 Billion in Insured Losses from Winter Storm Fern,“ February 2026 [link]

⁴Texas Department of Insurance, "Insured Losses Resulting from the February 2021 Texas Winter Weather Event," March 2022. [link]

 ⁵Fox Business, “More than 1 million Americans lose power as monster winter storm sweeps across the US,” January 2025 [link]

 ⁶Carrier Management, “Frozen Pipes Lead to $628M in Losses for State Farm,” January, 2026 [link]

⁷ Barrons, “Winter Storm Fern Packed a Wallop. Now the Cost Estimates Are Rolling In.,“ February 2026 [link]

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Wildfire risk in the United States is no longer confined to the edges of forests or traditionally high-risk zones. New analysis using ZestyAI’s property-level wildfire models shows that millions of homes classified as low or no wildfire risk under federal assessments face elevated wildfire danger when evaluated at the property level.

This analysis was recently featured in Vox, which examined how wildfire behavior is evolving — and why broad, backward-looking risk maps are increasingly misaligned with how fires spread today.

👉 Read the full article on Vox → https://www.vox.com/climate/476932/california-wildfire-los-angeles-risk-ai-housing-climate

Wildfire risk is closer — and more granular — than most maps show

Many homes damaged or destroyed in the 2025 Los Angeles wildfires were still classified as “low risk” under federal wildfire assessments. ZestyAI’s property-level analysis provides a different perspective.

By evaluating individual structures — including vegetation proximity, defensible space, building characteristics, and neighborhood-level fire dynamics — ZestyAI identified more than 3,000 properties worth approximately $2.4 billion in areas impacted by the Palisades and Eaton fires that showed elevated wildfire risk despite being classified as low or no risk under FEMA’s census-level assessments.

Across California, the classification gap is even broader. Approximately 1.2 million properties, representing roughly $940 billion in residential property value, are designated as low or no wildfire risk under federal maps, despite AI-driven property-level models indicating elevated wildfire danger.

Why census-level wildfire maps fall short

Wildfires do not spread evenly across census tracts or counties. Ember-driven ignition, structure-to-structure spread, wind conditions, and localized vegetation patterns create uneven outcomes, where one home survives and the next is destroyed.

Federal wildfire assessments are designed to provide a baseline view of community-level risk. FEMA has noted that its National Risk Index is not intended to serve as a property-specific risk assessment. When risk is evaluated at the individual property level, meaningful differences emerge that aggregated maps are not designed to capture.

What more granular wildfire risk intelligence enables

More detailed wildfire risk data can support:

• Targeted mitigation efforts at the property and neighborhood level
• More informed rebuilding and land-use decisions
• Clearer, more defensible underwriting and portfolio strategies
• Improved dialogue between insurers, regulators, and communities

A shift in how wildfire risk is understood

Wildfire risk is evolving faster than the systems built to measure it. Homes are no longer just adjacent to wildfire hazards; they increasingly influence how fires ignite, spread, and intensify, even in dense urban environments.

Property-level risk intelligence does not remove hard decisions. But without it, those decisions are made using an incomplete picture of where wildfire risk truly exists.

Read the full Vox article here.

In property insurance, roof age is one of the most critical factors in assessing risk. Yet too often, carriers rely on self-reported or agent-supplied data that is incomplete or inaccurate.

ZestyAI’s recent analysis of 500,000+ properties revealed widespread discrepancies in reported roof age. The result? Mispriced policies, unexpected losses, and operational inefficiencies that impact the bottom line.

As climate volatility grows and reinsurance pressure intensifies, overlooking the true condition and age of a home’s largest, most exposed surface is a risk no carrier can afford.

What’s Inside

• Uncover the biggest myths and blind spots in roof age records.
• Understand why traditional data sources, like claims systems and permits, fall short in providing accurate roof age.
• Learn how a multi-source verification strategy, combining aerial imagery, permits, tax records, and AI, offers a blueprint for improvement and 97% national coverage.
• Explore why roof age is a predictor of losses across multiple perils, not just wind and hail.
• Discover the one-two punch of verified roof age and real-time condition insights, delivering a complete view of risk, even for young roofs with hidden problems.
• Align your roof age data with growing regulatory expectations, particularly in states like Florida.

Access the Guide.

New research from ZestyAI reveals that 62% of U.S. homeowners are deferring critical home maintenance, adding up to $317 billion in potential claims exposure for insurers.

These findings come as Severe Convective Storms (SCS) caused an estimated $58 billion in insured losses in 2024, surpassing hurricane-related losses and marking the second-costliest SCS year on record.

Tornadoes, hail, and wind events now account for over 60% of all U.S. catastrophe claims, and research from the Insurance Institute for Business & Home Safety (IBHS) shows that roof damage accounts for up to 90% of residential catastrophe losses.

Key Findings from ZestyAI’s Homeowner Survey

According to ZestyAI’s nationally representative survey, 62% of homeowners have delayed essential repairs due to budget constraints, representing nearly 59 million U.S. homes with unaddressed vulnerabilities. Forty percent said they would rely on an insurance claim to cover major repairs like roof replacement, adding up to an estimated $317 billion in potential exposure for carriers.

Alarmingly, 63% of homeowners who weren’t living in their home at the time of the last roof replacement don’t know how old their roof is, making it even harder to detect aging systems before they fail. Meanwhile, 12% admitted they would delay repairs indefinitely, further increasing their risk of property damage.

Severe Convective Storms: The Growing Catastrophe Risk

This blind spot compounds known risks: prior ZestyAI analysis has identified over 12.6 million U.S. properties at high risk for hail-related roof damage, representing $189.5 billion in potential roof replacement costs.

“Deferred maintenance has long been a known risk factor, but today the stakes are higher than ever,” said Kumar Dhuvur, Co-Founder and Chief Product Officer of ZestyAI. "With claim severity rising and storm losses compounding, insurers need more than hazard maps to navigate this landscape."

"Property-level insights allow carriers to proactively address known vulnerabilities, improve underwriting precision, and work with homeowners to reduce losses before they happen.”

ZestyAI’s findings support a growing push toward data-driven, preventative underwriting strategies, especially as carriers face rising claim severity and pressure to improve combined ratios across storm-prone states.

With insured losses projected to exceed $30 billion, the recent Los Angeles wildfires rank among the costliest in U.S. history—reshaping how insurers think about risk, resilience, and readiness.

Watch the Full Webinar → LA Fires in Focus: What Insurers Need to Know

In this on-demand webinar, experts from the Insurance Institute for Business & Home Safety (IBHS), the Western Fire Chiefs Association, Cal Poly’s WUI Fire Institute, and ZestyAI unpack what really happened—from frontline response to lab-based research and model performance—and share critical strategies insurers can use to prepare for what’s next.

Watch this session if you’re a Product Managers, Underwriters, Actuaries, and Risk & Innovation leaders looking to make informed decisions in an increasingly volatile wildfire landscape.

What You’ll Learn

• Key takeaways from the Los Angeles wildfires
• Research on structure-to-structure fire spread and resilience factors
• How wildfire risk models performed—what we got right (and wrong)
• Practical strategies to reduce exposure and strengthen resilience

Meet the Experts

• Anne Cope, Chief Engineer, IBHS
• Bob Roper, CEO, Western Fire Chiefs Association
• Frank Frievalt, Director, WUI Fire Institute at Cal Poly
• Kumar Duhvur, Co-Founder & CPO, ZestyAI

With insured losses projected to exceed $30 billion, the recent Los Angeles wildfires rank among the costliest in U.S. history—reshaping how insurers think about risk, resilience, and readiness.

Watch the Full Webinar → LA Fires in Focus: What Insurers Need to Know

In this on-demand webinar, experts from the Insurance Institute for Business & Home Safety (IBHS), the Western Fire Chiefs Association, Cal Poly’s WUI Fire Institute, and ZestyAI unpack what really happened—from frontline response to lab-based research and model performance—and share critical strategies insurers can use to prepare for what’s next.

Watch this session if you’re a Product Managers, Underwriters, Actuaries, and Risk & Innovation leaders looking to make informed decisions in an increasingly volatile wildfire landscape.

What You’ll Learn

• Key takeaways from the Los Angeles wildfires
• Research on structure-to-structure fire spread and resilience factors
• How wildfire risk models performed—what we got right (and wrong)
• Practical strategies to reduce exposure and strengthen resilience

Meet the Experts

• Anne Cope, Chief Engineer, IBHS
• Bob Roper, CEO, Western Fire Chiefs Association
• Frank Frievalt, Director, WUI Fire Institute at Cal Poly
• Kumar Duhvur, Co-Founder & CPO, ZestyAI

In today’s property insurance market, understanding wildfire risk at the individual structure level is more critical than ever. For wholesale brokers like Jencap, the ability to deliver actionable, data-driven insights to retail agents and carriers is essential, especially as wildfire conditions evolve rapidly across the country.

That’s why JenCap turned to ZestyAI.

In the video above, Ben Beazley, EVP and Head of Property at JenCap, shares two key reasons why ZestyAI’s Z-FIRE™ model stood out:

• Structure-level precision: Z-FIRE analyzes wildfire risk at the individual property level—factoring in building materials, defensible space, vegetation, topography, and historical fire patterns to predict which homes are most vulnerable and which are more likely to withstand a fire.
• Continuously refreshed data: While the core model remains stable and DOI-approved, Z-FIRE is updated with the latest fire perimeters, confirmed loss locations, and vegetation data. This ensures it reflects the most current wildfire seasons and emerging risk patterns.
• Z-FIRE is already approved for rating and underwriting in every major wildfire-prone state. For JenCap and many others, it’s become a vital tool for evaluating wildfire exposure and supporting smarter decisions in high-risk areas.

Not every wildfire spreads through forests. Some move house to house, block to block, driven by wind, heat, and proximity. As these events become more common, insurers are asking sharper questions about how to assess risk in densely built environments.

Z-FIRE was designed to account for the real-world conditions that enable this type of fire behavior, capturing the structural, spatial, and environmental factors like building density, defensible space, mitigation strategy, climatology, and slope that contribute to urban conflagration.

What Is Urban Conflagration?

Urban conflagration refers to a fire that spreads rapidly through a densely built environment, jumping from structure to structure rather than moving solely through vegetation. Unlike traditional wildfires that primarily burn forests or grasslands, these events are driven by building materials and the adoption of fire mitigation practices, the spacing between structures, and proximity to areas that are known to be challenging for firefighters.

Wind and terrain—particularly steep slopes—often accelerate the spread, pushing fire fronts into residential areas and compounding the risk of widespread destruction. For insurers, urban conflagrations represent a uniquely challenging peril, where fire behavior is shaped as much by the built environment as by natural fuels. Without a model built to capture these dynamics, they’re nearly impossible to assess accurately.

We’ve seen what happens when wind-driven embers reach tightly packed communities. In fires like Marshall, Lahaina, and the recent Palisades and Eaton Fires, wildfires didn’t stop at the wildland-urban interface—they became full-scale urban conflagrations.

These events are stark reminders that wildfire risk doesn’t end at the edge of the forest and that densely populated areas can produce concentrated, catastrophic losses.

For insurers, these events raise a critical question: Where are the next high-concentration loss scenarios hiding in the portfolio?

When urban conflagration strikes, damage rates spike, and PML exposure can increase exponentially. Identifying the neighborhoods where construction patterns, terrain, and fuel conditions create the potential for that kind of fire behavior is no longer optional; it’s essential. 

At ZestyAI, we’ve been asking, and answering, these questions for years. The Z-FIRE model was designed to do exactly that.

Built to Reflect Real-World Urban Risk

From its inception,  Z-FIRE has accounted for the drivers of urban conflagration. Its two-level architecture combines neighborhood-level dynamics with property-specific characteristics, offering a detailed and scalable view of wildfire risk—even in densely built environments.

Level 1 (L1): Neighborhood Risk Score

L1 predicts the likelihood that a property will fall within a wildfire perimeter. It does this by analyzing climatology, historical wildfire behavior, terrain, fuel type, and wildfire suppression ratings to understand where fires are likely to start, spread, and grow.

Two variables are particularly important for identifying urban conflagration risk:

• Fuel Type, which accounts for both vegetative fuels and the built environment. In densely developed areas, clusters of structures can serve as fuel, particularly when combined with slope, dry conditions, or limited defensible space.
• Wildfire Suppression Rating (WSR), which indicates areas where fire suppression is likely to fail due to access, water availability, or firefighting capacity. Rather than relying on WSR alone, Z-FIRE factors in proximity to high and very high WSR zones, enabling it to capture risk spillover into nearby neighborhoods.

What sets Z-FIRE apart is how these variables interact. The model doesn’t assess them in isolation—it evaluates how multiple risk factors compound one another. This layered, interaction-driven approach allows Z-FIRE to surface hidden vulnerabilities that simpler, one-dimensional models often miss and accurately identify regions with high conflagration risk.

• L1 evaluates wildfire risk based on local terrain, fuel types, and building density. Critically, Z-FIRE incorporates two critical variables that are key indicators of conflagration risk. Both “fuel type” and proximity to areas with a High or Very High wildfire suppression rating.
  • Fuel Type accounts for both vegetation and for developed land characteristics. While vegetative fuel and its management are key for wildfire, building density can become a large contributor to conflagration risk if combined with the other high-risk factors. It is important to remember that Z-FIRE allows for unlimited variable interaction; a high-density neighborhood can be at high risk of other factors, contributing to conflagration risk.
  • Wildfire Suppression Rating (WSR) reflects the risk that a high-intensity fire may become impossible to manage by firefighters. Because those areas tend to be located in the WUI, Z-FIRE does not only rely on the WSR score, but also uses the distance proximity to high or very high WSR.

Level 2 (L2): Property-Specific Risk Score

At the structure level, L2 evaluates both nearby vegetation and building fuel density, a key driver of structure-to-structure ignition. This metric, validated by the Insurance Institute for Business & Home Safety (IBHS), helps Z-FIRE model how a single ignition can escalate within tightly packed neighborhoods, even in the absence of natural fuels.

Z-FIRE was trained on real-world events with clear urban conflagration patterns. In the 2017 Tubbs Fire, for example, flames spread deep into Santa Rosa, destroying dense subdivisions like Coffee Park. Similarly, recent fires such as Palisades and Eaton moved rapidly through built-up areas, where tightly spaced structures provided a continuous path of fuel.

Case in Point: Palisades and Eaton, CA

In the recent Palisades and Eaton Fires, Z-FIRE’s predictive accuracy was once again put to the test. Our analysis showed that over 91% of the affected area was already classified by Z-FIRE as high or very high risk based on Level 1 (L1) neighborhood scores.

Notably, none of the impacted areas were categorized as “Very Low Risk,” a strong validation that Z-FIRE captured the inherent vulnerability of these communities well before ignition.

Looking at Level 2 (L2) property-specific scores, the correlation between predicted risk and actual destruction became even clearer.

Structures with the highest Z-FIRE risk scores were 50% more likely to be destroyed compared to those with the lowest scores in the same fire footprint.

These destruction rates align with the model’s fundamental architecture: properties with denser surrounding structures, minimal defensible space, and combustible fuels nearby face a dramatically higher likelihood of loss during a wildfire.

The model identified risk in this community not simply based on topography or vegetation, but because of its underlying urban structure, including building materials, spacing between homes, and neighborhood density. These are the very factors that drive vulnerability to conflagration.

Z-FIRE was designed to capture these dynamics, reinforcing its value as a forward-looking tool for rating, underwriting, and mitigation planning.

A Model Informed by What’s Happening on the Ground

Wildfire conditions are constantly changing, and Z-FIRE stays current by continuously incorporating the latest ground-truth data, including updated fire perimeters, confirmed loss locations, and vegetation conditions.

While the core model remains stable and fully approved for rating and underwriting, this steady stream of fresh data ensures that Z-FIRE reflects the most recent fire seasons and emerging risk patterns.

The 2023 and 2024 fire seasons reinforced what we’ve long understood: fire behavior is increasingly shaped by the built environment. Z-FIRE continues to perform as expected across a wide range of scenarios, including structure-to-structure ignition in densely built areas.

These results reaffirm the model’s ability to provide timely, reliable insights to support carrier decision-making in a rapidly evolving risk landscape.

Available Now, for Those Who Need It Most

For carriers looking to better understand and underwrite wildfire risk, whether in traditional WUI zones or increasingly vulnerable urban neighborhoods, Z-FIRE offers a tested, approved, and field-proven solution.

‍Built on over a decade of confirmed loss data and designed to capture the drivers of urban conflagration, Z-FIRE supports smarter decisions across pricing, underwriting, mitigation, and reinsurance.

Importantly, Z-FIRE is approved for underwriting and rating by Departments of Insurance (DOIs) across all western states, with Oklahoma recently added to the list. Carriers can deploy the model today with confidence, knowing it meets regulatory standards while delivering granular, property-specific insights to support risk selection, pricing, and mitigation strategies.

If you're looking to validate wildfire risk insights on your own book of business, we invite you to put Z-FIRE to the test. Our team can run a targeted evaluation to show how the model performs across your portfolio, highlighting risk segmentation opportunities and identifying properties most vulnerable to structure-to-structure spread.

Get in touch to schedule an evaluation and see how Z-FIRE can strengthen your wildfire strategy today.

ZestyAI today announced the launch of ZORRO Discover™, a generative AI agent that delivers instant insights into regulatory filings and competitive market activity.

Solving a $2.5 Billion Regulatory Research Problem

Regulatory research remains one of the costliest inefficiencies in insurance, with an estimated $2.5 billion spent annually on manual reviews. The process consumes millions of hours across fragmented workflows, as filings often stretch into the thousands of pages, slowing competitive analysis and strategic decision-making.

To solve this, ZestyAI built ZORRO Discover on a proprietary generative AI pipeline, leveraging large language models optimized for insurance-specific content, including filings, objections, responses, and regulations from all 50 states.

Using agentic AI, the platform proactively navigates and synthesizes regulatory data, delivering a fast, intuitive experience tailored to the structure and language of insurance filings.

Accelerating Research by 95%

Early adopters of ZORRO Discover have found that insurers can cut research time by an average of 95%, saving thousands of work hours and millions in costs.  

“For too long, regulatory filings have buried critical intelligence under layers of complexity,” said Kumar Dhuvur, Founder and Chief Product Officer of ZestyAI. “Through years of working closely with regulators, we’ve come to deeply understand these challenges.”

“ZORRO Discover puts that intelligence at insurers’ fingertips so they can act faster, stay compliant, and make smarter decisions in an increasingly competitive market.”

How Teams Use ZORRO Discover

ZORRO Discover can be used by compliance, actuarial, product, and strategy teams to accelerate competitive research and reduce regulatory risk. By replacing manual reviews with a consistent, repeatable process, the platform helps teams improve research accuracy, focus on high-impact work, and stay ahead of regulatory changes.

Key Capabilities

• Plain-language search: Analyze and compare filings across carriers, states, and time periods using intuitive queries—no deep regulatory or technical expertise required.
• Zero in on critical details instantly: Transform complex filings into executive-level insights for faster, more informed decision-making.
• Spot market opportunities early: Track shifts in rates, rules, forms, and guidelines to anticipate industry trends and act proactively.
• Run side-by-side comparisons: Evaluate pricing strategies, regulatory objections, or model usage over time and across competitors. Example: Compare windstorm mitigation discounts across three major carriers in Florida.

Expanding to Additional Lines of Business

ZORRO Discover launches with a focus on homeowners insurance and is rapidly expanding to support regulatory and competitive intelligence across auto, commercial, and additional lines of business.

Request early access to ZORRO Discover here.

The Arizona Department of Insurance and Financial Institutions has approved the use of ZestyAI’s Severe Convective Storm suite, including Z-HAIL™, Z-WIND™, and Z-STORM™, giving insurers access to property-level risk insights.

Rising Storm Losses Across Arizona

In Arizona, the most frequent and damaging thunderstorm losses stem from strong, straight-line winds, especially downbursts. These sudden, localized wind events can exceed 100 mph and often cause significant roof and structural damage, particularly in vulnerable homes.

In 2024 alone, Arizona experienced 67 days of severe storm events, according to NOAA. These included high winds, large hail, tornadoes, and damaging downbursts, highlighting the growing volatility of the state’s monsoon season and the increasing threat to both rural and urban communities.

How ZestyAI’s Models Improve Precision

ZestyAI’s models are designed to address these risks. By analyzing how local climatology interacts with individual property features, the platform predicts both the likelihood and severity of storm-related claims, offering a more accurate alternative to traditional ZIP code or territory-based approaches, which often miss critical risk signals.

Each model is trained and validated on extensive real-world claims data and provides clear, transparent explanations of the key factors driving each score, enabling more confident underwriting and rating decisions.

Key Model Capabilities

• 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 claim vulnerability and severity.
• Z-STORM: Predicts the frequency and severity of storm damage claims, including hail and wind, examining the interaction between climatology and the unique characteristics of every structure and roof.

Regulatory Confidence in AI-Driven Models

Bryan Rehor, Director of Regulatory Affairs at ZestyAI said,

“Arizona is a key market for many of our carrier partners, and one of the most rigorous regulatory environments for model review. This approval is a strong signal of confidence in our technology and our commitment to transparency, precision, and regulatory alignment.”

Arizona marks the 19th state to approve ZestyAI’s Severe Convective Storm suite—and the fourth model approved in Arizona overall, joining Z-FIRE™, ZestyAI’s AI-powered wildfire risk model already in use across the state. With elevated risk across multiple perils, Arizona’s adoption of ZestyAI’s models reflects growing regulatory confidence in AI-driven models that enable smarter underwriting, precise pricing, and stronger resilience in the face of extreme weather.

At our 2025 offsite, we had the rare privilege of hosting Scott Stephenson, former Chairman, President, and CEO of Verisk, for a candid fireside chat with our CEO, Attila Toth.

More than a guest speaker, Scott has been a mentor to our founders and a steady source of insight and encouragement on our journey. 

Over two decades, he led Verisk through extraordinary growth: doubling revenue, quadrupling market cap, and shaping one of the most respected analytics companies in the world. Named one of Forbes’ Most Admired CEOs, Scott helped define the modern insurance ecosystem.

But what stayed with our team long after the session wasn’t the accolades. It was his clarity, humility, and deep conviction that great companies aren’t defined by vision—they’re defined by execution.

Here are three takeaways that stuck with us:

Trust is a survival skill

Scott opened with the story of the 1949 Mann Gulch wildfire, where only 3 of 16 elite smokejumpers made it out alive. The team leader lit an escape fire and lay down inside it. He shouted for the others to follow, but no one did.

“They were a team on paper,” Scott said, “but they’d never trained together. They didn’t trust each other enough to act as one.”

The lesson? In high-stakes environments, technical talent means little without trust. Success depends on how quickly and cohesively a team can move together.

Thinking isn’t doing

Smart teams often confuse planning with progress. As Scott put it:

“Thinking about something, even deeply—and talking about it with others—feels like you’re doing the work. But you’re not.”

Insight without execution is just potential energy. The companies that thrive aren’t the ones with the most ideas; they’re the ones that ship, iterate, and improve with discipline.

Leadership is a mindset

Scott began his career as an engineer. Leadership wasn’t on his radar—until curiosity led him to explore it. His message: leadership isn’t a job title, and it’s not reserved for MBAs. It belongs to those who take responsibility, act with principle, and stay grounded in reality.

Why it mattered

Scott didn’t come to ZestyAI to celebrate the past. He came to challenge us. To remind us that in an industry facing rising losses, regulatory pressure, and rapid change, progress isn’t driven by buzzwords or strategy decks. It’s driven by clarity, trust, and the discipline to do the hard work.

That message hit home. As we scale ZestyAI, our ambition isn’t just to innovate; it’s to lead with purpose. To build the new standard for risk analytics. One that helps insurers price with precision, expand access to coverage, and strengthen the resilience of entire communities.

ZestyAI, the leading provider of AI-driven risk analytics for the property and casualty insurance industry, today announced it has secured a $15 million credit facility from CIBC Innovation Banking.

The facility enhances ZestyAI’s financial flexibility and reinforces its strong balance sheet as the company scales adoption of its climate and property risk models, delivering over 31 million property risk assessments in 2024, more than double the volume from 2023, and on pace to exceed 50 million in 2025.

“We’ve built ZestyAI with long-term discipline and a clear mission: to help insurers navigate changing risks with confidence,” said Attila Toth, Founder and CEO of ZestyAI.

“As demand for trusted, property-level risk insights continues to grow, this capital infusion enables ZestyAI to further invest in supporting this rapid growth and become the industry standard for property risk analytics."

Strengthening ZestyAI’s Position for Rapid Growth

Capital that supports scale, product expansion, and customer demand

“ZestyAI is addressing a critical need in the insurance industry with its AI-powered approach to property and climate risk,” said Sean Thompson, Managing Director, California Market Manager at CIBC Innovation Banking.

“We’re proud to support ZestyAI’s next phase of growth as it continues expanding its platform and deepening its impact across the insurance ecosystem.”

ZestyAI’s footprint has expanded rapidly across the insurance landscape, with growing adoption among admitted carriers, MGAs, the E&S market, FAIR Plans, and public-private partnerships. Over the past year, the company secured 33 new and expanded customer partnerships, including 26 brand-new clients, and launched four new products.

Accelerating Regulatory Momentum Nationwide

This growth has been matched by accelerating regulatory traction. ZestyAI’s AI-driven models for wildfire, severe convective storms, and non-weather water damage have now been approved in over 50 regulatory filings nationwide, with recent approvals in Texas, Colorado, Ohio, Georgia, Connecticut, Michigan, North Carolina, Louisiana, and Oklahoma.

Recently reviewed by the California Department of Insurance (CDI) in the rate application process, Z-FIRE™, ZestyAI’s wildfire model, can continue to be filed for rate segmentation and underwriting without further review under the Pre-Application Required Information Determination (PRID) process, effective January 2, 2025.

Fueling a Mission to Expand Access to Coverage

Built at the intersection of property data, climate science, and AI, ZestyAI’s platform delivers precise, parcel-level risk insights that help insurers strengthen underwriting, price accurately, optimize inspections, and expand coverage, especially in catastrophe-prone regions. In 2024, ZestyAI helped carriers and insurers of last resort extend coverage to over 511,000 properties previously deemed uninsurable, advancing its mission to protect the livelihoods of homeowners, business owners, and their communities.