Reports & Research

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's analysis revealed that more than 12.6 million U.S. properties are at high risk of hail-related roof damage, representing $189.5 billion in potential replacement costs.

Powered by ZestyAI’s Z-HAIL™ model, the analysis underscores the growing financial threat of severe convective storms (SCS), including hail, tornadoes, and wind events. In 2024 alone, damages from SCS were estimated at $56 billion—surpassing losses from hurricanes.

Yet many insurers still rely on traditional models designed to estimate portfolio-level exposure, not property-level risk. As hail events increase in severity and frequency, these models often miss the structural and environmental conditions that drive real losses.

Kumar Dhuvur, Co-Founder and Chief Product Officer at ZestyAI said:

“Catastrophe models have helped insurers understand where storms may strike and how losses might add up at a portfolio level. But they weren’t built to assess risk at the individual property level, and they often miss the specific conditions that drive hail damage. By analyzing the interaction between structure-specific features and local storm patterns, we can distinguish risk between neighboring properties—enabling smarter underwriting, more precise pricing, and better protection for policyholders.”

Z-HAIL evaluates hail risk using a proprietary blend of climate, aerial, and property-specific data. By applying advanced machine learning to these inputs, Z-HAIL delivers highly granular predictions that reflect both the physical characteristics of a structure and the storm activity in its immediate surroundings.

Key findings from the analysis:

• 12.6 million U.S. structures flagged as high risk for hail-related roof damage
• $189.5 billion in total potential roof replacement exposure

Top five states by dollar exposure:

• Texas ($68B)
• Colorado ($16.7B)
• Illinois ($10.8B)
• North Carolina ($10.4B)
• Missouri ($9.5B)

States with the lowest dollar exposure:

• Maine ($4.7M)
• Idaho ($12.8M)
• New Hampshire ($18.5M)
• Nevada ($49.3M)
• Vermont ($64.7M)

In recent case studies, Z-HAIL has demonstrated the ability to pinpoint which properties are most susceptible to hail damage—even within the same neighborhood and exposed to the same storm. In one example from Allen, Texas, following a storm with 2.5-inch hailstones, Z-HAIL segmented risk across 483 policies, identifying no losses among properties rated “Very Low” by the model. This level of intra-territory precision gives insurers the ability to refine risk selection with confidence—even in the most hail-prone regions of the country.

Severe convective storms are becoming more frequent and costly, putting pressure on insurers to refine underwriting and risk management strategies. 

On April 2, our experts covered:

• Key drivers behind increasing severe storm losses
• What La Niña means for the 2025 season
• How AI-powered risk models improve risk segmentation
• Live Q&A – Get expert answers to your toughest questions!

Missed the live event? Stream now! 

Severe convective storms (SCS)—including tornadoes, hail, and damaging wind events—resulted in $58 billion in insured losses across the U.S in 2024.

Insurers face a dual challenge: navigating the uncertainty of storm patterns while ensuring their portfolios remain resilient enough to absorb the financial strain from clustered, high-loss events.

Research with IBHS confirms that SCS damage accumulates over time, particularly affecting rooftops after multiple exposures to intense storm activity. As housing stock deteriorates, insurers must reassess their portfolios to ensure underwriting, rating, and loss cost controls align with their risk appetite and maintain premiums that accurately reflect evolving exposure.

Get ahead of rising storm risks with expert insights that help you strengthen underwriting, risk assessment, and claims management.

Get our new report.

A staggering $2.15 trillion worth of U.S. residential property is at high risk of wildfire damage, according to a new AI-powered analysis from ZestyAI, the leader in climate and property risk analytics. The study, which assessed 126 million properties nationwide, found that 4.3 million individual homes face heightened wildfire risk—far beyond traditionally recognized high-risk areas.

Using advanced AI models trained on over 2,000 historical wildfires, ZestyAI mapped wildfire exposure at the property level, integrating satellite and aerial imagery, topography, and structure-specific characteristics. While California leads the nation with $1.16 trillion in wildfire-exposed property, other states such as Colorado ($190.5 billion), Utah ($100.3 billion), and North Carolina ($71.2 billion) also face significant risk.

Wildfire Risk is a Nationwide Challenge

While the Western U.S. has historically seen the most severe wildfire activity, ZestyAI’s findings confirm that high-risk properties exist across the country. States like North Carolina (4.6% of homes at high risk), Kentucky (2.9%), Tennessee (2.3%), and even South Dakota (11.0%) are now seeing increased wildfire exposure.

As more homes and businesses are built in fire-prone landscapes, the Wildland-Urban Interface (WUI) continues to expand. This, combined with intensifying climate conditions, is driving higher insurance costs and growing availability concerns. Today, one in eight U.S. homeowners already lacks adequate insurance coverage, and that number is expected to rise.

AI Expands Insurance Access in High-Risk Areas

Attila Toth, Founder and CEO of ZestyAI said:

"Wildfires are threatening more properties than ever before, with billions of dollars in exposure even in areas many people don’t associate with fire risk. Yet, too many homeowners are finding themselves uninsured or underinsured just as these disasters become more frequent and severe. Insurers have traditionally relied on broad, regional models that don’t account for individual property characteristics."

"That means some homeowners are denied coverage even when their true risk is much lower than their neighbors'.’"

AI-driven risk analytics are reshaping the way insurers assess wildfire exposure. By providing granular, property-specific insights, we’re helping insurers make smarter underwriting decisions—keeping coverage available in high-risk areas while ensuring that homeowners who take mitigation steps are recognized.

Last year, our models helped insurers extend coverage to 511,000 properties that had previously struggled to secure insurance due to outdated risk models. In 2025, we expect that number to reach a million, ensuring that even in high-risk areas, responsible homeowners have access to protection when disaster strikes.

Artificial intelligence is reshaping the P&C insurance industry, offering new ways to streamline underwriting, enhance risk management, and navigate evolving regulations.

But as AI adoption accelerates, insurers must ensure they’re using these technologies effectively—balancing innovation with compliance.

Our latest guide explores the most impactful AI applications in insurance, including:

• AI-powered underwriting and predictive analytics
• How regulators are shaping the future of AI in insurance
• Best practices for integrating AI while ensuring fairness and transparency

As AI-driven tools become the new standard, insurers who adapt early will gain a competitive edge.

Download our free guide to leverage these innovations while staying aligned with evolving regulations.

After years of working with leading insurers, one reality has become undeniable: enterprise data quality is one of the biggest hidden risks in the industry. 

The Problem: Carriers Don’t Trust Their Own Data

Data enters the system at the quote stage. That means it often comes directly from agents and policyholders—well-intentioned, but subjective. Did the policyholder really know the exact roof age? Did the agent catch the secondary structures in the backyard?

Inspection resources are limited, and most carriers can’t validate this information at scale. The result is a house of cards: data that looks complete in the policy system, but is riddled with blind spots and inconsistencies.

And even when the data is accurate in the moment, it quickly decays. Structures are living assets, meaning:

• Roofs degrade.
• Weather events roll through.
• Families expand, renovate, and change how they use the property.
• Secondary structures, pools, trampolines, and solar panels appear overnight.

The underwriting file that was “clean” at binding can be outdated and incomplete by renewal. Over time, carriers lose confidence that they have a real view of risk. 

The Six Dimensions of Data Quality

Data quality isn’t one thing—it’s multidimensional. For carriers, the challenge is ensuring property data is:

• Accurate: Correct at the point of use, not just at intake.
• Complete (and unbiased): Captures all risk-relevant details, verified against independent sources so fields aren’t left blank, misstated, or skewed by incentives.
• Consistent: Aligned across systems, from quoting to claims.
• Valid: Structured to meet business and regulatory rules.
• Timely: Refreshed when things change, not years later.
• Unique: De-duplicated, with a single source of truth.

By this standard, most carrier data today is falling short.

From Data Quality to Data Integrity

Data quality is foundational, but it’s only part of the picture. True data integrity comes from combining accurate data with the right context and continuous observability.

That means not just having the right roof age or square footage, but knowing whether that data has changed, and whether it aligns with other signals in the environment. It means having a complete, transparent, and continuously updated picture of every property.

 The ZestyAI Solution: Verified, Transparent Property Data

At ZestyAI, we built our property intelligence platform to solve exactly this problem. By unifying multiple independent data streams—and applying AI to synthesize them—we deliver property data that carriers can trust.

Here’s how we do it:

Imagery + Computer Vision

We ingest aerial, satellite, and terrestrial imagery, then apply over 90 proprietary computer vision models. These models don’t just “see” a property; they interpret it. That means extracting hard-to-get details like:

• Roof condition and penetrations
• Yard debris and overhanging vegetation
• Secondary structures and solar panels

This creates a dynamic, objective record of what’s on the ground, property by property.

Geospatial and Hazard Data

Context matters. We overlay geospatial layers to understand how a property interacts with its environment, including wildfire exposure, flood risk, and more.

Building Permits

Using large language models (LLMs), we extract and synthesize the real changes reflected in building permits: bathrooms added, kitchens renovated, roof replacements, solar installations. Permits reveal what’s changed, not just what was once approved.

Market and Public Records

We enrich the picture with MLS transactions, tax assessments, climatology, topography, and infrastructure data. Together, these data sources confirm and contextualize what imagery and permits reveal.

Standards and Designations

We integrate authoritative designations, such as IBHS Fortified™ standards, to validate resilience features.

Each data source adds a layer of verification. Together, they create a comprehensive, continuously updated property record that carriers can rely on.

 
Why This Matters: Enterprise Data Quality Transformed

When carriers bring ZestyAI data into their systems, the impact is immediate:

• More Accurate Underwriting and Rating: Quote data is validated against independent sources. That means fewer surprises at claim time, more consistent rating, and appropriate premiums.
• Change Detection and Accurate Renewals: Our models detect what’s changed since policy inception, leading to smarter renewal decisions, more proactive outreach to policyholders, and reduced leakage.
• Better Reinsurance Negotiations: Clean, transparent data helps carriers secure the right terms, conditions, and pricing from reinsurers—because they can prove their book is based on verified risk, not guesswork.
• Operational Efficiency: By replacing guesswork and manual inspection with AI-verified data, carriers reduce expenses and focus resources where they matter most.
• More Accurate Customer Communications: Data quality isn’t just about pricing and underwriting. It’s about trust. Verified property details enable carriers to send personalized, timely, and accurate communications.
• Renewal notices, policy updates, or even hurricane prep guidance land with credibility because they reflect the customer’s real property. That strengthens engagement, reduces confusion, and builds long-term retention. 

The Bottom Line

Carriers can’t compete in today’s market with messy, decaying data. Enterprise data quality is no longer a “back office” concern. It’s a competitive edge.

ZestyAI’s property intelligence platform solves the problem at its core: by continuously verifying property data with imagery, geospatial science, permits, and AI-powered interpretation.

That’s how carriers build trust in their data. That’s how they write better risks, renew smarter, negotiate stronger, and communicate with customers more effectively. 

Want to see how ZestyAI can transform your enterprise data quality? Contact us for a demo.

ZestyAI today announced a new partnership with Applied Home National Underwriters to adopt ZestyAI’s full suite of regulatory-approved models and property insights in support of more accurate underwriting, pricing, and portfolio management across its U.S. property insurance operations.

This includes peril-specific models for non-weather water (Z-Water™), wildfire (Z-Fire™), hail (Z-Hail™), wind (Z-Wind™), and severe convective storm (Z-Storm™). It also includes property insights that combine aerial imagery, building permits, and parcel-level data to identify risk-relevant features such as roof condition, vegetation overhang, and secondary structures.

ZestyAI’s models use proprietary AI trained on billions of data points, including aerial imagery, parcel-level attributes, building permits, climatology, and real-world loss data, to deliver precise, property-specific risk intelligence that supports confident risk selection, loss ratio improvement, and more robust concentration management across perils.

Brian Voorhees, Chief Operating Officer, Applied Home National Underwriters, said:

“ZestyAI’s AI-powered risk models offer the kind of granular, verified intelligence that strengthens risk evaluation across a broad spectrum of perils, from climate-related threats to non-weather water.”

“This partnership deepens our commitment to innovation and precision in underwriting and portfolio management.”

“Applied Home National Underwriters is known for taking on complex risks with clarity and conviction,” said Attila Toth, Founder and CEO of ZestyAI. “We’re proud to support that with trusted, property-specific insights that help sharpen underwriting, align pricing with true risk, and strengthen portfolio performance.”

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.

ZestyAI has secured regulatory approval to use its AI-powered Severe Convective Storm models in underwriting and rating in Nebraska and Kansas. The move greenlights Z-HAIL™, Z-WIND™, and Z-STORM™, for use in property-level hail and wind scoring, giving carriers more transparency and precision in two of the nation’s most storm-prone states.

2024 Loss Trends Highlight Urgency

Kansas recorded 495 major hail events (≥1 inch) in 2024, the second-highest total of any U.S. state. Nebraska saw 100 confirmed tornadoes, the most in over 20 years. According to 2024 NAIC reporting, Nebraska posted the highest loss ratio of any state at 135.74%, while Kansas ranked 13th at 67.98%, evidence of the severe financial pressures facing insurers in the region.

Why Property-Level Intelligence Matters

ZestyAI’s Severe Convective Storm models analyze the interaction between localized climatology and property-specific characteristics, such as roof condition, design, and complexity, to predict the likelihood and severity of hail, wind, and storm damage claims. In contrast, many models still rely on broad ZIP code or territory-level risk assessments, missing critical property-level 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.

Capabilities of the ZestyAI SCS Suite

• Z-HAIL: Predicts hail damage risk and claim severity using property-specific attributes such as roof complexity and accumulated damage to identify which homes are most likely to file a claim, even within the same neighborhood.
• Z-WIND: Analyzes AI-generated 3D roof condition, complexity, and potential failure points alongside local climatology to deliver pivotal insights into property-specific wind claim vulnerability and severity.
• Z-STORM: Assesses 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.

“Securing approval in the storm epicenter of the U.S. reflects both the transparency of our models and our alignment with rigorous regulatory standards,” said Bryan Rehor, Director of Regulatory Affairs at ZestyAI.

“It gives carriers the confidence to use precise, property-level hail and wind insights where they’re needed most, supporting risk-aligned underwriting and pricing in markets facing escalating storm losses.”

ZestyAI’s Severe Convective Storm models are now approved for use in over 20 states across the Great Plains, Midwest, and U.S. South—regions most impacted by hail, wind, and tornado losses.

VYRD, a Florida-based homeowners insurance company focused on delivering stability and protection in a challenging coastal market, has partnered with ZestyAI, the leading provider of AI-powered property risk analytics, to gain deeper visibility into property condition and risk exposure across its book of business.

By leveraging the ZestyAI platform, VYRD gains access to advanced risk insights that help identify vulnerabilities contributing to hurricane-related losses, such as roof degradation, overhanging vegetation, and yard debris. These insights support more accurate underwriting, proactive portfolio management, and better visibility into changing property conditions over time.

VYRD is using two core capabilities of Z-PROPERTY: Digital Roof applies AI to high-resolution aerial imagery to assess roof complexity, materials, and condition, highlighting structural vulnerabilities before they become claims. Location Insights evaluates the broader parcel, surfacing risk factors like vegetation overhang, yard debris, and secondary structures that can amplify storm losses or drive claims severity.

“Staying ahead of risk requires strong partnerships and smarter data,” said David Howard, President and CEO of VYRD.

“ZestyAI’s ability to deliver timely, property-level insights helps us strengthen our understanding of exposure across the homes we protect and continue delivering on our promise of dependable coverage for Florida policyholders.”

Z-PROPERTY helps VYRD assess properties at scale, surface emerging risk patterns, and make more informed decisions across the policy lifecycle. This partnership extends VYRD’s tech-forward, policyholder-first strategy—using trusted data not just at point-of-quote, but throughout ongoing portfolio management.

“Florida is one of the most unforgiving insurance markets in the country,” said Attila Toth, Founder and CEO of ZestyAI. “VYRD is taking a proactive, data-driven approach—using AI to uncover hidden property-level vulnerabilities, strengthen portfolio decisions, and build resilience where it matters most.”

Property insurers are under growing pressure to do more with less. The risks are increasing, regulatory expectations are rising, and the margin for error keeps shrinking. In regions where wildfire, hail, and wind are intensifying, the question becomes even more urgent: how can carriers continue writing business in high-exposure areas while managing loss ratios and navigating compliance?

The challenge becomes even more complex when the homes involved are in remote locations. These properties are often far from city infrastructure, difficult to inspect, and lack consistent, structured data. That’s the reality one regional carrier faced.

Their book included thousands of rural properties, many of them manufactured homes on private land across states like Texas, Arizona, and New Mexico, as well as other regions they serve nationwide. Each presented its own underwriting hurdles, from ambiguous fire protection data to increased storm exposure and aging roof systems.

To remain active in these areas and grow responsibly, the team knew they needed a more precise way to evaluate risk at the individual property level. Broad rules were no longer enough, and legacy models designed for urban density offered little support.

The Manufactured Home Challenge: Rural, Exposed, and Hard to Inspect

Standard Casualty has specialized in insuring manufactured and modular homes for decades, serving policyholders across a wide range of states, including Texas, Arizona, and New Mexico. It’s a segment that brings unique challenges, particularly when it comes to visibility and data.

“Many of the properties we insure are located in rural areas with little to no street-level data,” said Rick Smith, Underwriting Manager at Standard Casualty.

“Some are difficult to geocode. Others are so remote that a physical on-site inspection can be challenging or expensive.”

In states like Arizona and New Mexico, many homes fall within or near the wildland-urban interface (WUI), where wildfire exposure is increasing. In Texas, hail and wind are persistent threats. And across all markets, aging structures and outdated roof data complicate rating and eligibility decisions.

The team recognized they couldn’t rely solely on fire zones or broad peril maps. They needed more nuanced, property-specific data to support confident decision-making at scale.

Getting the Full Picture, Even Without a Site Visit

“One of the big innovations for us is the availability of aerial photography,” Smith explained. “We’re seeing a lot more of our risks located in remote areas. They’re either very difficult to find or it’s very expensive to do an on-site inspection. Aerial photography really solves that problem for us.”

Using ZestyAI’s platform, the team now accesses high-resolution aerial and oblique imagery, property-specific peril scores, and roof condition insights, without always requiring physical inspections.

ZestyAI works with all the major aerial imagery providers, giving underwriters access to a broader range of coverage and more recent images than any single source could offer. That means more properties are visible, more clearly, and more often.

“We can actually accomplish through aerial photography what we can do through an on-site inspection, at a fraction of the cost, which is really important for us."

If a structure is misidentified or not aligned, underwriters can reposition the property tag themselves.

“With the ZestyAI platform, it’s very easy."

We can simply move the identifier tag with our mouse onto the correct building, click a button, and it automatically reruns the scoring and the evaluation,” Smith said.

Aerial views also improve their ability to assess roof condition and wildfire vulnerability. “Sometimes the current photo may have been taken late in the day, and there are shadows on the roof. We can back up to a photo taken six months earlier and get a clearer picture of what’s going on.”

Risk Differentiation at the Parcel Level

ZestyAI’s approach stood out by combining hazard probability with structure-specific vulnerability.

“For every peril — wildfire, hail, or wind — we now get two things,” said Smith. “First, the probability of the event occurring at that location. Second, the expected severity if it does."

"That allows us to say: these two homes are in the same neighborhood, but only one is truly high-risk.”

This dual scoring model,  based on climate science and property characteristics, allows the team to evaluate each property on its own merits. “That kind of granularity is really a game changer for us in terms of underwriting,” Smith added.

Beyond Underwriting: A New Frontier in Rating and Renewal

ZestyAI’s insights also support dynamic pricing and renewals. Rather than relying on static or annual updates, risk scores adjust continuously based on the most recent information available — from new aerial imagery and roof condition changes to building permits and property improvements.

“We’re not just using the model once and forgetting it,” Smith said. “The scoring gets augmented, and as a result, the premium gets augmented as well. That means our pricing reflects current risk — not just what we assumed at inception.”

Because the models are approved for use in both underwriting and pricing, Standard Casualty can incorporate granular risk insights into its rating approach, streamlining the process of keeping rates aligned with actual conditions.

“It would take months of actuarial and analytical work to try to get to that level of granularity. And this is happening instantaneously as we renew our business.”

Retention Through Mitigation: A Smarter Approach to Non-Renewals

While many insurers are pulling back or issuing non-renewals in high-peril regions, Standard Casualty has taken a different approach: empowering policyholders to reduce their risk and stay insured.

“We can run the property eight months ahead of renewal,” Smith explained. “If we see wildfire vulnerability, we don’t just cancel. We notify the policyholder, show them what needs to change — defensible space, roof repairs, vegetation removal — and give them a chance to improve. That’s how we retain the risk and still operate responsibly.”

This proactive strategy improves retention, builds customer trust, and advances the company’s mission: improving lives by providing affordable coverage.

“This is the direction we need to go as an industry,” Smith emphasized. “We need to help policyholders understand the importance of mitigation and give them guidance so their costs — and everyone’s losses — are reduced.”

Built to Scale: Regulatory Readiness That Grows With You

One often overlooked barrier to adopting new risk models is regulatory complexity. For Standard Casualty, this was a key factor in choosing ZestyAI.

“We looked at a lot of vendors,” said Smith.

“But ZestyAI stood out because they were already approved in the states we care about and had a clear filing strategy for others.”

That alignment means the team can expand into new markets without rebuilding processes from scratch. The same tools and workflows that support Texas can be applied in Arizona, New Mexico, or future states, streamlining both operations and compliance.

“The Right Tools. The Right Decisions. The Right Time.”

As carriers navigate mounting loss pressure, tighter margins, and rising expectations from regulators and policyholders alike, Smith believes one thing will separate those who retreat from those who adapt:

“ZestyAI gives us the right tools to make the right decisions at the right time. That’s what allows us to write in places other carriers are leaving. That’s how we grow profitably, even in high-risk territories.”