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.

                           The property and casualty (P&C) insurance industry has always been about protecting people and property against the unexpected. But in 2024, U.S. insured catastrophe losses reached $113 billion—nearly double the 25-year average of $58 billion.

For many carriers, the gap between collected premiums and total payouts, including operating costs, continues to widen, underscoring the growing financial strain of climate-driven risks. At ZestyAI, we can’t control the forces of nature, but we’re helping insurers adapt.

From Franklin’s Fire Policies to AI-Powered Resilience

The concept of insurance dates back to the Middle Ages, when merchants sought protection from unpredictable events like storms and piracy. Over time, the practice evolved into today’s property and casualty insurance, safeguarding homes, businesses, and communities.

In the United States, Benjamin Franklin advanced the industry by founding the nation’s first fire insurance company in 1752. By refusing to insure fire-prone buildings, his company not only mitigated risk but also set new safety standards. This principle of risk reduction has guided the industry ever since.

Today, ZestyAI is building on that foundation with AI-driven risk models that help insurers address modern challenges. While the tools have changed, the mission remains the same: to protect people, property, and the future.

Modern Tools for Today’s Challenges

At ZestyAI, we’re helping insurers address modern challenges with solutions designed to fit seamlessly into their workflows. Our AI-driven platform combines high-resolution aerial imagery, proprietary data, and advanced modeling to provide a clearer, more reliable view of risk.

Unlike broad, traditional approaches, we focus on delivering actionable insights at the property level, helping insurers:

• Modernize outdated processes without overhauling their systems,
• Improve underwriting precision,
• Optimize inspections and resources, and
• Strengthen their portfolios.

A key example of this is our work in wildfire risk modeling. ZestyAI’s proprietary wildfire loss database—built using data from 1,500 events over the last 20 years—helps insurers predict property-specific risks with precision. By understanding wildfire risk at this granular level, insurers can proactively reduce losses, guide mitigation strategies, and protect their customers with greater confidence.

Discover how ZestyAI’s regulator-approved solutions set new standards for accuracy, compliance, and fairness in insurance.

ZestyAI, the leading provider of AI-powered property and risk analytics, today announced that its Severe Convective Storm suite, including Z-HAIL™, Z-WIND™, and Z-STORM™, has received regulatory approval from the Minnesota Department of Commerce.

This milestone supports Minnesota insurers in improving storm risk assessment, enhancing underwriting precision, and supporting proactive risk management strategies.

Minnesota has seen significant losses from severe convective storms. According to data from NOAA’s National Centers for Environmental Information (NCEI), the state experienced three billion-dollar weather events in 2024 alone, with hail and wind causing extensive damage. A July storm in the Twin Cities resulted in more than $1.8 billion in insured losses, highlighting the need for innovative solutions to manage storm-related risks.

ZestyAI’s Severe Convective Storm suite delivers property-specific risk insights by combining climatology analysis with granular property data. Built on extensive loss data and validated by regulatory authorities, the suite equips insurers to assess and address storm risks with a higher level of accuracy and confidence. Key features include:

• Z-HAIL: Evaluates each roof’s unique characteristics, including accumulated damage, to predict which properties are likely to file a claim, even in the same neighborhood.
• Z-WIND: Uses AI-generated 3D analysis revealing pivotal insights about roof condition, complexity, and potential points of failure.
• 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
  These models allow insurers to move beyond reactive damage assessments, helping them identify high-risk properties, allocate resources effectively, and support policyholders in reducing risks.

Bryan Rehor, Director of Regulatory Affairs at ZestyAI said:

“Minnesota’s exposure to hail and wind damage underscores the importance of property-specific insights. With this approval, insurers can access validated models to deliver precise underwriting and rating decisions and encourage risk-reduction measures among policyholders.”

This approval builds on a series of regulatory endorsements in key wind and hail-prone states across the Great Plains, Midwest, and U.S. South, including Texas, Colorado, Indiana, Missouri, and Iowa, among others.

National insurance carriers have long relied on sophisticated models to drive underwriting accuracy and profitability.

But even the most advanced systems can benefit from fresher, more granular, and unique data inputs.

That’s where ZestyAI comes in. With 97%+ U.S. property coverage and exclusive data points—like roof condition and building permits—ZestyAI provides national carriers with the data needed to supercharge their existing models and achieve unparalleled accuracy in risk assessment.

Every insurance model thrives or falters based on the quality of its inputs. Using computer vision and AI-powered insights, ZestyAI captures property-specific features with unmatched precision and is updated multiple times annually. This allows carriers to access insights they’ve never had before, including:

• Roof Condition and Complexity: ZestyAI’s 3D analysis evaluates every facet, penetration, and angle of a roof, providing a complete view that powers complex rating models.
• Parcel-Level Features: Detailed property-level data, such as driveway condition, building permits, lot debris, and overhanging vegetation, reveal nuanced risks for underwriting.
  Climate, Geography, and Infrastructure Variables: Comprehensive data encompassing topography, slope, climate factors, and critical infrastructure.

Why National Carriers Choose ZestyAI as a Data Partner

Adopting an off-the-shelf solution isn’t always the right fit for national carriers with robust internal modeling teams. Instead, these insurers benefit from ZestyAI’s ability to integrate powerful datasets into their existing infrastructure seamlessly. Key advantages include:

• Data Uniqueness: Proprietary insights like property updates and nuanced parcel-level conditions unavailable from public or conventional sources.
• Broad and Deep Coverage: 97%+ U.S. property coverage ensures nationwide relevance.
• Change Detection and Data Recency: AI-driven updates keep your models ahead of evolving risks with near-real-time insights.

By integrating ZestyAI’s data, carriers can complement their models and ensure their outputs are informed by the latest, most comprehensive property-level information.

Driving Precision with a Science-Driven Approach 

At ZestyAI, science and a hypothesis-driven approach form the foundation of our offerings. Hundreds of variables are tested for each model, carefully selected and validated to ensure they meet both logical and causal standards—not just correlations. This rigorous methodology ensures compliance with regulatory scrutiny and real-world risk prediction.

Immediate Benefits, Long-Term Value

• Enhanced Model Accuracy: ZestyAI’s data serves as a diagnostic lens, revealing what’s missing in your existing frameworks and sharpening predictions.
• Operational Efficiency: Recent upgrades to ZestyAI’s API infrastructure, including a 50% reduction in response times and a 10x increase in data processing power, ensure carriers can seamlessly integrate real-time insights into their workflows. These enhancements enable faster decision-making and improved scalability, helping insurers stay ahead in an evolving risk landscape.
• Regulatory Readiness: Transparent, explainable data sources ensure compliance with even the most stringent underwriting regulations.

ZestyAI, the leader in AI-powered property and climate risk analytics, today announced a partnership with THORE Insurance, a Texas-based company.

Johnathan Yazdani, President and CEO of THORE Insurance, said:

"After evaluating several options, ZestyAI was the clear choice for our underwriting needs. Far too often, our industry suffers preventable, foreseeable losses and chalks it up to a cost of doing business. No more. Zesty's comprehensive property insights and roof age solution stood out, offering the precision and scalability we need to grow our business in Texas year over year and maintain low prices for our members through underwriting excellence."

"Their data-driven insights and depth of 40+ property features made the decision easy for us, and we’re confident ZestyAI will be a key partner as we build for the future."

ZestyAI’s Roof Age insights, derived from building permits, aerial imagery, and advanced AI analysis, provide 97% data coverage across the U.S., addressing inaccuracies in self-reported roof data.

‍Recent research shows that 15% of roofs are at least eight years older than reported, highlighting the need for reliable, data-driven solutions.

The Digital Roof™platform uses AI-generated 3D analysis to assess roof attributes like condition, complexity, and potential failure points, while Z-PROPERTY™ Location Insights identifies property features such as vegetation overhang, swimming pools, and solar panels. Together, these capabilities deliver deeper insights to refine risk assessment and pricing.

“In addition to their appetite for innovation, THORE’s leadership clearly communicated a vision to serve Texas homeowners with fairly-priced, best-in-class insurance products,” said Sebastian Kasza, Director of Strategy and Business Development at ZestyAI.

Leveraging AI-powered, property-specific insights in underwriting and pricing is the best way that a carrier can achieve that ambition sustainably.

We are thrilled to partner with Jonathan and his team as they serve the Texas insurance market.”

By Attila Toth, Founder and CEO, ZestyAI 

“SaaS is dead.” 

With these three words, Microsoft CEO Satya Nadella sparked a global debate, challenging the foundation of enterprise operations. His prediction? AI agents will soon replace traditional SaaS workflows, moving business logic to a dynamic AI layer and leaving legacy tools behind. 

For property and casualty (P&C) insurers, this prediction is more than a tech trend—it’s a wake-up call. Carriers have invested heavily in SaaS platforms to modernize underwriting, claims, and risk management. But with AI agents poised to dominate, are these investments like castles built in the sand, vulnerable to the rising tide of AI? 

Let’s unpack what Nadella’s claim means for P&C insurers and explore how to prepare for a future where agentic workflows reshape this $2.6 trillion global industry. 

From Static Systems to Agentic Workflows

Over the past decade, P&C carriers have focused on modernizing their technology stacks, moving
to SaaS-based systems for policy management including underwriting, claims, and billing. These platforms promise efficiency gains, streamlined workflows, and improved business intelligence. Yet Nadella’s vision points to a future where AI agents bypass these systems altogether, directly interact- ing with data to execute tasks dynamically. 

Enter agentic workflows. 

Agentic workflows are powered by AI agents—autonomous systems that can analyze data, make decisions, and execute tasks in real-time without rigid reliance on predefined rules or interfaces. Unlike traditional workflows that depend on user interaction, agentic workflows dynamically adapt to the situation, accessing real-time data and leveraging advanced decision models to solve problems creatively. 

Let’s Break it Down With Examples:

• Underwriting: Traditionally, underwriters rely on policy management systems to assess risk, manually inputting and analyzing data. In an agentic workflow, an AI agent pulls data from internal and external sources, such as property imagery or weather patterns, and evaluates risk in real time, and proposes pricing autonomously.
• Claims: Instead of adjusters triaging claims by reviewing data and making decisions step by step, AI agents analyze First Notice of Loss (FNOL) data, cross-reference it with historical patterns, flag potential fraud, and recommend payouts or next steps—all in seconds. 

Think of agentic workflows as moving from a ‘static map’ to a ‘smart GPS.’ Traditional SaaS systems provide fixed routes, like a printed map or a AAA TripTik, where users must plan and follow a predefined path. In contrast, AI agents function like a GPS that dynamically adjusts to roadblocks or detours, guiding you in real-time to reach your destination more efficiently. 

This doesn’t eliminate the roles of underwriters or adjusters—it amplifies them. With agentic workflows, professionals transition from being data processors to strategic decision-makers, supported by AI agents that execute repetitive and analytical tasks. 

The question is not if, but how fast this shift will occur. In P&C, where SaaS investments are relatively new, the transition may take time. But the direction is unmistakable, and forward-thinking executives should prepare now.

How Insurance Leaders Can Prepare for the AI Era 

The shift to AI-driven workflows brings both challenges and opportunities. To stay ahead, insurance leaders must act now. Here’s how: 

1. Build AI-First Architectures 

Insurers must prioritize modular, API-driven platforms that enable seamless integration with AI agents. An AI-first architecture treats applications as interchangeable layers rather than static end- points, ensuring adaptability to future innovations without extensive system overhauls. 

2. Unify Siloed Data 

AI agents thrive on data, yet fragmented, siloed data remains a significant challenge for insurers. It’s not about choosing the “right” database—AI agents can interact with any data store. What matters is creating a unified and federated data structure that breaks down silos and provides AI agents with a cohesive view of organizational information. 

CIOs should prioritize data integration, ensuring underwriting, claims, customer, and risk data are accessible across the enterprise. A federated approach bypasses the need for lengthy consolidation projects while enabling AI-driven insights. 

3. Engage Regulators Early

AI workflows will only succeed if regulators are on board. Departments of Insurance (DOIs) need to trust the decision-making processes of AI agents and ensure they meet standards for transparency, fairness, and compliance. 

At ZestyAI, we’ve worked with state Departments of Insurance across the U.S. to gain approval for our AI models. Building trust with regulators requires proactive engagement, clear communication, and ongoing education. Insurers that lead in this area will not only gain competitive advantages but also shape the regulatory frameworks that govern the use of AI. 

4. Pilot Agentic Workflows 

Start small, but start now. Deploy AI agents in low-risk areas like claims triage or fraud detection. Early pilots provide valuable lessons and build organizational confidence in agentic workflows. 

5. Expand ROI Thinking 

AI agents are poised to fundamentally transform operations, requiring a broader perspective on ROI. Beyond traditional metrics like cost reduction or workflow efficiency, consider strategic gains such as: 

- Faster speed to market.
- Improved customer satisfaction. - Enhanced risk segmentation. 

6. Put Technology Partners to the Test

Carriers should evaluate their SaaS providers on their readiness to transition to agentic workflows. Ask pointed questions: What is their AI strategy? How do they plan to integrate AI agents into their products? - Are they prepared to support modular, dynamic workflows? 

The Bottom Line 

The future Nadella outlines—a world driven by AI agents—is as disruptive as it is exciting. For P&C insurance executives, it’s a call to action: the technology stack of today may not meet the demands of tomorrow. Preparing now by investing in AI-first architectures, building unified data structures, and engaging with regulators will position insurers to thrive in this new era. 

SaaS isn’t dead yet, but the writing is on the wall. The question is, are you ready to embrace the future? Are you building a castle ready to weather the waves?

Missouri’s severe convective storms are growing more destructive, with hailstorm-related claims skyrocketing by 245% in 2024 alone. To address this rising risk, ZestyAI has secured regulatory approval from the Missouri Department of Insurance for its Severe Convective Storm suite, including Z-HAIL™, Z-WIND™, and Z-STORM™.

Missouri’s Rising Storm Losses

Missouri’s vulnerability to severe convective storms is well-documented. Since 1980, 82 weather events have each caused over $1 billion in damages. In 2024, a March hailstorm—dubbed the “Gorilla Hail” storm—resulted in nearly 7,000 claims, a dramatic increase from just over 2,000 hail claims the previous year.

How ZestyAI’s Models Make a Difference

ZestyAI’s Severe Convective Storm suite provides property-specific risk assessments, enabling insurers to predict and manage extreme weather impacts with precision.

Key Features:

• Z-HAIL™: Identifies a roof’s susceptibility to hail damage and estimates potential claim severity using property-specific attributes like roof complexity and historical losses.
• Z-WIND™: Predicts wind claim frequency and severity by combining climatology with property-specific data such as roof structure and damage history.
• Z-STORM™: Delivers granular risk scores for storm claim frequency and severity, factoring in climatology, building characteristics, and roof design.

These AI-driven models help insurers proactively manage storm-related risks, allocate resources effectively, and encourage policyholders to take preventive measures.

Empowering Insurers with Advanced Risk Insights

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

Missouri’s exposure to tornadoes, hail, and damaging winds makes advanced risk assessment tools essential. By streamlining the regulatory process, we enable insurers to focus on protecting policyholders while reducing losses.

With regulatory compliance and validated loss data at its core, ZestyAI’s suite enables insurers to:

• Enhance underwriting precision and optimize deductible strategies.
• Provide policyholders with actionable insights to reduce risks and prevent losses.
• Move beyond reactive damage assessments to proactive storm risk management.

Looking Ahead

ZestyAI’s Severe Convective Storm suite has already received regulatory approvals in Texas, Colorado, Illinois, Indiana, and Iowa, with additional filings in progress. By equipping insurers with AI-powered tools, ZestyAI is modernizing the way storm risks are assessed, ensuring communities and insurers are better prepared to weather the storm.