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ZestyAI, the leading provider of climate and property risk analytics solutions powered by Artificial Intelligence (AI), announced today its partnership with Donegal Insurance Group® on a project that utilizes its new Roof Age solution for Donegal’s existing Personal Lines book of business.

Through the project, Donegal, a Pennsylvania-based regional insurance carrier, leveraged ZestyAI’s Roof Age solution to populate Homeowner policy renewals where roof age was absent.

ZestyAI’s Roof Age solution pinpoints the age of a roof using data from both building permits and historical imagery going back 20-plus years. This unique approach allows the company to determine the validated age of each roof with over 90 percent accuracy and nearly 100 percent coverage across the contiguous US.

“Accurate roof age information is critical for properly assessing and pricing risk,” said Hank Narvaez, Vice President of Personal Lines Product Development at Donegal. “ZestyAI’s Roof Age solution was a clear choice for us due to its solid coverage length of historical imagery. By leveraging both building permit data and aerial imagery, we gain added confidence in our underwriting and rating decisions.”

“ZestyAI’s Roof Age solution was a clear choice for us due to its solid coverage length of historical imagery. By leveraging both building permit data and aerial imagery, we gain added confidence in our underwriting and rating decisions.”

“Blind spots in assessing property risk can be very costly for insurers,” said Attila Toth, Founder and CEO of ZestyAI. “Roof claims stand as the primary driver of insurance losses, yet many carriers continue to rely on unvalidated roof age information. We are excited to partner with Donegal to enhance their risk assessment with the most accurate roof age solution on the market.”

Roof Age is one part of a complete range of ZestyAI products designed to evaluate roof-related risk. Other solutions include Digital Roof, which creates a digital twin of every structure in the US for unparalleled insights on condition, complexity, and potential points of failure, as well as peril-specific risk models such as Z-HAIL, Z-WIND, and Z-STORM.

Climate Intelligence: How AI Shifts Risk and Redefines the Insurance Landscape

In today’s competitive insurance market, carriers equipped with AI-powered property insights gain a significant edge, enabling them to assess and manage risk with unprecedented accuracy.

By leveraging advanced technologies like artificial intelligence and computer vision, insurers can now analyze property-specific risks with a level of detail that was previously unattainable.

Z-FIRE: Leading the Way in Wildfire Risk Assessment

For example, Z-FIRE, ZestyAI’s leading AI-powered wildfire risk solution, has been widely adopted by carriers across the western U.S., providing them with critical insights to underwrite new business in wildfire-prone areas.

This information advantage drives a phenomenon known as adverse selection, where disparities in risk assessment capabilities lead to an uneven distribution of risk across the market. Insurers without access to advanced tools like Z-FIRE are at a distinct disadvantage, as they continue to underwrite policies based on outdated methods.

Insurers without access to advanced tools like Z-FIRE are at a distinct disadvantage, as they continue to underwrite policies based on outdated methods.

Over time, this imbalance results in a higher concentration of risk among carriers relying on traditional approaches, leading to significant discrepancies in loss ratios between competitors.

Transforming Underwriting Practices

Property-Specific Risk Analytics

Z-FIRE exemplifies how property-specific risk analytics can transform underwriting practices, particularly in high-risk areas. By providing detailed insights into the frequency and severity of potential wildfire losses, Z-FIRE allows carriers to identify and avoid high-risk policies more effectively.

However, even after these properties are identified and potentially avoided by Z-FIRE users, they remain in the market. This leaves insurers without such insights increasingly vulnerable to the costly effects of adverse selection.

Z-FIRE and Regulatory Milestones

Adapting to Regulatory Requirements

Z-FIRE was the first AI model for wildfire risk assessment to obtain approval as part of a carrier rate filing from the California Department of Insurance (CDI). This milestone highlights ZestyAI’s leadership in adapting to the evolving regulatory landscape, where transparency and risk mitigation are becoming increasingly critical.

New regulations in states like California and Oregon now require insurers to incentivize homeowners’ risk reduction efforts and provide clear, detailed information about rate adjustments and policy decisions. This push for greater transparency aligns with the capabilities of advanced tools like Z-FIRE, which offer insurers the detailed, property-specific data needed to comply with these regulations and ensure fair treatment of policyholders.

Adverse Selection in a Changing Market

Risk Concentration and Legacy Approaches

As ZestyAI’s insurance partners continue to vet properties using state-of-the-art risk models, the proportion of very high-risk policies in the remaining market continues to grow.

This shift underscores the unsustainability of legacy approaches to wildfire risk, as the environment changes and competitors armed with superior insights make new policies even riskier for those lagging behind. Insurers relying on outdated risk models may not realize how the market has shifted until the claims process reveals significant and unforeseen losses.

Insurers relying on outdated risk models may not realize how the market has shifted until the claims process reveals significant and unforeseen losses.

Z-FIRE's Growing Value in the Insurance Industry

With a growing percentage of insurers adopting Z-FIRE, its value as a tool for underwriting new business becomes more evident than ever. While the threat of adverse selection looms large for carriers not using AI-powered insights, those with access to these advanced tools are better positioned to navigate the evolving landscape.

Preparing for Future Challenges

As wildfire seasons become increasingly severe and the regulatory environment continues to tighten, the ability to accurately assess and transparently communicate risk is crucial to the stability of the insurance market.

The ability to accurately assess and transparently communicate risk is crucial to the stability of the insurance market.

Ultimately, insurers who embrace AI-powered property insights gain a competitive edge, allowing them to minimize losses, stay ahead of regulatory demands, and outpace competitors still relying on traditional methods. In a market where information is power, ZestyAI’s platform provides the advantage needed to thrive.

Adverse Selection's Implications for Pricing

The Role of Property Mitigation in Risk Assessment

Adverse selection has significant implications for pricing. Consider the Park Fire perimeter, an area with a markedly elevated wildfire risk. Property mitigation plays a crucial role in risk assessment. The majority of properties in this area carry a high level of risk; 54% are categorized as “very high” risk according to the Z-FIRE L2 score.

Identifying Lower-Risk Properties

However, there are still opportunities to identify lower-risk properties, even within wildfire-prone regions. In fact, low and moderate-risk properties account for 17% of those within the Park Fire perimeter, presenting valuable opportunities for insurers to differentiate pricing and capture lower-risk business even in high-risk areas.

How Does Adverse Selection Impact Pricing?

Territory-Based Pricing vs. Property-Specific Scores

So how does this impact pricing? Let’s break it down with an example. Assuming a carrier’s statewide average wildfire premium is $280, we can assume the average wildfire premium is $843 for this area based on ZestyAI’s Z-FIRE model output. Applying territory-based pricing would mean that every home pays roughly the same wildfire premium per dollar of coverage.

The Benefit of Z-FIRE’s Tailored Approach

However, by leveraging Z-FIRE’s property-specific scores, insurers can adopt a more tailored approach that accurately reflects each property’s unique risk profile.

By leveraging Z-FIRE’s property-specific scores, insurers can adopt a more tailored approach that accurately reflects each property’s unique risk profile.

For example, a low-risk property would be charged a wildfire premium of $513, while a very high-risk property could be assigned a load of $986. This strategy not only helps attract lower-risk customers through preferred pricing, but also ensures that higher-risk properties are adequately rated.

Outpacing Competitors Through Risk-Based Pricing

In contrast, carriers whose pricing strategies are based on the average premium will be most competitive for high-risk properties but will struggle to attract lower-risk ones. Z-FIRE allows carriers to outpace risk and competitors alike.

Want industry-leading wildfire risk insights?

See Z-FIRE in Action

Regulatory Ready: How to Use AI Responsibly in Insurance Under the NAIC Bulletin

AI innovation is revolutionizing the insurance industry, but with these advancements come new regulatory challenges. To ensure responsible use of AI in insurance, it’s essential to stay informed about the latest regulatory frameworks.

Join us on November 13 at 11 PT / 2 ET for an exclusive webinar where we’ll break down how to navigate AI regulations under the NAIC Model Bulletin.

In this session, led by

• Kevin Gaffney, Vermont’s Commissioner of Financial Regulation and Chair of the NAIC’s Innovation & Tech Committee
• Bryan Rehor, Director of Regulatory Strategy at ZestyAI

you'll gain critical insights on how to align AI usage with evolving regulatory expectations.

 
What You’ll Learn

This webinar will provide practical takeaways that can help insurance professionals understand and comply with the latest AI standards:

• NAIC Model Bulletin Overview: Understand the core principles behind the NAIC’s AI regulation framework.
• Ensuring AI Compliance: Learn how to ensure responsible AI usage according to NAIC standards.
• Preparing for Regulatory Oversight: Get ready for closer state-level inspections and regulatory scrutiny.
• Vendor & Partner Compliance: Ensure that your partners meet regulatory requirements for transparency and fairness.
• Interactive Q&A: Take advantage of the opportunity to ask our experts about the complex world of AI and insurance compliance.

Meet the Experts

Kevin Gaffney
Vermont Commissioner of Financial Regulation

As an expert in AI regulations and the NAIC’s Model Bulletin, Commissioner Gaffney will provide key insights into how insurance companies can effectively implement responsible AI practices. His experience in overseeing state-level financial regulation will offer attendees a unique perspective on aligning AI innovation with compliance.

Bryan Rehor
Director of Regulatory Strategy at ZestyAI

Bryan Rehor will offer practical advice on maintaining AI compliance while harnessing the full potential of AI innovation. His expertise lies in guiding insurers through regulatory demands, ensuring that AI practices meet industry standards while avoiding common pitfalls.

Why You Should Attend

This webinar is tailored for professionals in insurance, particularly those in Executive, Legal, Compliance, Product Management, Underwriting, Actuarial, Risk, and Innovation roles.

Whether you’re navigating the complexities of AI regulation or preparing for the next steps in compliance, this session will provide actionable insights to help you move forward confidently.

Bonus Content

By registering for the webinar, you’ll receive our interactive guide:

“When Innovation & Regulation Meet: What Insurers Need to Know About AI and Regulatory Compliance.”

This resource will deepen your understanding of how to stay compliant while leveraging the power of AI in your insurance operations.

 
Don’t miss out!
Register for the webinar and ensure your spot in this exclusive event.

Beating the Odds in Insurance With ZestyAI

Insurance is not just about playing the odds—it's about beating them. In an industry facing relentless pressure from rising catastrophic losses, inflation, and soaring reinsurance costs, staying ahead of risk is more critical than ever. That’s where ZestyAI comes in.

We built a platform that we believe is a true game-changer, designed to help insurers tackle these challenges head-on. With our ‘Property Insights’ and peril models, we give companies the power to manage risk with unmatched precision.

Delivering a 10X Return on Investment

But at ZestyAI, we don’t stop there—we deliver a promise: a 10X return on investment (ROI) for our customers. In my last post, I shared what makes us stand out. Today, I’m going deeper into how we turn every dollar invested into ten. That’s not just a claim; it’s what we do.

While AI initiatives often promise big results, we consistently show how advanced risk analytics translate into tangible, financial wins for our clients.

While AI initiatives often promise big results, we consistently show how advanced risk analytics translate into tangible, financial wins for our clients.

Property intelligence provided by our platform includes Digital Roof, Roof Age, and Location Insights™. Our peril-specific models include Z-HAIL™, Z-WIND™, Z-STORM™, Z-FIRE™, and Z-WATER™, with many more in the R&D pipeline. Our suite of products provides carriers with the insights they need to strategically adjust premiums, manage risk, and make more profitable decisions—all while keeping those all-important loss and expense ratios under control.

So, how exactly do we turn $1 into $10? Let’s get into the details.

Nailing Premium Strategies with Precision

Growing the Premium Base With Property-Specific Risk Assessments

ZestyAI helps carriers grow their premium base by delivering precise, property-by-property risk assessments. While traditional models rely on broad geographic segmentation, our AI platform digs deeper, assessing risk at an individual property level. This enables carriers to offer competitive, risk-aligned pricing—growing in low-risk segments while strategically managing exposure in higher-risk areas.

Let’s be honest: territory-based segmentation is like spreading peanut butter. It’s imprecise. With ZestyAI, carriers avoid the pitfalls of adverse selection, where "good risks" get undervalued, and "bad risks" take over. Our models allow insurers to price policies based on actual risk, not generalizations, ensuring profitability even in higher risk regions. This is growth that’s smart, targeted, and sustainable.

Capturing Additional Premiums Through Accurate Data

ZestyAI’s insights also help carriers capture additional premiums through more accurate data. We enable insurers to:

• Accurately estimate property square footage
• Identify premium roofing materials (tile, slate, metal)
• Understand roof complexity (facets, pitch, penetrations)
• Locate solar panels and their total area
• Spot swimming pools, trampolines, decks, and skylights
• And more.

These detailed insights ensure premiums reflect the true risk and value of each property, leading to improved loss ratios.

Reducing Loss Ratio: Mitigating Risk With Data-Driven Insights

Predicting Losses

The cornerstone of controlling losses is precise underwriting. Carriers can define threshold levels using our predictive analytics and set clear criteria for the risks they’re willing to accept. Take Z-HAIL, for example: it scores hail claim frequency on a scale of 1 to 10. Milliman’s research paper shows that properties with a Z-HAIL score of 10 have a hail-only loss ratio of 50.4%, compared to just 2.4% for those with a score of 1. That’s a 21X lift—segmentation power that speaks for itself.

But it’s not just about predicting losses.

Proactive Risk Mitigation

ZestyAI helps insurers take proactive steps to mitigate risk before a policy is even issued. With insights like roof condition or surrounding vegetation, carriers can require clients to make repairs or clean up overgrown areas before taking them on. The result? Fewer costly claims, better underwriting, and a healthier bottom line.

Moreover, our platform helps insurers manage losses through more accurate policy adjustments. From adjusting deductibles to offering coverage at actual cost value (ACV) instead of replacement cost value (RCV), we provide carriers the data they need to make smarter decisions. The result? Better policy fit, reduced claims, and healthier loss ratios.

Taking the Hatchet to Expenses

Efficiency is the name of the game when it comes to expense ratios, and ZestyAI’s suite of products helps insurers cut costs while boosting productivity. Our high-speed APIs provide real-time data, allowing for straight-through processing—reducing the need for manual underwriting and saving time.

Take one of our recent success stories: a major national carrier was renewing part of their book, and we helped them identify 7% of their properties as high risk. By integrating our insights into their workflow, their underwriters processed each of these high-risk properties in under 30 seconds, with a 93% agreement rate. This efficiency led to $40 million in savings and a 4-point reduction in their loss ratio for that section of their book.

This efficiency led to $40 million in savings and a 4-point reduction in their loss ratio for that section of their book.

When it comes to inspections, ZestyAI applies the Pareto Principle with laser focus. We help insurers avoid unnecessary inspections for properties that can be confidently underwritten using our data, while targeting the high-risk properties that need deeper evaluation. This approach saves time, money, and resources, and—most importantly—helps carriers manage their expense ratios more effectively.

Securing Superior Reinsurance: Gaining the Competitive Edge

A Competitive Advantage in Reinsurance

In today’s market, the ability to secure favorable reinsurance terms is a serious competitive advantage. ZestyAI equips insurers with the data they need to become industry leaders—the “haves,” favored by reinsurers for their sophistication in managing risk.

Reinsurers increasingly favor carriers who take a property-specific approach, like the one ZestyAI provides. By leveraging AI-driven insights, carriers can demonstrate that they accurately assess risk at an individual property level. This positions them to not only secure more capacity but also negotiate better terms and pricing.

We’ve seen carriers using Z-FIRE, Z-WIND, and Z-HAIL present a more refined risk profile to reinsurers by providing a detailed, property-specific approach that goes beyond traditional portfolio catastrophe (CAT) models. It’s about more than just getting reinsurance—it’s about showing reinsurers that you’ve taken proactive steps to assess, mitigate, and manage risk with precision.

It’s about more than just getting reinsurance—it’s about showing reinsurers that you’ve taken proactive steps to assess, mitigate, and manage risk with precision.

Moreover, using ZestyAI’s insights, brokers can enhance their CAT modeling results with secondary modifiers or complement traditional models with next generation, AI-powered models for a more comprehensive view into risk. This enables brokers to position their clients as “haves” who are not only sophisticated in their risk management but also proactive in securing the best possible reinsurance agreements.

The 10X ROI: Our Promise

At ZestyAI, value isn’t just a buzzword—it’s our promise. Our commitment to delivering a 10X return on investment drives everything we do. With our advanced data insights, insurers can grow premiums strategically, cut losses, slash costs, and secure better reinsurance terms, turning every $1 into $10.

Each of our predictive analytics models can shave one to two points off a carrier’s combined ratio, and when multiple products are used, the impact multiplies. In one case, we helped reduce a carrier’s combined ratio by seven full points—an outcome that speaks for itself. These savings aren’t just incremental; they’re transformative.

As we continue to innovate, our mission stays the same: to empower insurers to tackle risk with precision and confidence. We’re not just a data vendor—we’re changing the way risk is managed, one dollar at a time, and turning it into ten.

Read more of the ZestyAI Founder Series:

What Sets ZestyAI Apart 

ZestyAI’s Active Role in Shaping Catastrophe Modeling and Ratemaking

We at ZestyAI are proud to have been actively involved in shaping the future of catastrophe modeling and insurance ratemaking through our participation in the California Department of Insurance (CDI)'s public hearing. As climate risk continues to challenge the resilience of California’s communities, it is more important than ever to develop regulatory frameworks that promote innovation while safeguarding consumer interests.

Key Recommendations for Regulatory Improvements

Our Director of Regulatory Affairs, Bryan Rehor, presented key recommendations during the hearing to streamline regulatory processes, reduce unnecessary duplication, and ensure catastrophe models are built on the most reliable scientific data. By working closely with regulatory bodies like the CDI, ZestyAI is dedicated to advancing the use of AI-driven models that improve risk assessment while ensuring fair, efficient, and timely regulatory outcomes.

This dialogue underscores ZestyAI’s commitment to balancing cutting-edge innovation with the consumer protections that are vital in today’s evolving climate landscape.

These are ZestyAI’s remarks in their entirety:

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Dear Commissioner Lara,

Thank you for your continued leadership in addressing the challenges posed by climate risk and catastrophe exposure in California. ZestyAI supports the goals of this regulatory framework, which aims to protect consumers and ensure that insurance remains fair and available, while also promoting innovation in catastrophe modeling. However, we believe there are several areas where improvements can be made to ensure these goals are met effectively.

1. Avoiding Duplicative Work for Models Already Used in Rating Plans

A primary concern involves the treatment of risk models that are already part of approved rating plans. Many of these models have long been used by insurers to segment rates and assess risk, and some have undergone rigorous reviews by the Department and consumer advocates as part of their initial approval. Under the proposed regulation, these models will be required to undergo the PRID process, even though they have already been extensively reviewed and accepted by CDI for use in rating plans.

We strongly recommend that the review of these previously approved models be expedited to avoid duplication of work that has already been performed. Carriers who have filed and received approval to use models that have already been subject to rigorous model reviews should not have to undergo the full PRID process again. This would impose unnecessary delays and create inefficiencies in a process that should encourage innovation and quick adoption of proven tools. We believe CDI should explore an accelerated PRID process for handling these approved models, such as fast-tracked reviews that focus only on elements that have not already been considered by the department.

2. Ensuring Consistency of Standards

Second, there must be clear and consistent standards in place to guide the industry in preparing for PRID model reviews. The CDI already has a robust checklist in place for wildfire risk models, which provides a strong foundation for determining what information is required from modeling companies. Publishing consistent standards based on this checklist will ensure fairness and transparency, while also giving modeling companies the necessary time to prepare for what might be asked of them by the Model Advisor.

Additionally, once a model has been approved and integrated into rate plans, it should not be subject to further scrutiny unless there are substantive changes to the model itself. This approach will prevent confusion and avoid placing undue pressure on insurers and modelers, ensuring a smooth and efficient regulatory process.

CDI should formalize and publish standards using the existing checklist as a baseline, providing clear guidelines that standardize the PRID procedure across all models. It is important that these standards are issued prior to the initiation of a PRID to allow adequate preparation time in order to avoid delays.

3. Clarifying the Use of “Best Available Scientific Information”

An additional concern lies in the use of the term “best available scientific information.” While we agree that models should be grounded in science, this phrase introduces an element of subjectivity that could lead to inconsistent standards over time. Science evolves, and what is considered "best available" today may change tomorrow, leading to uncertainty for modelers and insurers who need stable and predictable regulations.

We believe that the definition of “best available scientific information” should be left to the industry. Modeling companies are the experts in their fields, with a deep understanding of how to incorporate relevant data, emerging technologies, and evolving risk factors into their models. By allowing the industry to define what constitutes the most up-to-date and applicable scientific information, the Department can ensure that models remain at the cutting edge of innovation without being constrained by shifting regulatory interpretations. The role of regulators is to ensure that each component of a model is backed by credible scientific information, not prescribe which scientific information should be adopted.

Additionally, we recommend that any requirements focus on real-world data and statistically significant evidence. Inputs and weighting factors should be based on data collected in the environments where properties are located, not merely theoretical assumptions or untested hypotheses. The industry is best positioned to determine how to integrate this information into models in a way that reflects actual risk.

4. Risks of Centralized Authority with the Model Advisor

The last major improvement opportunity we see involves the substantial authority granted to the Model Advisor under the proposed regulation. The Model Advisor has significant discretion to define "required model information" and control the course of the PRID process. While this centralization of authority can streamline reviews, it also introduces risks of inconsistent and subjective decision-making. This would lead to unpredictable outcomes for risk modelers, as well as the carriers that license their models.

Additionally, the Model Advisor's control over the timing of the PRID process could lead to delays in model approval if the model advisor determines that "good cause" is shown to keep the record open. The Model Advisor’s authority over when PRID can be extended needs to be better defined, which will help keep rate filings to a predictable timeline, something the CDI has agreed is important to an efficient insurance market. In the event that an extension is absolutely necessary, we recommend defining a maximum number and duration of extensions, following the example of Bulletin 2024-7, which allows two extensions of thirty days during the rate application review process. To prevent PRID delays from stalling rate filings we recommend that the scope of the Model Advisor's discretion be more clearly defined, especially around the authority to extend PRID reviews, and that specific criteria be put in place to guide decision-making.

In summary, ZestyAI remains committed to working with the California Department of Insurance to create a regulatory framework that supports innovation while protecting consumers. We encourage the Department to:

1. Expedite the review of models already approved as part of rating plans and explore alternatives to the PRID process for these cases to avoid unnecessary duplication of work.

2. Publish consistent standards based on the CDI model checklist, ensuring predictability, and confirm that new standards will not be retroactively applied to older models already in use.

3. Clarify the meaning of “best available scientific information” to reduce subjectivity and ensure that models are based on solid, quantifiable principles derived from real-world evidence.

4. Limit the discretionary authority of the Model Advisor to ensure predictable, consistent, and timely outcomes, preventing unnecessary delays.

Thank you again, Commissioner Lara, for your commitment to addressing these critical issues. We look forward to continued collaboration and discussion to ensure that these regulations achieve the best possible outcomes for all stakeholders.

Sincerely,

Bryan Rehor
Director of Regulatory Affairs
ZestyAI

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ZestyAI remains steadfast in our mission to drive innovation in catastrophe modeling while maintaining a strong commitment to regulatory integrity and consumer protection. By actively participating in discussions like the CDI hearing, we ensure that our AI-driven solutions not only meet but exceed the evolving needs of the insurance industry. We look forward to continued collaboration with regulatory bodies and industry stakeholders to create a resilient future for California and beyond.

Want to learn more about how ZestyAI supports insurers with regulatory success?

Read "Achieving Regulatory Success With Insurance Innovation"
 

ZestyAI has announced the expansion of its partnership with West Bend Insurance Company.

Building on their successful collaboration, West Bend will now use ZestyAI’s property risk analytics platform, Z-PROPERTY, to enhance the precision of its underwriting and renewal processes for personal lines insurance.

West Bend will now use ZestyAI’s property risk analytics platform, Z-PROPERTY, to enhance the precision of its underwriting and renewal processes for personal lines insurance.

Why Property Condition Insights Matter for Personal Lines Underwriting

As properties age and undergo changes, their risk profiles evolve.

Factors such as roof condition, debris accumulation, and overgrown vegetation can significantly impact the risk associated with a property.

ZestyAI‘s Z-PROPERTY platform uses computer vision and machine learning to analyze aerial and satellite imagery, building permits, and other unique data sources for over 150 million residential and commercial properties.

This continuous monitoring of property conditions, maintenance, and upgrades allows West Bend to proactively manage risks and ensure accurate coverage for policyholders.

How Z-PROPERTY Delivers Accurate, Up-to-Date Property Intelligence

Leveraging verified data from various sources, including historical imagery and building permits, Z-PROPERTY provides West Bend Insurance with comprehensive coverage and precision, enhancing its ability to manage its residential portfolio effectively.

This integration of advanced analytics will streamline West Bend’s underwriting process, allowing for quicker, more informed decision-making while ensuring that trained representatives make all final coverage decisions.

Jonathan Schulz, AVP Personal Lines at West Bend Insurance, said:

“Our partnership with ZestyAI has significantly improved our ability to assess and manage residential property risks. By expanding our use of Z-PROPERTY for our personal lines business, we can gain deeper insights into the properties we insure, allowing us to mitigate risks more proactively and efficiently.”

Attila Toth, Founder and CEO of ZestyAI, said:

“Conducting a thorough review of a portfolio can be a daunting task. Z-PROPERTY simplifies this process, enabling carriers to assess their entire book of business seamlessly, pinpointing the riskiest policies to ensure proper risk mitigation. The platform is easy to deploy, requiring no IT integration, and covers every property in North America.”