Reports & Research

The insurance industry has been grappling for years with the skyrocketing losses caused by wildfires. As ZestyAI unveils its annual Wildfire Season Overview, we can see that insurers are in a pivotal position to navigate the ongoing threat.

Wildfire Risk Isn’t Going Anywhere

While we are currently experiencing a brief reprieve from the wildfire devastation of the last few years, the ongoing threat of wildfire remains at an all-time high.

Extreme snow and rainfall across the West in 2023 have led to wetter-than-normal conditions that have acutely reduced the risk of wildfire. However, wetter conditions lead to vegetation growth, so despite 2023 presenting lower wildfire risk, the resulting vegetation accumulation, combined with persistent drought conditions in future years, will likely result in extremely high losses in the coming years. In fact, heavy rainfall has preceded many of the most severe wildfire years ever recorded in California.

Heavy rainfall has preceded many of the most severe wildfire years ever recorded in California.

Preparing for Future Wildfire Seasons

With high wildfire activity on the horizon, what steps can insurance companies take now to prepare for future wildfire seasons?

Here are three essential strategies:

1. Leverage Data for Better Understanding

Research by ZestyAI reveals that wildfires ravage 87% more land during drought years compared to non-drought years. With the western US still experiencing a megadrought that is the worst in over a millennium, it’s critical to understand the data and risks involved.

Not all homes face high risk. For the remainder, detailed property risk insights can highlight areas requiring risk mitigation. Integrate property-specific wildfire risk data into the underwriting and renewal process. This year is also an excellent opportunity to review a complete portfolio using an AI-powered wildfire risk assessment tool like Z-FIRE.

2. Educate and Empower Property Owners Through Transparency

Technology, particularly satellite/aerial imagery and artificial intelligence, can shed light on wildfire risks. Insurers can use this technology to assess the risk reduction measures that policyholders have implemented and understand how a property might withstand a wildfire.

This information is invaluable for educating homeowners and insurance agents. By knowing the specific actions that can be taken to reduce risk, such as clearing brush or using fire-resistant materials, both insurers and homeowners can be better prepared for wildfires.

3. Choose a Technology Partner Wisely

ZestyAI's Z-FIRE has set a benchmark by integrating loss data from over 1,500 wildfires and employing cutting-edge technology to derive insights on each property. By combining aerial and satellite imagery with machine learning and cloud computing, ZestyAI created Z-FIRE, a highly detailed wildfire risk assessment model.

Z-FIRE has been adopted by leading insurance carriers in every single western US state.

In 2022, Z-FIRE demonstrated remarkable performance. Its integration of data through machine learning and computer vision models has established Z-FIRE as a potent tool in wildfire risk assessment for both underwriting and rating.

Make Informed Decisions with Z-FIRE

Using Z-FIRE, insurance carriers, MGAs, and reinsurers can get access to actionable insights developed from detailed property-level risk factors. While wildfire losses may be inevitable, understanding in detail how individual properties contribute to average and tail risks is a large step forward.

The specific time and location of a wildfire is nearly impossible to predict. However, Z-FIRE can give carriers an assessment of the preconditions for that fire, and describe in detail the factors which contribute to it. Knowing, not guessing, which properties fall into a high-risk category is more important now than ever. We look forward to helping our customers through this fire season and many to come.

Z-FIRE Stands Alone in Compliance

Z-FIRE has been developed in partnership with top carriers and has been included in successful filings in California and many other western states. As regulators continue to push for additional transparency and accuracy in how insurers treat wildfire risk, AI-powered solutions provide a clear advantage because of their interpretability and sensitivity to changing conditions.

In 2023, California began requiring insurers to provide discounts based on mitigation measures, and in 2024 Oregon is poised to establish similar requirements on communications to homeowners. All of these changes create a burden on insurers, but those who can adapt to the new regulatory environment by leveraging knowledgeable partners like ZestyAI will have an advantage over competitors. AI is part of the solution, helping address climate risk and maintaining the insurability of properties across the US.

Download ZestyAI's 2023 Wildfire Season Overview 

ZestyAI has released its annual Wildfire Season Overview for 2023. This comprehensive report combines insights from recent wildfire events, prevailing drought conditions, and cutting-edge advancements in artificial intelligence to assist insurers in effectively managing wildfire risk.

Download ZestyAI's 2023 Wildfire Season Overview

Here are some key findings from the report:

A Chance To Prepare While Wildfire Fuels Accumulate

Despite a brief respite from recent wildfire devastation, the current threat remains high. Over the past decade, wildfire risk has notably increased, particularly in California. However, the occurrence of extreme snow and rainfall in the West during 2023 has temporarily reduced the risk due to wetter conditions.

It's important to note that vegetation accumulation and ongoing droughts will likely lead to substantial losses in the coming years. California remains highly susceptible to losses and significant vegetation growth. This temporary relief in 2023 creates an ideal opportunity for insurers to review the risk technologies they have in place and embrace innovative solutions to prevent future losses.

No Role for Drought in Underwriting

Drought is indicative of fire intensity, but not losses. Although drought is an important factor in seasonal wildfire risk, the presence of drought shouldn't drive underwriting. Instead, insurers should look at property-specific solutions that consider wildfire risk over the lifetime of a policy.

Research has shown that this year's heavy rainfall may be a leading indicator for severe wildfire years to come. A comprehensive understanding of buildings, vegetation, and mitigation methods at the property level is necessary to effectively manage future wildfire risk.

A comprehensive understanding of buildings, vegetation, and mitigation methods at the property level is necessary to effectively manage future wildfire risk.

Using Advanced Models to Adapt to Changing Risks & Regulations

AI-powered risk models play a key role in mitigation. Insurers who write business in wildfire states have found increasing value in AI-powered wildfire risk models as they offer actionable risk insights, adapt quickly to changing climate risks, and comply with all regulations.

Over the last year, several western states have begun to implement new regulations for insurers in response to the changing risk environment. Discounts and transparency for mitigation efforts and property-specific decisions may become an industry standard as they have in California and Oregon.

What This Means for Insurers

In evaluating wildfire risk, many analyses tend to focus on the number of fires and the size of the area they burn. However, what really matters to insurance companies and property owners is the loss of structures and what can be done to mitigate those losses.

For example, those providing insurance in California might be surprised to learn that despite smaller losses in 2022 compared to 2021, the total national count of acres burned and fires ignited in 2022 actually exceeded that of 2021. This mismatch between yearly wildfire activity and the number of structures lost suggests that wildfire losses are not simply dictated by wildfire activity.

The most significant factor is not how many fires start, or how far they spread, but the potential resilience of every structure and what the communities and homeowners have done to prepare for wildfire exposure. Research from ZestyAI and IBHS shows that for a more precise understanding of potential losses, insurers need to zoom in on individual properties. They should consider a structure’s location, building materials, surrounding vegetation, and efforts taken by the surrounding community to prepare for wildfires.

Modern wildfire risk tools like ZestyAI's Z-FIRE do just that. They analyze individual property features and measure the impact of those features on the probability of loss. They also factor in nearby vegetation, community preparations, local infrastructure, and the lay of the land. This property-centric approach doesn’t try to predict exactly what a wildfire will do. Instead, it gives valuable information on how and why properties might be damaged by wildfires.

These models don't just offer a simple risk score, but also help explain what makes a particular property vulnerable and what steps can be taken to protect it.

 
Find out more, including how Z-FIRE performed in 2022, in this year’s Wildfire Season Overview.

Download ZestyAI's 2023 Wildfire Season Overview

San Francisco, CA, April 19, 2023 – Today ZestyAI, the leading provider of climate and property risk analytics solutions powered by artificial intelligence (AI), and the Insurance Institute for Business & Home Safety (IBHS) released new research examining catastrophic losses from severe convective storms, particularly hail. The study focuses on hail-driven losses in property and casualty insurance.  

Hail losses are a persistent problem for property insurers’ risk management efforts. Historically, carriers have focused on intense events to predict hail risk, with supporting data confined to storms with hailstones larger than one or two inches. The study Small Hail, Big Problems, New Approach shows high concentrations of small hail are more important than previously thought, pointing to an opportunity to broaden data sets to account for the cumulative effect all hailstorms have on a roof’s susceptibility to damage over time, leading to a claim. 

This new research shows all hail needs to be accounted for when modeling and ultimately understanding losses. Using data from all hail events, not just those with hail that meet the severe criteria of one inch or greater, allows carriers to consider valuable data on smaller hailstone impacts. Additionally, insurers can integrate climate and materials science to better understand hail frequency and severity. Research suggests using this new approach could perform as much as 58 times more accurately than looking at events with large and very large maximum hail sizes alone, allowing carriers to more effectively assess hail risk, achieve more profitable underwriting and open up ratings to previously avoided areas.  

“As we’ve learned more about hailstorms, we've discovered storms that produce large concentrations of small hail are more common than we thought, and despite causing less individual damage than a single large hailstone, small hail, especially in high concentrations, is likely a meaningful contributor to the loss we see each year from hail,” said Dr. Ian Giammanco, managing director of standards and data analytics at IBHS. “Experiments also show large concentrations of smaller hailstones cause degradation to the asphalt shingles, specifically dislodging large amounts of granules. Once enough granules are lost, the underlying asphalt material can become more susceptible to aging and weathering. Repeated exposure to these types of hailstorms can shorten the life of an asphalt shingle roof and increase the damage caused by large hailstones in the next storm.” 

“Hail losses are a persistent problem for property insurers’ risk management efforts,” said Attila Toth, founder and CEO of ZestyAI. “Three of the nation’s five largest publicly-traded P&C carriers mentioned hail as a key concern in 2022 financial reports. Greater losses have brought attention to hail risk, and the insurance industry needs better approaches to solve this problem.” 

“Three of the nation’s five largest publicly-traded P&C carriers mentioned hail as a key concern in 2022 financial reports. Greater losses have brought attention to hail risk, and the insurance industry needs better approaches to solve this problem.” 

Hail risk can be especially costly to insurers because, unlike other catastrophic perils like hurricanes and wildfires, it can be difficult to identify the storm that caused a hail claim. As a result, insurance carriers could be forced to raise overall premiums or introduce high deductibles to compensate for the added costs.

As climate and materials science have developed, more data has become available providing  improved hail risk evaluation options that can lead to better decisions at earlier stages of the policy life cycle. Other benefits could include more profitable underwriting, a greater ability to rate previously-avoided areas and significantly reduced loss ratios.

For the complete ZestyAI and IBHS research paper visit this page.
 

About ZestyAI

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in North America. The company uses AI, including computer vision, to build a digital twin for every building across the country, encompassing 200 billion property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit zesty.ai for more information. 

About the Insurance Institute for Business & Home Safety (IBHS)

The IBHS mission is to conduct objective, scientific research to identify and promote effective actions that strengthen homes, businesses and communities against natural disasters and other causes of loss. Learn more about IBHS at ibhs.org. 

### 

For more information, contact:

Linsey Flannery

Director of Communications, ZestyAI 

linsey@zesty.ai 

416-939-9773 

Mary Anne Byrd

Communications Director, IBHS

mbyrd@ibhs.org

803-669-4216 

Reinsurance rates are spiking to an all-time high. Fitch estimated a 20-60% rate increase for cedants in the overall property reinsurance market at the January 1st renewals.¹ Terms and conditions are also tightening - many reinsurers are limiting their cedants to much higher attachment points², or exiting CAT-exposed lines altogether

The main drivers for uptick in reinsurance rates

Our research has found three drivers underpinning the trend:

1. Devastating CAT losses, particularly from secondary perils

59% of all CAT losses come from secondary perils³, and those losses have caused major shifts in the reinsurance landscape. Howden estimates that global property CAT reinsurance rates were up 37% at the January renewals⁴.

2. A new urgency to improve return on capital

“When the cost of capital is equal to the rate of return, something has to change.” - Aditya Dutt, CEO of Aeolus Capital Management⁵. The reinsurance industry has underperformed since 2017, with an average return on equity of just under 5%⁶. Poor underwriting performance was a key driver, with an industry average 101% combined ratio over the same period⁷. Reinsurers are poised to use the tightening market as a chance to improve performance, with Fitch forecasting a 4pp underwriting margin expansion for reinsurers in 2023⁸. Unfortunately for primary insurers, Goldman Sachs predicts that the same tightening market will create significant volatility for cedants⁹.

3. Value erosion in reinsurer investment portfolios

Macroeconomic factors are driving significant unrealized investment losses for reinsurers, particularly on fixed income portfolios due to rising interest rates. Aon estimates that these investment portfolio losses drove a 17% decline in global reinsurance capital across the first 9 months of 2022, with some players reporting equity value losses as high as 40-50% over that period¹⁰. Reinsurers will look to shore up these losses with better underwriting performance, which likely means tougher rates for primary carriers.

How property insurers can improve their odds with AI-powered predictive climate and property risk platforms

These factors mean that primary insurers can expect challenging reinsurance negotiations at the June 1st renewal deadline, particularly on property lines. However, new AI-powered predictive climate and property risk platforms can improve the odds for property insurers in three areas:

1. Rapid improvements in risk mitigation

Implementation-free portfolio reviews can quickly drive major loss ratio improvements.

2. Turn the tables of CAT risk screening in your favor

Improving data quality can lead to more favorable stochastic model portfolio screens, particularly with insight about the roof.

3. Enter the room as a leader in cutting-edge risk practices

Showing the same commitment to new technologies as industry leaders can help cedants build a better case.

Conclusion

With the right mitigation action and a cutting edge view of portfolio risk, cedants can navigate the upcoming 6/1 renewal successfully.

Learn more about how an AI-powered predictive climate and property risk platform can help you.

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Sources

1 & 8 - Fitch, Reinsurers’ Underwriting Margins to Expand by 4pp in 2023

2 & 3 - Gallagher Re, Gallagher Re Natural Catastrophe Report 2022

4 - Howden, Howden’s renewal report at 1.1.2023: The Great Realignment

5 - AM Best, Reinsurance: Roundtable Discussion on Renewals and What 2023 May Hold

6, 7 & 10 - AON, Reinsurance Market Dynamics

9 -  Reinsurance News, Hard market to increase volatility for primary insurers: Goldman Sachs

San Francisco, CA, September 20, 2022 – ZestyAI, the leading provider of property risk analytics solutions powered by Artificial Intelligence (AI), has developed a 180-day playbook to support insurance carriers as they work to meet the Mitigation in Rating Plans and Wildfire Risk Models regulation expected to be adopted by the California Department of Insurance (CDI) before year-end. The playbook reflects the company’s unique ability as the only comprehensive solution in the marketplace to help insurers meet or exceed every single requirement in the new regulation — meeting 100 percent compliance inside the tight 180-day window.

On September 7, 2022, Insurance Commissioner Ricardo Lara announced he had submitted the department’s insurance rating regulation recognizing wildfire and safety mitigation efforts made by homeowners and businesses, to the California Office of Administrative Law for final approval. This first-of-its-kind regulation will require all insurers in California to refile their existing rating plans on an aggressive 180-day timeline. 

“Eight of the ten most destructive wildfires in California’s history have occurred in the last five years,” said Attila Toth, Founder and CEO of ZestyAI. “While the new wildfire regulations will have a significant impact on California’s insurance industry, adapting to this peril is key to having a sustainable insurance ecosystem in California. As the leader in property-specific wildfire risk assessment, we have offered input at each step of this process. We are here to support admitted carriers with a turnkey solution complying with every single requirement as they navigate this process and work to meet the new regulations.”

The new wildfire safety regulation requires insurance companies to consider the structure of a home, its surroundings, and community-level mitigation. Insurers with concerns about the regulation can reach out to ZestyAI to get a complete explanation of how the regulations will impact them. This includes access to the 180-day playbook, which breaks down the regulatory compliance process into an orderly roadmap that addresses all three major challenges that insurers will face:

• Operational — The process of rapidly integrating new data sources, educating the public on how wildfire mitigation affects insurance policies, and a framework for a compliant appeals process.
• Rating — How to weight property-specific characteristics, including those with and without historical loss data, in rating plans as well as guidance on mitigation credits.
• Filing — Carriers who use a rating plan reliant on traditional wildfire models without property-specific information will need to overhaul their rating framework. Relying on multiple approved rate filings, ZestyAI has developed a comprehensive filing toolkit that can support carriers at every facet of the filing process.

ZestyAI’s Z-FIRE™ model has quickly become the leader in property-specific wildfire risk assessment. Using AI algorithms trained on more than 1,500 wildfire events across 20 years of historical loss data, Z-FIRE™ provides a level of detail that is of essential value to both the insurer and the homeowner.

The model was the first AI model ever approved as part of a rate filing by the CDI and the second wildfire risk model. It has been widely adopted across the Western U.S., where its use has been approved for both underwriting and rating. During 2021's APCIA Western Region Conference, CDI representatives expressed that the agency’s familiarity with Z-FIRE™  means in future filings the focus will be limited to the carrier's specific use of the model, not the details of the model itself, potentially greatly expediting the reviews of carriers using the Z-FIRE™ model.

ZestyAI’s Z-FIRE™ considers features such as topography and historical climate data in combination with factors extracted from high-resolution imagery of the property itself and its surroundings, including homeowner and community mitigation efforts, to provide both neighborhood and property-specific risk scores. 

A significant advantage to insurance carriers is that they can use these data elements to communicate with homeowners on what specific actions can be taken to lower their property’s risk, such as upgrading building materials and cutting down surrounding dry brush or overhanging vegetation. The impact of mitigation efforts can be significant. A joint study by the Insurance Institute for Business & Home Safety (IBHS) and ZestyAI, which studied over 71,100 wildfire-exposed properties, found that property owners who clear vegetation from the perimeter of their home or building can nearly double their structure's likelihood of surviving a wildfire.

About ZestyAI

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in the United States. The company uses AI, including computer vision, to build a digital twin for every building across the country, encompassing 200 billion property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit zesty.ai for more information.

Download Report

Today, ZestyAI released its 2022 Wildfire Season Overview. Each year, ZestyAI prepares a comprehensive overview to help guide insurers based on recent wildfire events, persistent drought conditions, and advancements in artificial intelligence for managing wildfire risk.

If it seems like wildfires are burning at all times of the year, it's not just you. Very destructive events, like last December's Marshall Fire, are occurring in months not typically associated with high wildfire danger. Those who study wildfires, including ZestyAI, have begun to start thinking in wildfire "years" instead of wildfire "seasons'. Strong wildfire years, with 10+ million acres burned, have quickly become the new normal. The last 10 years have been the worst on record for property and casualty (P&C) insurers when it comes to wildfire. 8 of the top 20 fires in California history, and more than half of the acreage burned by them, occurred in just the years 2020 and 2021.

What can insurers do to prepare themselves for persistent wildfires?

• Understand the Data: Instead of sticking with decades-old approaches, assess wildfire risk at the property level.
• Continue to Bring Transparency and Education to Homeowners: Insights from AI-based wildfire risk models may be passed on to homeowners and agents, enabling a much better understanding of wildfire risk.
• Find the Right Technology Partner: Aerial and satellite imagery, machine learning, and infinitely scalable cloud computing resources were combined to build the most granular wildfire risk assessment model (Z-FIRE™). Using Z-FIRE™, ZestyAI can accurately estimate an individual property’s wildfire risk, plus highlight the key property-level factors that contribute to that risk.

Click here to download ZestyAI's 2022 Wildfire Season Overview.

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in North America. The company uses AI, including computer vision, to build a digital twin for every building in North America, encompassing 200B property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit https://zesty.ai for more information.

Who We Are: Revolutionizing Risk Management with AI

ZestyAI is the leading property and climate risk platform, leveraging advanced AI and data science to provide property-level risk insights to insurers across the United States. Our mission is to redefine how insurers assess and manage risk while fostering a healthier, more sustainable, and more affordable insurance market.

At its core, ZestyAI empowers carriers to assess risk with unmatched precision, aligning premiums with actual property-level data. Our data-driven recommendations help insurers improve underwriting, streamline operations, and optimize portfolios—all while ensuring transparency and regulatory compliance.

ZestyAI’s solutions are rigorously validated by state departments of insurance, including in California, Texas, and Colorado. Our wildfire risk model became the first to gain approval as part of a carrier rate filing from the California Department of Insurance (CDI), setting a benchmark for the responsible adoption of AI in the industry.

With over 200 billion data points analyzed and nearly 100% aerial imagery coverage across the contiguous U.S., ZestyAI transforms vast amounts of data into actionable intelligence. From roof conditions to vegetation density, our platform enables insurers to reduce loss ratios, improve profitability, and increase the availability of insurance to underserved markets.

Proven Performance in the Toughest Conditions

ZestyAI's AI models have been tested and trusted under the industry’s most challenging scenarios:

• Z-FIRE™: The gold standard for wildfire risk, adopted across all wildfire-prone markets in the U.S.
• Z-HAIL™: Predicts hail claim frequency and severity with up to 58X greater accuracy than traditional models, as validated by IBHS research.
• Z-WIND™ and Z-STORM™: Deliver granular risk assessments for wind and storm frequency and severity, with regulatory approval across several states.
• Z-WATER™: Our newest model predicts non-weather-related water damage claims using insights from property construction, local water systems, and environmental factors.

Powered by 30+ proprietary computer vision algorithms, our peril-specific models provide carriers with the flexibility to use ZestyAI’s risk scores or integrate detailed property insights directly into their own workflows.

The Origin of ZestyAI: A Bold Pivot

ZestyAI’s journey began not in insurance, but in clean energy. Founded as Powerscout, we made a pivotal shift in 2017 when the devastating California wildfires revealed a critical gap in how insurers assess risk. Recognizing the potential of our imagery and AI models to become a lifeline for carriers and communities alike, we refocused our mission. By 2018, ZestyAI was born, dedicated to revolutionizing insurance through AI-driven insights.

Why ZestyAI?

Today, our platform empowers carriers to achieve transformative outcomes:

• Set Precise Rates: Move beyond broad territory-based pricing to property-level assessments, attracting low-risk customers with precision pricing while helping high-risk properties mitigate exposure.
• Enhance Risk Selection: Leverage comprehensive risk profiles for smarter underwriting, optimizing combined ratios and profitability.
• Improve Product Fit: Tailor coverage options with appropriate deductibles and endorsements, reducing losses and improving customer satisfaction.
• Optimize Inspections: Direct resources to properties that need attention, reducing unnecessary on-site inspections and cutting costs.
• Streamline Operations: Automate approvals for low-risk properties, freeing underwriting teams to focus on complex cases.
• Optimize Portfolios: Reassess books of business to identify accumulated risks and adjust premiums or coverage based on real-time data.

At the heart of these capabilities is our commitment to delivering a 10X return on investment (ROI) for our customers. By combining precise risk assessment with actionable insights, ZestyAI helps carriers achieve measurable results.

Building a Healthier Insurance Ecosystem

ZestyAI’s impact extends beyond carriers—we’re reshaping the insurance ecosystem to be more accessible, equitable, and efficient.

• Fair Pricing: Align premiums with actual risk to attract low-risk customers while offering guidance for mitigating high-risk exposures.
• Proactive Risk Mitigation: Empower policyholders with tailored guidance, like improving roof conditions or clearing vegetation, to reduce exposure.
• Operational Efficiency: Streamline processes to lower costs and focus resources on the most impactful areas.

Leading the AI Revolution in Insurance

As climate risks grow more intense, insurers must adopt innovative tools to assess and mitigate these challenges. ZestyAI is at the forefront of this transformation, enabling carriers to not only adapt but thrive in an evolving market.

The future of insurance lies in AI-driven precision, and with ZestyAI, that future is already here.

Schedule a demo today to see how ZestyAI can transform your risk strategy in 2025.

As we close out 2024, we’re taking a moment to reflect on a year defined by deeper partnerships, relentless innovation, and measurable progress in the insurance industry.

Watch the video message from our CEO, Attila Toth, to learn more about the milestones we achieved and what’s next for ZestyAI.

Strengthening Relationships

This year, we deepened our collaborations with existing partners and welcomed new customers across the country. Nearly half of the top 100 insurance carriers now trust ZestyAI to deliver precise, AI-driven insights that enhance underwriting, streamline operations, and address emerging risks in high-peril areas.

Innovation That Delivers Value

At ZestyAI, innovation is never just for its own sake—it’s about delivering actionable value that solves real-world challenges. In 2024, we launched:

• Z-WATER™: Our new model that predicts non-weather-related water damage claims, helping carriers identify and mitigate this growing risk.
• Roof Age: Leveraging historical imagery and building permit data to provide accurate roof condition insights for better underwriting and risk management.

We also delivered substantial enhancements to Z-PROPERTY™, our leading property insights platform:

• Increased Coverage and Improved Hit Rates: Ensuring more comprehensive and reliable property assessments.
• New Features: Including driveway condition insights and support for multi-structure properties, enabling insurers to prioritize resources and streamline inspections.

Tremendous Regulatory Momentum

This year brought significant progress in regulatory approvals, further validating ZestyAI’s solutions in key markets like California, Texas, and Colorado. Our models continue to set a benchmark for responsible AI adoption, ensuring insurers can confidently integrate advanced property risk insights into their workflows.

Delivering Performance Across Perils

ZestyAI’s AI-powered models continued to prove their value under the industry’s most challenging conditions:

• Z-FIRE™: Recognized as the gold standard for wildfire risk across the U.S.
• Z-HAIL™: Predicting hail claim frequency and severity with up to 58 times greater accuracy than traditional models.
• Z-WIND™ and Z-STORM™: Delivering granular risk assessments for wind and storm-related risks.

These innovations are helping insurers reduce loss ratios, improve profitability, and ensure coverage availability where it’s needed most.

Looking Ahead to 2025

As we move into the new year, ZestyAI remains committed to revolutionizing risk management through AI-driven precision and proven performance. We will continue to help insurers navigate emerging challenges, drive efficiency, and build a more resilient future for their policyholders.

From all of us at ZestyAI, thank you for being part of this journey. Wishing you a wonderful holiday season and a successful year ahead.

The California Department of Insurance (CDI) has introduced significant updates as part of its Sustainable Insurance Strategy. These new bulletins and draft regulations aim to accelerate regulatory approvals, embrace forward-looking models, and address critical reinsurance challenges.

But what do these changes mean for insurance carriers—and how can you prepare?

On January 29, 2025, we hosted a webinar, California’s Evolving Insurance Landscape: The Future of Insurance in the Golden State. 

Designed for Legal & Compliance professionals, Product Managers, Underwriters, Actuaries, and Risk & Innovation leaders, the discussion featured expert insights from:

• Michael Peterson, Deputy Commissioner of Climate & Sustainability, California Department of Insurance
• Karen Collins, VP, Property & Environmental, APCIA
• Bryan Rehor, Head of Regulatory Affairs, ZestyAI

Missed the live event but want to gain actionable insights from industry leaders at the forefront of California’s insurance evolution? Watch on demand now!

ZestyAI, the leading provider of AI-powered climate and property risk analytics solutions, has received regulatory approval from the Iowa Insurance Division for its Severe Convective Storm suite, including Z-HAIL, Z-WIND, and Z-STORM. 

This approval marks a critical milestone in helping insurers address the growing challenges posed by severe weather in one of the Midwest’s most storm-affected states.

In 2024, Iowa experienced five billion-dollar severe weather events, with hailstorms driving billions in damages and insurance losses. In one instance, a hailstorm caused $2.4 billion in damage, underscoring the urgent need for innovative tools to assess and manage storm-related risks.

ZestyAI’s Severe Convective Storm suite delivers property-specific risk assessments, enabling insurers to predict and mitigate extreme weather impacts with precision. Designed with regulatory compliance at its core, Z-HAIL is validated using actual loss data and provides a clear breakdown of the top three risk drivers for each score. This transparency empowers insurers to make informed decisions and share actionable insights with policyholders. 

By analyzing climatology, geography, and building characteristics, ZestyAI equips insurers to identify high-risk properties, allocate resources strategically, and encourage proactive risk-reduction measures among policyholders.

"This approval empowers our carrier partners to act quickly and confidently in addressing Iowa’s severe weather challenges," said Bryan Rehor, Director of Regulatory Affairs at ZestyAI.

"By streamlining subsequent filings, we help insurers save time and resources, ultimately making high-quality property insurance more accessible to Iowa homeowners."

ZestyAI’s Severe Convective Storm suite has already received approvals in other key hail belt states, including Texas, Colorado, Illinois and Indiana, with additional filings in progress. These models enable insurers to move beyond reactive damage assessments, improving their ability to assess and manage storm-related risks at a granular, property-specific level.

With this approval, ZestyAI continues to lead the charge in equipping insurers with the tools they need to navigate an era of climate uncertainty, ensuring communities and insurers alike can better weather the storm.

                           Scaling a complex system of machine learning models while delivering real-time insights is no small feat. ZestyAI’s engineering team reimagined its architecture to overcome these challenges, leveraging NVIDIA’s Triton Inference Server and introducing the “Monster Pod.” This transformation halved API response times, increased throughput by 10x, and cut cloud costs by 75%. Dive into how strategic experimentation and innovative design unlocked efficiency and positioned ZestyAI for future growth.                        

By Andrew Merski, VP, Engineering 

The Challenge: A Complex and Scaling System

Business Context

At ZestyAI, we deliver critical insights to insurance clients using machine learning models. Our API processes a significant volume of data, including imagery, geolocation, and structured data, to produce real-time results. The complexity of each request places immense demands on our infrastructure:

• Synchronous API Calls: Each request must be processed in real-time, with all insights delivered back to the client in a single response. Low latency is non-negotiable, as our clients’ workflows rely on immediate feedback.
• Multiple ML Models Per Request: Each request may invoke up to 30 ML models, ranging from computer vision models analyzing aerial imagery to models synthesizing geospatial and tabular data.
• Growing Model Catalog: The catalog of ML models we deploy continues to expand, driven by both customer needs and internal innovation. Each new model adds additional complexity to the system.
• Exceptional Reliability: Our clients in the insurance sector demand a system that operates flawlessly, with uptime and accuracy critical to their decision-making processes.

Previous Architecture: A Decentralized Model

In our previous system, each ML model operated as an independent microservice. Each model scaled independently, and each instance required its own GPU. While functional, this architecture introduced critical issues:

• Resource Underutilization: GPUs were underutilized, with non-GPU tasks consuming significant time.
• Scaling Challenges: Periods of high API traffic put additional strain on system components, leading to some inefficiencies.

• Capacity Limitations: The previous architecture had constraints that limited scalability, which could have restricted future growth.

This architecture also resulted in significant operational complexity. Each model’s independent deployment meant substantial manual effort in testing, scaling, and troubleshooting. Cloud costs also escalated rapidly as new models were added, creating diminishing returns for each improvement in service quality. 

The Solution: A Centralized Architecture with Triton

Faced with scaling challenges and rising customer demand, we reimagined the entire architecture. At the heart of the solution was NVIDIA’s Triton Inference Server, a tool designed for efficient multi-model serving.

Why Triton?

Triton enabled:

• Shared GPU resources across models.
• Ensemble models to define workflows using configuration rather than code.
• Extensive benchmarking tools for performance optimization.
• Support for various backends, including Python and Pytorch.

However, Triton required significant investment in layers of customization to meet our needs. Its low-level interface and lack of native autoscaling demanded a tailored implementation.

New Architecture: The Monster Pod

To maximize Triton’s potential, we introduced the “Monster Pod,” consolidating all models and supporting microservices into a single Kubernetes pod. Key features included:

• Single-host model serving: All models resided in a unified Triton instance.
• Integrated workflow management: The workflow orchestrator and other microservices were co-located with Triton.
• Streamlined scaling: Each pod functioned as an independent unit, simplifying horizontal scaling.

This “Monster Pod” approach offered numerous benefits:

Improved Resource Utilization

• Maximized GPU usage by serving multiple models per instance.
• Reduced the overhead associated with multiple nodes and microservices.

Simplified Testing and Benchmarking

• Each pod contained all necessary components, enabling comprehensive testing in isolation.
• Benchmarking provided clear insights into throughput and resource requirements.

Reduced Scaling Overhead

• Eliminated dependency on Istio for internal traffic management.
• Simplified node provisioning and scheduling.

Predictable Costs

• Each pod corresponds to a fixed node cost, allowing accurate cost planning. 

Lessons Learned

This project revealed critical insights that extend beyond Triton or even ML systems:

1. The "Microservices vs. Monolith" Debate Isn’t Binary
Architectural decisions don’t have to be all-or-nothing. For instance, while our deployment consolidated models into a single pod, we retained microservices for other aspects of the platform. Evaluating “single vs. many” decisions at multiple levels allowed us to optimize each layer independently.

2. Understand the Bottlenecks Before Designing Solutions
Identifying the root causes of inefficiency—scaling overhead, resource underutilization, network traffic—helped us design a system that addressed these challenges holistically rather than incrementally.

3. The Power of Consolidation
Integrating multiple components into a single deployment reduced complexity, improved performance, and simplified scaling. This approach may not suit every scenario, but in our case, it delivered transformative results.

4. Be Open to Temporary Solutions (Flexibility Leads to Innovation) 
The “Monster Pod” started as a quick workaround but became a permanent fixture due to its outsized impact. Being open to experimentation unlocked unexpected benefits, such as easier resource planning and reduced operational complexity.

Business Impact

Rebuilding our ML inference platform was a bold move that paid off. The new architecture produced dramatic improvements across key metrics:

• Latency: API response times were halved.
• Capacity: System throughput increased by 10x, eliminating the previous capacity ceiling.
• Cost Efficiency: Cloud costs for model serving dropped by 75%.

These gains position us to scale with growing demand while maintaining industry-leading performance. Additionally, the simplified architecture has freed up engineering resources to focus on innovation rather than maintenance.

While Triton Inference Server played a critical role, the real success lay in our architectural decisions and willingness to rethink the status quo. This project underscores the value of experimentation and the importance of tailoring solutions to meet unique challenges.

The lessons learned from this journey will continue to inform our approach to system design and scalability as we look ahead. The Monster Pod has not only transformed our current capabilities—but has also set the stage for future growth and innovation.

For a deeper dive into the technical details, check out Andrew Merski’s original blog on Medium.

In an industry as established and thoughtful as insurance, bold innovation isn’t always easy to come by. ZestyAI is working to change that by integrating artificial intelligence into the core of how insurers manage risk, optimize pricing, and drive growth.

We are honored to receive two recognitions this year, highlighting the growing role of technology in driving meaningful progress and affirm our commitment to being a trusted partner for property insurers navigating an ever-evolving landscape

Leading the Way in P&C Insurance Technology

ZestyAI has been named one of the Everest Group’s Leading 50™ Property & Casualty (P&C) Insurance Technology Providers for 2024. This recognition celebrates technology providers that are transforming the P&C insurance sector through advanced platforms and solutions.

The Everest Group evaluated companies based on metrics such as revenue derived from P&C-focused technology, value chain coverage, and innovation in product offerings and partnerships.

ZestyAI earned accolades in two categories: Emerging Risks Intelligence and Assessment (Climate) and Risk Intelligence for Property Insurance.

These honors reflect the proven performance of our AI-driven platform under the most challenging conditions. From the devastating California wildfires of 2020 and 2021 to the unprecedented 2023 convective storm season, our models—Z-FIRE, Z-HAIL, Z-WIND, and Z-STORM—have consistently delivered reliable insights and results

Shaping the Future of Climate Risk Analytics

Chartis Research ranked ZestyAI 47th out of 175 organizations in climate risk analytics, recognizing our significant contributions to addressing climate risk challenges in insurance. This acknowledgment highlights our commitment to tackling secondary perils—including wildfire, hail, wind, and severe convective storms—through advanced AI-powered property insights and predictive models.

Our platform-driven approach, grounded in climate science, equips insurers with actionable, peril-specific insights. These tools empower them to navigate the complexities of climate risk, adapt to an evolving regulatory and environmental landscape, and optimize risk management strategies.

Driving Innovation and Excellence

This year’s recognitions join a growing list of accolades celebrating our contributions to innovation and excellence. ZestyAI has been named one of Forbes’ Top Startups to Work For in America, as well as one of Inc. 5000’s Fastest Growing Private Companies in America. Additional honors include recognition on the Deloitte Technology Fast 500, inclusion in the CB Insights Insurtech 50, an AI Breakthrough Award for Machine Learning, and a PropertyCasualty360 Insurance Luminary award for Risk Management Innovation.

 These accolades inspire us to keep pushing boundaries, delivering exceptional value, and pursuing our mission to create a more sustainable and resilient insurance ecosystem.