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ZestyAI today announced that Standard Casualty Company, a specialized property insurer serving manufactured homeowners, is expanding its partnership by adopting ZORRO Discover™, bringing agentic AI to regulatory and competitive intelligence.

Standard Casualty first partnered with ZestyAI in 2024, adopting Z-PROPERTY™, Z-FIRE™, and Z-HAIL™ for property-level underwriting. In 2025, the carrier expanded its use of Z-PROPERTY to include Roof Age and Wildfire Mitigation Prefill, applying them at the portfolio level to strengthen its view of risk across the book. 

With the addition of ZORRO Discover, the carrier is extending its use of AI across the full product lifecycle, from underwriting precision to rate strategy, competitive benchmarking, and state-by-state filing execution.

Historically, understanding competitor rate filings meant teams manually combing through thousands of pages of regulatory documentation across jurisdictions. ZORRO Discover automates that process, drawing on more than 2 million P&C rate and form filings to help regulatory, actuarial, and product teams spot rate trends, benchmark competitors, and get ahead of potential regulator objections.

For Standard Casualty, that means deeper competitive visibility, stronger pricing strategy, stronger submission readiness, and fewer delays from objection cycles.

Rick Smith, Underwriting Director at Standard Casualty, said:

"The ZestyAI platform has become core to how we underwrite and manage risk. ZORRO Discover was the natural next step, giving us structured visibility into how the market is moving and helping us strengthen our products and move through filing cycles more efficiently."

To support that kind of day-to-day decision making, ZORRO Discover goes beyond traditional research tools. It continuously analyzes new submissions and regulatory outcomes, so teams can track market shifts as they happen and adjust strategy proactively instead of reacting after the competition has already moved.

Attila Toth, Founder and CEO of ZestyAI, said:

‍"Insurance is quickly moving toward AI as core infrastructure. Standard Casualty has been putting that into practice across their business for years — and with ZORRO Discover, they're turning market moves and regulator signals into structured intelligence they act on in minutes instead of months."

ZestyAI today announced that NRDC (Natural Resources Defense Council), one of the nation's leading environmental advocacy organizations, is using ZORRO Discover™ to support its climate research and public policy advocacy efforts.

NRDC's FAIR Future Team uses ZORRO Discover to analyze insurance rate filings, track regulatory trends, and support climate-focused policy advocacy across U.S. insurance markets.

As climate-related risks reshape property insurance markets, policymakers and advocates face growing pressure to respond with evidence-based solutions. Yet the underlying data has historically been difficult to access—buried within hundreds of pages per filing, across dozens of insurers and multiple states.

ZORRO Discover addresses this challenge by using agentic AI to aggregate and structure more than 2 million P&C rate and form filings, representing over 200 million pages of regulatory documentation, into a unified, searchable system of decision intelligence.

NRDC will use the platform to support state-level advocacy and analyze rate and risk trends in key jurisdictions.

Alfonso Pating, Global Financial Regulations Specialist at NRDC, said:

“Insurance filings contain critical information about how risk is being priced and why—but extracting that information across dozens of companies and multiple states has traditionally required an enormous investment of time and effort."

NRDC's decision reflects a broader shift: regulatory data is no longer just a compliance artifact—it is a strategic asset for understanding how insurance markets function and evolve.

ZORRO Discover brings the same depth of regulatory intelligence used by insurers into the policy and research ecosystem shaping the industry.

Attila Toth, Founder and CEO of ZestyAI, said:

"Insurance filings contain the most detailed record of how risk is priced—but until now, they haven’t been accessible or usable at scale. That changes how insurance markets can be understood. We’re pleased to support NRDC’s work in this area."

ZORRO Discover continuously analyzes new regulatory submissions and outcomes across jurisdictions, giving users a current view of how insurance markets are changing. For NRDC’s FAIR Future Team, this means monitoring how insurers are responding to climate-related risk in real time—and to ground advocacy and media engagement in comprehensive, current data rather than fragmented or outdated sources.

The Zest: Key Takeaways

• Guy Carpenter's Kevin Van Leer on how property-level data is reshaping reinsurance placement and what's changing for catastrophe modelers.
• A broader shift is underway: carriers are elevating property data quality to an enterprise priority, sharpening the data that powers every decision across the insurance lifecycle — from underwriting and pricing to claims, portfolio management, and reinsurance.
• The bar for AI in insurance has shifted from "interesting" to "purpose-built": systems that are faster, more consistent, and more defensible than the manual processes they replace.

Few people have seen property analytics from as many angles as Kevin Van Leer. After studying atmospheric science at Purdue, he drove the release of the wildfire catastrophe model at RMS, a model whose appetite for granular property inputs ran ahead of what the data side could deliver at the time. He then spent seven years on the property analytics side at CAPE Analytics before joining Guy Carpenter as a Certified Catastrophe Risk Analyst. Today, he sits at the center of one of the most consequential shifts in reinsurance placement in a generation: how brokers, cedents, and reinsurers actually use property-level data to make capital decisions.

In a recent keynote conversation with the ZestyAI team, Kevin shared his view of where this market is going. The themes he covered are worth carrying forward.

Property data quality is now an enterprise imperative 

For decades, the industry has been moving from portfolio averages and class plans to property-level precision — and that shift is now reaching the part of the lifecycle where the most capital is at stake. Property-level data has long delivered value in underwriting and pricing, but the next leap is consistency: carriers building a single, trusted view of property risk and applying it the same way through underwriting, rating, claims, portfolio management, and reinsurance. The result is enterprise-grade data quality that holds up under reinsurer scrutiny. 

You can see the shift in how carriers go to market. The submission a cedent brings to reinsurers is a tight document covering financial highlights, key initiatives, exposure updates, and CAT loss updates, and data enrichment now sits among those headline sections rather than as a footnote.

What this unlocks is a virtuous lifecycle. Carriers sharpen their view of risk, make better risk selection decisions, capture appropriate rate, and underwrite with greater precision. When they bring that same view of risk to their reinsurer, both sides are aligned — giving cedents the clarity to make the right decisions on reinsurance structure, terms, and pricing. The view of risk gets sharper for everyone involved in the transaction. 

The CAT modeling gap is closing

Catastrophe models simulate peril at remarkable resolution, capturing wind fields, fire spread, flood inundation, and seismic intensity. They work best when those peril views are paired with equally granular property inputs, such as roof material captured at the address, building age that matches reality, defensible space measured rather than estimated, and exposures captured at the building rather than averaged across a ZIP code. For most of the industry's history, that level of property detail wasn't available at scale.

Much of this gap comes down to secondary modifiers — characteristics like roof material, roof age, roof condition, defensible space, and surrounding vegetation. These modifiers don’t come packaged with a CAT model, but they have a material impact on stochastic loss results. Without accurate, property-level secondary modifiers, even the most sophisticated model is making assumptions about the very inputs that drive its outputs. With them, carriers and reinsurers get a sharper, more defensible view of modeled losses.

As property-level data flows into reinsurance submissions, CAT modelers are working with the inputs their models were designed for, and reinsurers are pricing risk against a more accurate picture of what they're actually covering.

Purpose-built AI is the only kind that moves insurance forward 

Another theme that resonated: generic AI doesn’t move insurance forward. Purpose-built AI does. By “purpose-built,” Kevin means AI engineered for a specific insurance problem — replacing something expensive, slow, or inconsistent (manual inspections, piecemeal public-records pulls, or subjective desktop reviews) with something faster, more consistent, and uniformly applied to every property. 

The bar for adoption is simple: the AI’s output has to tie directly to claims outcomes. If an underwriter, actuary, or chief risk officer can see how a capability sharpens loss prediction, it earns its place in the workflow. If they can’t, no amount of polish makes it useful. 

What's next

There's a lot still ahead, including new markets to enter, new perils to model, and deeper integration into the systems where carriers and reinsurers actually make decisions.

Insurance is 700 years old, and the next decade is its most consequential yet. Property risk modeling sits at the center of that decade, combining technical and commercial work at the intersection of science and capital. 

Want to learn more about how ZestyAI helps carriers, brokers, and reinsurers optimize reinsurance decisions? Learn more about ZestyAI’s reinsurance solutions → 

See how carriers are uncovering hidden fire risk.

Join us May 13 at 11a PT | 2p ET.

Reserve Your Spot

Learn what property-level intelligence reveals that community scores never could.

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Non-weather fires cost the industry $25B in annual losses.

Claim severity is up 43%.‍

Yet most carriers are still assessing the risk with tools designed to measure how fast trucks arrive, not whether a fire starts.

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Why Non-Weather Fire Is Difficult to Assess — and Easy to Miss

High severity, low visibility

At an average of $173K per claim, non-weather fire hits harder than any other peril. Yet most of the risk never shows up in loss history, leaving carriers exposed without knowing it.

How carriers are applying this in underwriting and pricing

How carriers are incorporating these signals into underwriting, pricing, and portfolio strategy — including a live look at Z-SPARK

A structural data gap

Much of this risk isn’t visible in claims data, and community-level scores treat neighboring properties as identical risks. They're not.

Portfolio impact

Bad risks enter quietly. By the time they surface, the loss in unrecoverable.

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Featured Speakers

Alex Kallos‍

Risk Modeling & Analytics

Leads development of property-level risk models at ZestyAI

Abdul Mohammed‍

P&C Insurance Market Strategy

Leads product marketing for ZestyAI’s risk models, working with carriers on underwriting and pricing decisions

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What You'll Take Away

How non-weather fire is evaluated today

Identifying the critical gaps in traditional assessment, where current tools fall short — and what they're missing

What’s driving severity and loss trends

What’s behind rising losses — and why claims are up 43% in four years. Why severity alone doesn't tell the story.

Where current models break down

How community-level scoring and incomplete data lead to misclassification, mispricing, and adverse selection

What actually differentiates risk at the property level

The signals that separate similar-looking properties — and where risk is often missed

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Reserve Your Spot

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Regulators in Utah, Colorado, Tennessee, Missouri, and Montana have accepted ZestyAI's AI-powered non-weather water risk model for use in carrier rate and rule filings, bringing the model's total approved footprint to 12 states nationwide.

Now the fourth-costliest peril in homeowners insurance, non-weather water drives more than $15 billion in annual losses across over 1 million claims, with average claim size exceeding $15,000. Routine failures like burst pipes and hidden leaks are now producing catastrophe-scale losses that surpass hurricanes in severity, yet the peril is difficult to model using traditional rating tools—which rely on territory-level or age-based proxies that overlook the property-specific factors driving interior water losses.

Using verified insurer loss data, Z-WATER™ applies computer vision to aerial imagery and incorporates property-level data, permitting history, localized climatology, and infrastructure context to capture the property-specific drivers of interior water losses. By modeling how these variables interact, Z-WATER predicts both the frequency and severity of non-weather water claims with 18× lift in risk segmentation compared to traditional territory- and age-based models.

"Non-weather water losses place real pressure on carriers' books, but they're also highly preventable when you understand where the risks actually lie," said Bryan Rehor, Director of Regulatory Strategy at ZestyAI.

"Z-WATER helps insurers pinpoint those vulnerabilities at the property level and price them appropriately, while meeting regulators' expectations for clarity and fairness."

These approvals add to ZestyAI's broader regulatory momentum. The ZestyAI platform — spanning wildfire, hail, wind, severe convective storm, non-weather water, and property and roof intelligence — has secured more than 200 regulatory approvals nationwide.

Carrier adoption and regulatory acceptance of AI rating models are accelerating in parallel, as the industry moves away from territory- and age-based proxies toward property-specific analytics.

P&C Insurance Is Being Rewritten - Quietly but Rapidly.

Filings from the last three years show faster shifts in P&C products than at any point in recent history - and those changes are now surfacing at scale.

Endorsements, exclusions, deductibles, and appetite resets are reshaping coverage and competitive positioning across carriers. But the pace isn't uniform. National carriers, regionals, MGAs, and farm bureaus are moving in different directions - and most teams don't have visibility into how quickly competitors are adjusting forms and filings.

This session breaks down the signals that matter for 2026 product strategy.

‍Drawing on 2M+ carrier filings, Stephanie Kuczynski reveals the real trends reshaping P&C product strategy headed into 2026.

You'll learn what's accelerating, where carriers diverge, and how to translate these shifts into action.

What You Will Gain

• Which changes are accelerating fastest — and where carriers diverge. State-by-state adoption patterns for endorsements, deductibles, and coverage restrictions.
• How strategies differ by carrier size and focus — niche players vs. nationals. The creative, targeted approaches emerging from regionals and MGAs versus the broad strategies deployed by national carriers.
• Where regulators are drawing the line on coverage restrictions. Prepare for closer state-level inspection, due diligence, and shifting expectations across markets.
• How these shifts impact 2026 product strategy and competitive positioning. Why the pace and direction of changes matter — and how to ensure your strategy reflects where the market is heading.

Watch Now