From Costly Water Losses to Millions in Savings

Non-weather water claims are a leading cause of property insurance losses, costing insurers over $20 billion annually. 

Join us for a FREE live webinar where our experts will discuss the latest trends, challenges, and insights to help you mitigate non-weather water risk.

What We'll Cover

Our experts Rob Silva, ACAS (Director of Customer Success) & Sam Fetchero (Head of Marketing) will present:

• Current Trends: Understand the rise in severity and total loss costs of non-weather water claims.
• Risk Assessment Challenges: Learn why traditional methods fall short in assessing non-weather water vulnerability.
• Key Risk Factors: Identify the main drivers of non-weather water damage.
• Strategic Insights: Discover strategies to improve your management of non-weather water claims.
• Z-WATER in Action: Experience our new AI-powered model that predicts non-weather water risk with unparalleled accuracy.
• Interactive Q&A: Get your questions answered by our experts.

Who Should Attend

This webinar is ideal for Executives, Product Managers, Actuaries, Underwriters, and CAT Modelers committed to enhancing their understanding and management of non-weather water risks. 

Bonus Content

As a bonus, you'll receive our exclusive infographic, "Below the Surface: Research Reveals Knowledge Gap in Homeowner Water Loss Prevention and Coverage."

This research gives key insights into water loss experiences, coverage details, homeowner protection measures, and information on water shutoff devices and heater conditions.

Register Now

It’s hard to overstate how important the roof is from an insurability standpoint. The roof represents significant risks and potential opportunities, making it a critical focus area for insurers. This has become even more important in recent years as the impact of severe convective storms is often reflected in roof losses. Understanding this, ZestyAI has released new research for property insurers called The Insurers Guide to Roof Risk.

Download The Insurers Guide to Roof Risk

In an era where the severity and frequency of roof-related claims are on the rise, particularly due to the increasing impact of severe convective storms, innovative tools and strategies are essential. The Insurers Guide to Roof Risk provides actionable insights to improve risk assessment, underwriting processes, and overall business strategy.

What’s Inside the Guide?

The Insurers Guide to Roof Risk includes:

• Roof Failure Factors: Learn the underlying contributing factors behind why older roofs fail more often.
• Beyond Roof Age: Discover why roof complexity, condition, and climate are more important than roof age alone.
• Identifying Missing Risk Factors: Understand the key factors to roof risk that most traditional models miss.
• Advanced Risk Segmentation: See how using machine learning and new data sources can split risk more than 60 times better than traditional models.
• Portfolio Optimization: Access a comprehensive toolbox to optimize your portfolio and new business selection to generate exponential lift versus traditional models.

Download Now

Annual Wildfire Season Overview provides insights to assist insurers in effectively managing wildfire risk.

ZestyAI has released its annual Wildfire Season Overview for 2024. This year’s guide provides critical insights carriers need to stay ahead of the rapidly evolving wildfire landscape. Offering more than just data, this year’s guide is designed to help insurers make informed risk decisions in some of the country’s most volatile states. 

Download ZestyAI's 2024 Wildfire Season Overview 

This year’s guide includes: 

• Countrywide Wildfire Impact Analysis: Understand how wildfires are affecting regions beyond traditional hotspots like CA, including significant events in TX & NM.
• Future Wildfire Trends: Explore predictions for the 2024 wildfire season and understand the long-term implications of current conditions on wildfire risks.
• Regulatory Insights: Stay updated on the latest regulations affecting wildfire risk assessment insurance practices.
• AI-driven Risk Models: Learn how ZestyAI's Z-FIRE model accurately predicts wildfire risks and assists insurers in making informed decisions.
• Property-Level Risk Assessments: Discover the importance of granular, property-specific risk evaluations to improve underwriting accuracy and transparency for consumers.

Download Now

Costing insurers approximately $19 billion every year, roof claims stand as the primary driver of property insurance losses.

Traditional methods of obtaining roof age information are deeply flawed. Most carriers depend on policyholder or agent-reported data, which is often inaccurate, leading to blind spots in assessing property risk. In a recent ZestyAI survey, 63 percent of homeowners reported not knowing the age of their roof if they were not in their homes the last time it was replaced.

Join our expert panel for a deep dive into leveraging roof age analytics for a cutting-edge underwriting process and gain insider knowledge on:

• The Leading Cause of Claims: unveil the hidden truths behind roof-related claims and the costly consequences of outdated assessment methods.
• A New Era of Data: Learn about ZestyAI’s pioneering approach to roof age analytics, incorporating building permits, historical imagery, and AI for a comprehensive view
• Precision at Scale: See how to apply precise, AI-driven roof age data across your entire portfolio for consistent and reliable underwriting and claims decisions
• Technical Decision Making: Empower your actuaries and underwriters with the insights needed to enhance risk selection and optimize pricing strategies
• Efficiency in Operations: Streamline inspections and operations, focusing resources where they’re needed most, improving time-to-quote, and enriching the customer experience
• Best Practices: Learn how leading carriers are using roof age, roof condition, and peril-specific models to improve risk selection and lower combined ratios 

This transformative session is available on demand. Learn how to enhance accuracy, efficiency, and profitability in property insurance.

Save Your Spot

In an ever-evolving regulatory environment, staying informed and adaptable is crucial. Our latest interactive guide, "When Innovation and Regulation Meet", offers a comprehensive toolkit for navigating the complexities of insurance compliance and the integration of AI technologies. 

What's Inside?

• Regulatory Landscape Map: Delve into a detailed state-by-state analysis, uncovering the intricacies of filing laws and approval speeds.
• Staying Ahead of Regulation Changes: Learn how to stay prepared and ensure your compliance strategies are future-proof, aligning with the latest regulatory expectations.
• Essential AI Partner Checklist: Choosing the right AI partner is crucial for success. Our guide offers a meticulous checklist for selecting a partner that is not only compliant but also transparent and supportive, ensuring you make an informed decision.
• Emerging Regulatory Concerns: What do you need to know about privacy, bias, and AI oversight?
• Real-World Applications: Discover how ZestyAI's collaborative approach with regulatory entities has led to successful model approvals across the United States. 

Why This Guide?

As the regulatory framework becomes increasingly complex, having a reliable and insightful resource is indispensable. Our guide is tailored for insurance professionals seeking to enhance their regulatory strategy, embrace AI innovation responsibly, and achieve a competitive edge in the market.

Ready to get up to speed on 2024's regulatory environment? Access the guide.

The landscape of hail risk management is undergoing a significant transformation. Our latest publication, "Hail Risk 2024: An Interactive Guide for Insurers," offers a critical examination, illustrated with compelling data, of the factors contributing to the alarming rise in hail-related losses over the last decade. Major changes to how hail is understood have changed how insurers should view the peril. This guide isn't just about understanding hail risk; it's about redefining how it is managed in the insurance industry.

Why This Guide is Indispensable:

• Losses Rising — Understand the key factors driving record-breaking hail losses, and why there's more to the story than just "climate change."
• Reinsurance  — Learn why insurance carriers now shoulder more of the burden due to changing risk transfer relations.
• The Right Tools  — Explore how AI-based climate risk models are supplementing stochastic and actuarial models for a full picture of climate risk.
• Actionable Steps  — See how leading carriers are applying granular, property-level insights and learn the proactive steps they're taking to mitigate risks and losses.

Ready to get up to speed on hail risk in 2024? Access the guide.

For property insurers, roof age is more than just a data field — it’s a critical underwriting decision point that directly impacts pricing, risk selection, and loss costs. But what happens when that data is wrong two-thirds of the time?

A large U.S. carrier with over $500 million in direct written premium recently found out. Relying on self-reported and agent-estimated roof ages, they were systematically underpricing risky properties while overpricing safer ones. The result: adverse selection, elevated loss ratios, and underwriting decisions built on shaky foundations.

The Scale of the Problem: Two-Thirds of Roof Age Data Is Wrong

ZestyAI’s research shows that 67% of self-reported roof ages are inaccurate:

• 43% underestimate roof age — meaning roofs are older and riskier than reported
• 24% overestimate roof age — leading to overpricing or turning away good business

This isn’t just a pricing issue. Analysis also found that 78% of carriers in key U.S. regions use age-based triggers for ACV roof endorsements, with some starting as early as 8 years old. When roof age is wrong, policies can be misclassified across underwriting, eligibility, and coverage terms — creating compounding risk across the insurance lifecycle.

From Estimates to Evidence: How ZestyAI Verifies Roof Age

To replace guesswork with ground truth, the carrier deployed ZestyAI Roof Age, which analyzes building permits, more than 20 years of aerial imagery, and regional climatology using advanced machine learning. Each assessment is paired with a transparent confidence score.

Unlike traditional approaches that rely on policyholder memory or limited inspections, ZestyAI Roof Age:

• Anchors assessments in the property timeline to prevent false positives
• Cross-validates imagery with permits and climatological patterns
• Provides confidence scores to distinguish high-certainty predictions from cases requiring inspection
• Delivers explainable, auditable results that underwriters and actuaries can trust

The difference was immediate.

Real-World Examples from the Carrier’s Portfolio

In one Denver property, the agent reported an 8-year-old roof. ZestyAI identified it as 10 years old, confirmed by aerial imagery showing the replacement event.

In a Baltimore case, what was reported as a 5-year-old roof was actually 21 years old — verified through imagery and permitting history.

These weren’t edge cases. They reflected a systemic pattern across the portfolio.

The Impact: A 1.71% Improvement in Combined Ratio

By integrating verified roof age into underwriting and pricing workflows, the carrier achieved a 1.71% reduction in combined ratio. The improvement came from three measurable levers:

• Loss Cost Controls (-1.08%)
  Accurate age enabled appropriate use of deductibles and ACV endorsements, lowering claims severity.
• Better Risk Selection (-0.38%)
  More precise pricing attracted lower-risk properties while deterring higher-risk ones.
• Inspection Optimization (-0.25%)
  Confidence scores guided inspections to properties that truly needed them, reducing wasted expense.

Beyond loss ratios, better roof age data improved portfolio transparency, supported expansion into previously restricted markets, and strengthened actuarial and underwriting decision-making.

What’s Next: Expanding the Foundation of Property Intelligence

After proving the value of accurate roof age, the carrier is now building on that foundation. They are incorporating additional property attributes — including roof complexity, roof quality, and parcel-level features — through ZestyAI’s Z-PROPERTY™ platform.

By standardizing and elevating property data quality at scale, the carrier expects to unlock similar gains across quoting, underwriting, renewals, and even reinsurance discussions.

The takeaway is clear: in an industry built on precision, even a single data point — when made accurate — can deliver outsized impact.

Read the full Roof Age Accuracy case study to see how verified roof age drives measurable underwriting and pricing gains → From Self-Reported to Verified: Roof Age Accuracy That Pays Off

ZestyAI today announced that Augusta Mutual has selected ZestyAI’s Roof Age and Z-PROPERTY™ to enhance underwriting and rating accuracy, target inspections more effectively, and support sustainable growth across Virginia.

Based in Staunton, Virginia, Augusta Mutual is a single-state carrier serving Virginia since 1870 with a longstanding reputation for personalized service and local expertise. By upgrading from traditional imagery and inspection approaches to ZestyAI’s computer vision and machine learning technology, the insurer gains broader, more consistent property coverage and a more comprehensive, AI-driven view of property risk—unlocking property-level insights such as verified roof age, roof condition, vegetation overhang, and debris accumulation that directly influence claim frequency and severity.

“ZestyAI’s solutions bring a new level of precision to our underwriting process,” said Gretchen H. Collins, Vice President of Underwriting at Augusta Mutual.

“We moved from legacy property risk tools to gain broader, verified property coverage, helping us make faster, more consistent, and more confident decisions for our policyholders across Virginia.”

ZestyAI’s Roof Age delivers verified roof age by cross-validating building permit records with over 20 years of aerial imagery, detecting roof replacement events and assigning confidence scores across 97% of U.S. properties. Z-PROPERTY™ further enhances this insight by assessing roof complexity, materials, and condition, along with other parcel-level attributes that influence loss potential.

ZestyAI works closely with regulators to ensure transparency, validation, and continuous monitoring of its AI-driven models. Its portfolio of risk models has secured nearly 100 approvals from regulators nationwide, giving insurers confidence they can be deployed immediately with the accuracy and transparency regulators demand.

By Attila Toth, Founder & CEO of ZestyAI

The U.S. P&C industry enters 2026 with stronger balance sheets, renewed underwriting profitability, and a sense that the hardest part of the cycle may be behind it. But beneath the surface, the risk environment is moving in the opposite direction. Climate-driven loss volatility, localized catastrophe patterns, and structural property vulnerabilities are accelerating — even as markets begin to soften.

The result is a widening gap between carriers chasing growth and those wiring discipline deeper into how risk is selected, priced, and managed. Here are three dynamics that will define P&C performance in 2026.

1 — A Softer Market Meets a Hard Climate Reality

The industry enters 2026 from a position of renewed financial strength: the last couple of years produced the best U.S. P&C underwriting results in more than a decade, with combined ratios improving into the mid‑90s and a clear swing back to underwriting profit. 

Capital has rebuilt, competition is intensifying in many property segments, and some markets are now seeing flat or slightly negative renewals, encouraging carriers to cautiously re‑enter territories that were pulled back during the hard market. 

The risk environment, however, has not softened; insured catastrophe losses have exceeded USD 100 billion for multiple consecutive years, and recent nat‑cat studies now describe annual insured losses approaching USD 150 billion as the emerging “new normal,” driven disproportionately by severe convective storms, wildfire, localized flooding, and non‑weather water losses rather than a single headline hurricane season.

In 2026, carriers will not move in lockstep. Some will quietly relax property underwriting and broaden appetite to chase top‑line volume in what feels like a more forgiving market, even as U.S. SCS losses alone have entered a period where annual insured losses now consistently exceed USD 40 billion, while others will double down on discipline by wiring property‑level climate and vulnerability metrics into day‑to‑day decisions. 

Early in the year, the visible story may favor the volume‑chasers as premium growth accelerates, but by late 2026 the more revealing story will be in loss ratios, with hail‑, SCS‑, wildfire‑adjacent, and water‑heavy portfolios that were loosely underwritten posting the most uncomfortable deterioration.

2 — Hyperlocal Exposure Management Becomes a Core Profit Lever (and Reinsurers Will Expect It)

Even with some rate relief on better risks, carriers face a structural problem going into 2026: loss volatility is increasingly driven by frequent, highly local events and structural property issues rather than a single major catastrophe. 

A two‑block hail cluster, an ember‑exposed hillside parcel at the wildland–urban interface, or aging roofs can generate thousands of mid‑sized claims that erode margin even when headline cat activity looks “average.” 

When property‑level secondary modifiers are missing or stale, catastrophe models and capital providers default to conservative assumptions, inflating modeled losses, uncertainty loads, and reinsurance costs; reinsurers are responding by demanding clearer visibility into roofs, vegetation, defensible space, elevation, and mitigation before offering the most favorable terms.

In this environment, hyperlocal exposure management is becoming a core profit lever rather than a niche analytics exercise. Leading carriers are using verified parcel‑level attributes to identify frequency‑prone parcels inside ZIP codes that look stable in aggregate, to counter overly conservative model assumptions with auditable evidence, and to walk into reinsurance renewals with property‑level documentation rather than broad averages. 

They are steering appetite, pricing, inspections, and mitigation strategies on a near‑real‑time basis instead of waiting for annual rate cycles, effectively trading unmanaged volatility for intentional, data‑driven control. The net result is that 2026 will reward carriers that can prove property‑level truth to reinsurers, regulators, and their own underwriting teams, replacing assumptions with evidence and episodic adjustments with continuous portfolio management.

3 — Agentic AI Becomes Insurance’s Next Operating System

2026 is shaping up as the year agentic AI shifts from experimental to essential in P&C, as carriers discover that the binding constraint is no longer access to data but the speed, consistency, and defensibility of decisions across underwriting, filings, compliance, and product change. Risk conditions are moving materially faster than traditional annual guideline refreshes can accommodate, supervisors and rating agencies are sharpening expectations around explainability and consistency, and decades of underwriting and regulatory expertise are retiring faster than they can be replaced.

Across the market, early adopters are already using agent‑like systems to flag likely regulatory objections before filings go in, compress filing and approval timelines from months to weeks, and synthesize competitive and regulatory intelligence with strong safeguards and human‑in‑the‑loop review. These systems are also starting to refresh underwriting and pricing playbooks using live property‑risk signals instead of static territorial assumptions, closing the loop between climate data, filings, and front‑line decisions. For many carriers, 2026 will be remembered as the year AI stopped being primarily predictive and became operational infrastructure — software that can understand intent, reason through complex rules, coordinate multi‑step workflows, and take auditable action alongside human teams.

How Leading Carriers Are Responding

The most forward-positioned carriers entering 2026 are already using parcel-level intelligence to refine appetite, pricing, inspections, and mitigation in high-hazard and water-exposed regions, treating hyperlocal data as a core underwriting input rather than an afterthought.

They are refreshing eligibility criteria and underwriting guidelines based on property-specific hazard, vulnerability, and mitigation features, and preparing regulator-ready and reinsurer-ready documentation on defensible space, roof condition, and other secondary modifiers.

They are steering portfolios continuously, adjusting aggregates, concentrations, and mitigation incentives throughout the year instead of relying solely on renewal season to reset course. Together, these behaviors signal a broader shift away from episodic, once-a-year recalibration toward continuous, property-level risk management supported by AI-enabled operating systems.

Insurance availability has become a constraint on the housing market.

That’s the central argument Ross Martin, VP of Risk Analytics at ZestyAI, makes in ATTOM’s newly released Q4 2025 Housing News Report—and it’s one that will increasingly shape affordability, underwriting, and buyer behavior heading into 2026.

Housing discussions still focus on mortgage rates and inventory. But in many markets—especially catastrophe-exposed ones—insurance is becoming a gate in the transaction. If a property can’t get insured, or coverage is uncertain, deals stall. And when insurance costs spike unexpectedly, affordability breaks even when the mortgage penciled out.

Ross’s point isn’t simply that insurance is getting more expensive. It’s that availability and predictability now matter as much as price—and the market would function better with clearer, property-level risk signals.

Today, homes in similar locations can carry meaningfully different risk based on factors like roof condition and materials, defensible space and vegetation management, yard and debris conditions, and documented improvements captured in permits or listing data. When those distinctions aren’t consistently reflected in underwriting or pricing, mitigation efforts go unrewarded—and higher-risk properties don’t get early, property-specific signals to improve.

For insurers, this lack of granularity creates real portfolio risk. When individual properties aren’t differentiated clearly enough, volatility increases, adverse selection becomes harder to avoid, and long-term participation in catastrophe-exposed markets becomes less sustainable. Property-level, mitigation-aware models help address this by improving segmentation and enabling insurers to stay in market with more confidence.

Recent advances in property-specific data and modeling now make this differentiation possible at scale. Insurers can assess dozens of attributes—including roof age and materials, defensible space, vegetation conditions, building permits, occupancy type, and hazard-specific science—to build a clearer view of a structure’s vulnerability. Just as importantly, these models can recognize mitigation actions—like roof replacements, defensible space creation, and debris removal—and incorporate them more consistently into underwriting and pricing.

When mitigation is visible and rewarded:

• Homeowners and investors gain more control over premiums
• Insurers can maintain more stable portfolios, even in high-risk regions
• Housing markets get clearer signals—making insurance availability and long-term cost less of a guessing game for buyers and lenders

Regulators are paying attention as well. In several states, regulators are examining how property-level data, mitigation, and modern risk modeling approaches can be incorporated more consistently into rate structures, with transparency as a common objective.

The takeaway is straightforward: insurance is shifting from a background cost to an active constraint—and clearer, property-level risk signals are key to easing that constraint. As 2026 approaches, the ability to differentiate risk at the individual property level will play a growing role in restoring predictability, supporting availability, and shaping housing market outcomes.

Read the full article, “The Insurance Shift Reshaping the 2026 Property Market,” in ATTOM’s Q4 2025 Housing News Report.

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By Abdul Mohammed, Director of Product Marketing, ZestyAI

Every year, carriers and filers submit hundreds of thousands of rate, rule, and form transactions to state insurance departments (DOIs), many through SERFF. In 2023 alone, SERFF processed 517,571 transactions (NAIC SERFF, reported 2025). These filings are the DNA of the insurance market: the definitive record of how competitors set rates, where they plan to expand, and where they get approval for new ideas or pull back.

Even though much of this information is publicly available in many jurisdictions, with different rules for access and confidentiality, most carriers still miss out on the competitive signals hiding in plain sight. 

Key takeaways

• Regulatory filings are a strategic dataset, not just compliance paperwork. They reveal competitor intent and market shifts.
• “Public” does not mean “easy to analyze.” Filings are often massive, cross-referenced, and inconsistently structured, making manual review at scale impossible.
• The winning approach is a filing intelligence stack. Success requires filing data tracked across amendments and effective dates, structured parsing, deterministic calculation, and precise citations.

How Filings Got So Complicated

From 1945 to The Modern Filing Ecosystem

The story begins with the McCarran-Ferguson Act of 1945, which gave states the power to regulate insurance rather than the federal government. As a result, requirements differ by state, making rates, rules, and forms complicated across jurisdictions. 

In 1998, the National Association of Insurance Commissioners (NAIC) introduced SERFF (System for Electronic Rate and Form Filings), developed in collaboration with regulators and industry to digitize rate and form submissions. While it successfully moved filings online, the underlying complexity of the content remained.

Since then, the volume and complexity of filings have exploded. Our analysis of SERFF filing packages shows that the largest homeowners’ rate filing now exceeds 300,000 pages, including attachments, exhibits, and correspondence. In our data, the biggest package grew from 38,102 pages in 2009 to 346,064 pages in 2024.

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Why Public Filings Remain Inaccessible for Analysis

A public filing is not always easy to access or understand. Carriers know that regulators, consumers, and competitors will review their filings, so the resulting documents are often dense, cross-referenced, and hard to piece together. If you have ever tried to reverse engineer a competitor rate change, you have probably faced these challenges:

The Trade Secret Exception 

Some carriers request confidential treatment for specific elements of a filing that may qualify as trade secrets under state rules, such as granular territorial data or specific model inputs. When those sections are redacted or withheld, you lose visibility into important details even though the overall filing is public, and the level of protection varies by jurisdiction.

"When a Product Filing contains Trade Secret information, the Product Filer may identify those portions of the Product Filing, including correspondence with the Compact Office, that contain Trade Secret and seek to protect their disclosure." 

Interstate Insurance Product Regulation Commission, FIN 2024-1

The Reference Maze 

Sometimes, instead of putting all rate information in one document, a filing will reference several other filings across multiple years. To fully understand the change, an analyst has to track down and reconcile multiple historical filings, creating a confusing trail of "breadcrumbs"  that is almost impossible to follow manually.

The PDF Image Trap 

Many carriers submit rate tables as scanned images in PDFs instead of machine-readable text. While human eyes can read these tables, most data tools and basic OCR software treat them as pictures, so the information cannot be easily searched, filtered, or analyzed at scale.

The Objection and Response Trail

Often, the most valuable intelligence isn't in the initial filing, but in the "Objection and Response" exchanges between the carrier and the state regulator. These discussions can reveal rationale, supporting evidence, and the boundaries regulators will accept. However, this material is often spread across multiple attachments and correspondence, making it easy to miss critical insights without a structured way to collect and review them.

Non-Standard Nomenclature

There is no universal dictionary for insurance variables. One carrier might call the roof age factor “rf_yr_mod”, while another uses “const_age_rel”. Without a way to normalize these labels, mapping equivalent factors across carriers becomes manual work, making benchmarking slow, error-prone, and difficult to repeat.

Why General Purpose LLMs Often Struggle Here

We are in the age of Generative AI, so the natural question is: "Why not just upload these PDFs into a tool like ChatGPT or Gemini and ask questions?"

You can upload filing PDFs to ChatGPT or Gemini, and you may get a helpful summary. But insurance filing work is not “writing assistance.” It is a precision workflow in which small mistakes lead to incorrect conclusions. General-purpose LLMs are built to generate plausible text from the input you give them, not to reliably preserve filing structure, track versions, run exact calculations, and produce audit-ready citations. 

The following are the top reasons why general LLMs often struggle in the inusrance domain:

1. The Math Problem

Insurance filings require exact math and exact linkage across tables, factors, relativities, and formulas. LLMs are probabilistic; they predict likely answers rather than performing exact calculations. If you ask an LLM to calculate a 3.5% rate increase over three years using a specific table, it may give a confident answer that is still wrong. In insurance, even a 0.01% mistake can mean millions in lost premium. As actuarial researchers noted in a 2024 paper from Cambridge University Press, “while LLMs can explain concepts, they often provide inaccurate or incorrect mathematical facts, sometimes in subtle ways.”

2. Structure Blindness

LLMs are mostly trained on regular text, such as books and articles. They are not skilled at understanding tables, following footnotes, or applying formulas consistently across multi-part exhibits, especially when documents are scanned or formatted inconsistently. A standard LLM treats a table as plain text and often fails to understand how the cells are logically and mathematically connected.

3. Context Window Overload

State filings are often longer than what standard LLMs can handle. If you give a model a 2,000-page document, it may lose track of what appeared earlier in the filing and still try to answer questions as if it remembered everything. This can cause the AI to make up numbers (hallucinate) to fill in missing information. 

What a Modern Filing Intelligence Approach Looks Like

The industry does not need a better chatbot. It requires a filing intelligence stack that combines structured data, deterministic computation, and auditable reasoning.

Below are the key steps in building this intelligence stack:

Build a clean, versioned filing archive 

Ingest filings continuously and preserve them with consistent metadata such as state, carrier, line, status, effective date, and relationships to related submissions. This creates a single, reliable system of record for all filing history.

Parse filings into insurance native components

Break filings into rates, rules, forms, exhibits, objections, and responses. Store rating tables, factors, and hierarchies as structured data instead of plain text, so they can be queried and reused.

Pair language with deterministic calculation

Use deterministic engines for calculations and rate reconstruction, then use language models to explain the results, clarify their meaning, and support structured analysis. The math engine produces the numbers; the LLM explains what they mean.

Make everything traceable

Every conclusion should link back to the exact filing section it came from. This traceability is what turns AI output into something regulators, actuaries, and executives can trust and defend.

The Future Belongs to the Agile

The number of filings and their sizes keep growing. As climate risk reshapes markets, rate reviews and underwriting changes will happen more often. The carriers who succeed will be those who treat regulatory filings as a source of strategic insight rather than a compliance burden. 

Those who don’t will pay the price. Without visibility into how competitors are changing rates, rules, and eligibility in near-real time, carriers slip out of sync with the market, underwriting yesterday’s risk at today’s loss costs. That gap is where adverse selection takes hold.

Having access to filings is not enough to gain an edge. The real advantage comes from understanding them quickly and accurately, in a way that can be repeated across teams and product lines. ​​To do this well, you need systems built for the job, not just a general-purpose model. This is where a purpose-built filing intelligence stack changes what is possible.