Reports & Research

Across the P&C industry, delayed rate approvals are costing an estimated $72.8 million per day in foregone premium — and the largest single category of objection causing those delays is procedural, not substantive. A P&C Specialist article published May 13, 2026 by Jennifer Ortakales Dawkins covers a ZestyAI analysis of more than 2 million P&C rate and form filings on SERFF, with commentary from ZestyAI's senior director of regulatory and government affairs Bryan Rehor on what's actually driving objection cycles and how filing teams can systematically reduce them.

Read the full article in P&C Specialist →

How much does P&C rate filing delay actually cost?

About $72.8 million a day across all lines, per the ZestyAI analysis. That figure is the aggregate cost of delayed approvals translating into lost premium — premium the rate change would have produced if it had taken effect when intended rather than weeks or months later. The cost compounds two ways: directly, in foregone premium for the period of delay, and indirectly, in continued exposure to the loss patterns the rate change was meant to address.

It also lands unevenly. California took a median of 267 days to approve a rate filing in 2025; Maryland took 206. At the other end of the spectrum, use-and-file states like Wisconsin clear filings in a median of three days, and Wyoming doesn't require rate filing at all. Most of the daily-cost burden concentrates in the slow-approval states.

What causes most rate filing objections?

According to the ZestyAI analysis, 74% of carriers receive objections on at least half of their filings. The most common reasons are procedural rather than substantive: submission gaps, missing or inconsistent supporting exhibits, unclear rationale for the rate change, and incomplete responses to regulator inquiries.

"Submission gaps are a very common and avoidable cause of delay," Rehor told P&C Specialist. The deeper challenge is that each state has its own filing requirements, and the volume of state-specific procedural rules makes it easy to miss something even when the substantive content of the filing is sound.

Which states have the highest rate filing objection rates?

For homeowners filings, the top objection rates are concentrated in the Northeast and the largest markets: New Jersey (87.7%), Massachusetts (87%), New York (84.5%), California (83.1%), and Texas (80.9%). For auto, California (87%) and Massachusetts (85.2%) lead, followed by New Jersey, Texas, Kansas, and Michigan.

Texas has the highest absolute number of objections in both lines, but its overall objection rate is moderated by very high filing volume — more than double the second-place state in either line. The most common reasons for objections in Texas were underwriting errors in home filings and missing or incorrect values in auto filings.

Why does the second objection round matter so much?

Because delay compounds, not stacks linearly. ZestyAI's data shows that after the first objection, each additional challenge adds approximately two months to the filing process as review clocks reset and the scope of scrutiny expands. That's why preventing the first objection is worth more than resolving it efficiently. Filing teams that systematically reduce procedural gaps before submission collapse the timeline far more than teams that respond well to objections after they arrive.

What separates fast-moving filing teams from slow ones?

Less about regulatory environment than execution. The P&C Specialist article includes practical guidance from state insurance departments — a Pennsylvania regulator's reminder that carriers should actually use the department's checklist before submitting, and a Washington regulator's note that subjective language in rate manuals ("above average," "better") will get flagged because regulators require any two people reading the manual to arrive at the same premium for the same risk.

The unifying point: filing teams that internalize the procedural patterns regulators care about — checklists, supporting exhibits, specific language, complete responses — systematically reduce both objection volume and the number of objection rounds. That's the operational gap behind the $72.8M-a-day cost. It's mostly addressable.

Read the full article

The Industry's $73M-a-Day Problem: Rate Filing Delays →

P&C Specialist, May 13, 2026, by Jennifer Ortakales Dawkins. Includes state-by-state objection data from ZestyAI's analysis, additional commentary from regulators in Pennsylvania and Washington and actuarial consultants at Perr&Knight, and detailed examples of what drives objection cycles in the slowest-approval states.

AI-driven property models in U.S. home insurance are shifting from experimental tools to a baseline capability for underwriting — and the economics of consecutive record catastrophe years are accelerating the move. P&C Specialist reporter Vrushank Nayak detailed the shift in a May 2026 analysis, Ignore AI Property Models at Your Own Risk?, featuring perspectives from ZestyAI co-founder and chief product officer Kumar Dhuvur and senior director of regulatory & government affairs Bryan Rehor.

Read the full article in P&C Specialist →

What's actually driving the shift?

Per Dhuvur, the pressure is economic, not technological. The U.S. property insurance market absorbed roughly $89B in insured natural catastrophe losses in 2025 and $108B in 2024 per Swiss Re Institute — what Dhuvur described as "the most expensive operating environment in the history of U.S. property insurance," with no visible path back to historic loss levels. In that environment, the territory-level averages and patchy property-level inputs underwriters have historically relied on aren't fine enough to differentiate risk anymore.

What changed on the supply side is just as important. Aerial and satellite imagery, combined with computer vision, now generate consistent structure-level signals at portfolio scale. Roof condition, structural characteristics, visible damage, and prior exposure can be evaluated across an entire book in the same way they'd be assessed on a single inspected property. That capability didn't exist seven or eight years ago.

How widespread is AI property model adoption today?

Dhuvur estimates roughly half of U.S. carriers now use AI property models in some form, with underwriting the most common use case. Pricing adoption is slower, in part because of regulatory complexity. The P&C Specialist analysis of state rate filings identifies major national carriers — Nationwide, Liberty Mutual, Allstate, and State Farm — among those citing third-party AI property models in homeowners filings, alongside specialty and regional carriers using a wider mix of providers.

Why don't rate filings always reflect actual AI model usage?

This is where Bryan Rehor's regulatory perspective in the article matters most. Filing citation counts can understate real adoption because filing rules differ state to state. Some states require carriers to resubmit their full rating manuals with each filing; others require only the portions that have changed. In change-only states, carriers may continue using an AI model year over year without re-citing it in every filing. Colorado, for example, doesn't require the AI model to be mentioned in every rate filing, but does require carriers to document and govern any model in use, because regulators can audit at any time.

The implication: market-wide AI property model adoption is harder to read from filings alone than the raw citation counts suggest.

What's the cost of moving slowly?

Dhuvur framed the strategic risk as a compounding adverse-selection problem. When some carriers price properties at their true individual risk and others continue pricing on territory averages, the precise pricers systematically capture the better risks and the territory pricers absorb the worse ones. Over time, that gap compounds into loss ratios, retention, and growth — and the carriers most exposed are the ones whose competitors have already moved.

As AI property models shift from differentiator to default, the question isn't whether to adopt. It's how to govern adoption credibly enough to satisfy regulators while still capturing the underwriting gains.

Read the full article

Ignore AI Property Models at Your Own Risk? →

P&C Specialist, May 4, 2026, by Vrushank Nayak. Includes additional commentary from Patrick Schmid at the Insurance Information Institute and other experts, plus detailed analysis of which carriers and states are citing AI property models in current rate filings.

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

Severe storm losses don't begin when a storm materializes. They're shaped earlier — by underwriting and renewal decisions, by the quality of underlying property data, and by how risk accumulates across a portfolio over time. A new ZestyAI on-demand session, featuring Robert Silva, ACAS (formerly Farmers Insurance) and Keren Chheang, FCAS (formerly Wawanesa), examines the upstream patterns driving 2026 severe convective storm (SCS) outcomes — and where traditional CAT models are diverging most sharply from realized loss.

About this session: Loss Happens Before the Storm: The New Drivers of 2026 Severe Storm Risk is an on-demand webinar covering why storm losses vary dramatically within the same ZIP code, what three record SCS years (2023: $66B, 2024: $59B, 2025: $51B) reveal about portfolio volatility, and the underwriting and pricing decisions carriers are making now. Featuring Robert Silva, ACAS, and Keren Chheang, FCAS.

Prefer to watch instead? Access the full on-demand session → — includes property-level signals, CAT model gaps, and live Q&A.

When are 2026 severe storm losses actually being set?

Earlier than most carriers price for. Once a storm forms, the loss outcome on any given property is largely determined by conditions that were locked in months or years before: roof age and condition, the property characteristics the carrier underwrote on (or didn't), the renewal decision made at the last anniversary, and the aggregate exposure the portfolio quietly accumulated through prior cycles. The storm is the trigger. The loss was already set up.

That's the framing shift behind this session. Carriers that treat storm season as the moment exposure crystallizes consistently arrive too late to influence the loss outcome.

Why are traditional CAT models misaligned with realized loss?

Because the gaps that drive loss aren't event-driven, they're data-driven. Traditional CAT models work at a level of geographic and structural abstraction that smooths over property-level variability — roof age, prior weathering, soft-metal exposure, structural state. When the building stock is uniform, that abstraction works. When it isn't — and three record years of accumulated damage mean it isn't — the modeled loss and the realized loss diverge. Carriers feel this as adverse surprise in seasons the model didn't flag.

What upstream decisions actually drive portfolio-level storm outcomes?

Four levers, in roughly the order they take effect:

• Underwriting data quality. The accuracy, coverage, and consistency of property-level information at the point of underwriting sets the ceiling for every downstream decision.
• Rating and segmentation choices. Whether structural signals — roof age, condition, prior loss exposure — are priced into the rate, or smoothed into a territory average.
• Renewal decisions. Whether deteriorating properties are repriced, repositioned, or non-renewed before the next season, or carried at the prior year's terms.
• Portfolio accumulation. Whether risk is concentrating in degraded properties or in better-conditioned ones across the book.

Each of these decisions happens long before a storm forms. Each shapes how much loss any future storm will produce.

How do property-level signals change underwriting and renewal decisions?

Property-level signals — roof condition, accumulated weathering, prior structural exposure — let carriers segment risk before it shows up as a claim. The session walks through how carriers are using this layer of intelligence to inform underwriting decisions, calibrate rating plans against structural realities the territory map can't see, and identify properties that quietly migrated from "acceptable" to "deteriorated" since the last renewal. The point isn't to write less business. It's to know which business is changing before the storm reveals it.

What this means for 2026 storm strategy

As the 2026 season takes shape, many of the loss outcomes carriers will record this year are already being set — through underwriting decisions being made now, through renewal terms being finalized, through portfolio accumulation patterns that won't be visible until they show up as concentrated loss. The session is built for product, underwriting, pricing, portfolio, actuarial, CAT, and reinsurance teams shaping how their organization shows up before the first storm forms.

Watch the full session on demand

Loss Happens Before the Storm: The New Drivers of 2026 Severe Storm Risk →

Featuring Robert Silva, ACAS (formerly Farmers Insurance) and Keren Chheang, FCAS (formerly Wawanesa), the session goes deeper on CAT model divergence, the four upstream loss drivers, and what differentiated underwriting can move on 2026 portfolio outcomes.

Watch the on-demand recording

Three consecutive record years — $66B in insured losses in 2023, $59B in 2024, and $51B in 2025 — have reshaped how carriers need to think about severe convective storm risk. A new ZestyAI executive briefing on the 2026 SCS season shows the defining feature of these losses isn't storm frequency. It's accumulated structural damage. Within the same ZIP code, sometimes within the same complex, properties exposed to identical storms are producing materially different outcomes — and territory-level analysis is smoothing over the dispersion that actually drives the loss.

About this analysis. Findings are drawn from analysis of recent SCS seasons, IBHS research, and property-level storm segmentation across U.S. portfolios. The full briefing — 2026 Severe Convective Storm Season Overview — is a 16-page executive report covering loss dispersion patterns, peril-specific modeling, and what differentiated underwriting can actually move on portfolio performance.

Want the data? Download the full briefing → — 16 pages, immediate access.

What's driving three consecutive record SCS years?

Severe convective storm losses have accelerated steadily over the past five years, with sustained elevated activity beginning in 2020. The pattern of 2023, 2024, and 2025 — three of the highest insured-loss years on record back to back — isn't random. Underlying storm frequency has increased, but the cumulative effect on the building stock is the part most often underestimated. Each season of hail and wind exposure leaves a portfolio in a slightly more vulnerable state going into the next.

Why doesn't ZIP-code geography explain SCS loss patterns anymore?

Territory-level analysis smooths real differences between buildings. Two homes on the same street can face the same hailstorm and produce very different claims depending on roof age, roof material, prior exposure, defensible space, and the structural state of soft metals like gutters and vents. Blending those properties into a ZIP code average hides volatility that materially changes portfolio performance under stress.

That's the structural-not-geographic shift. Identical storms produce different outcomes because the structures aren't identical — and territory-level segmentation can't see it.

How does accumulated structural damage compound SCS risk?

This is the part of the story regulators and reinsurers are starting to focus on. Small hail events and ongoing weathering degrade roofing systems over time, even when no individual storm triggers a claim. Each marginal event leaves the roof slightly more vulnerable. Over a multi-year SCS cycle, the portfolios most exposed to losses are often the ones that quietly accumulated the most undamaged-but-degraded properties through prior seasons.

The implication is that a portfolio's SCS risk profile in 2026 isn't fully captured by its 2025 loss experience. The risk that didn't claim is still on the books.

Why does peril-specific modeling matter for hail and wind?

Hail, wind, and recurring micro-exposure don't behave the same way within an SCS footprint, even when they're all grouped under the same broad peril category. Hail damage concentrates in dense, often spatially clustered loss events. Straight-line wind produces a different damage signature. Recurring small-hail exposure compounds slowly across seasons. Modeling all three as a single SCS exposure obscures the separation that property-level intelligence can actually pick up — and that differentiated underwriting can actually act on.

What can property-level segmentation actually change?

In a Texas retrospective covered in the briefing, targeted underwriting actions informed by property-level segmentation would have reduced modeled loss cost by 48%. That's the magnitude of separation hiding inside portfolios that look uniform at the territory level. It's also what makes the 2026 question simpler than it sounds: not whether storms will develop, but whether the portfolio is segmented precisely enough before they do.

Get the full briefing

2026 Severe Convective Storm Season Overview →

The executive briefing walks through the multi-year SCS loss pattern, how property-level segmentation separates loss outcomes within the same footprint, and the Texas retrospective showing what differentiated underwriting can move on portfolio performance.

Regulatory delays in P&C filings are predictable, compounding, and largely fixable. A new ZestyAI research analysis of where filings actually break down shows the problem isn't primarily oversight — it's execution. Breakdowns cluster around a small number of recurring failure points, almost all of them early in the process, and once an objection cycle starts, delay compounds disproportionately with each round. The result is months of slippage that quietly erodes pricing effectiveness and consumes scarce actuarial and regulatory capacity.

About this analysis. Findings are drawn from analysis of recent P&C rate, rule, and form filings using ZORRO Discover, ZestyAI's AI agent purpose-built for insurance-specific research. The full report — Where P&C Filings Go Off Track — is an 8-page research analysis of filing breakdowns and the execution patterns that separate teams that manage delay from those that systematically reduce it.

Want the data? Download the full report →

Where do P&C filings actually go off track?

Not where most teams assume. Filings don't typically stall during deep technical review by regulators — they stall earlier, during submission, packaging, and initial support. Procedural gaps and missing exhibits trigger objections before reviewers ever engage with the substantive content of the filing. That's the first counterintuitive finding: a meaningful share of delay is generated before the regulator has even started evaluating the rate change, model factor, or rule update being filed.

The implication is that delay is largely an upstream problem with upstream fixes.

Why does delay compound once it starts?

Each objection round resets the review clock and expands the scope of scrutiny. What looks like a small clarification request adds weeks; a second cycle adds months; a third compounds further as the reviewer's questions broaden to adjacent assumptions. Once a filing is in the objection loop, timelines stretch disproportionately, not linearly. This is the second pattern: delay is non-linear, and most filing teams don't price the compounding cost of the second and third cycle into their internal timeline forecasts.

The carriers that move fastest are the ones that prevent the first objection — not the ones that respond to it well.

What does a late effective date actually cost?

The cost of delay is measurable, even when it's not always measured. Late effective dates scale impact in two directions at once: across the premium book affected by the change, and across the time the carrier operates under the prior (often inadequate) rates. A rate change that earns full approval three months late doesn't just lose three months of expected premium — it continues exposing the carrier to the loss patterns that motivated the change in the first place. Meanwhile, actuarial and regulatory teams burn cycles managing the objection process rather than building the next filing.

What do high-performing filing teams do differently?

They treat approval readiness as an operating capability, not a one-time deliverable. High-performing teams standardize how filings are packaged, build internal review against the procedural patterns regulators care about most, and use the institutional history of prior objections — their own and the market's — to anticipate the questions a reviewer will ask before the reviewer asks them. The performance gap between organizations isn't about regulatory environment or filing volume. It's about whether the filing function is built for speed.

What this means for 2026 product execution

As filings grow more complex and regulatory expectations rise, the gap is widening between organizations that manage delay and those that systematically reduce it. The pricing decisions, product changes, and risk responses being filed today only matter to the extent they reach the market when intended. Approval readiness is increasingly what separates carriers that translate strategy into results from those that watch their best work stall in the review queue.

Get the full report

Where P&C Filings Go Off Track →

The 8-page report walks through the specific breakdown patterns, where objection cycles start, how delay compounds across rounds, and how high-performing teams systematically reduce both.

Download the report — or request a trial of Zorro Discover to access the institutional filing history that lets teams anticipate regulator objections before they arrive.