ZestyAI Warns Insurers Are Paying an “Uncertainty Premium” Due to Critical Property Data Gaps

Incomplete property data is forcing insurers to pay an "uncertainty premium" in the reinsurance market, as gaps in exposure information lead to conservative modeling assumptions. For carriers, MGAs, and reinsurers, this disconnect means capital is being priced against data quality rather than actual risk, potentially penalizing high-quality portfolios and distorting market capacity.

What was announced

ZestyAI released a whitepaper titled "The Uncertainty Premium" following a live poll of insurance professionals. The research found that 71% of respondents identified incomplete or outdated exposure data as the primary hurdle when evaluating property catastrophe risk. This far outweighed other concerns like differing stakeholder views of risk (13%), difficulty validating third-party data (11%), and a limited ability to distinguish risk within similar portfolios (3%). The whitepaper, which analyzed 94 responses to this question, highlights how these data gaps force reinsurers to use conservative defaults in catastrophe models.

These assumptions can lead to higher modeled losses, affecting attachment points and available capacity. A key example cited in the research is roof age. ZestyAI found that 67% of policyholder-reported roof ages differ from verified ages by at least three years. In 43% of cases, roofs are at least three years older than reported, which understates risk. Conversely, 24% are at least three years newer, meaning some risks are overpriced or declined unnecessarily. Furthermore, 78% of carriers examined across the Midwest, Great Plains, and Southeast link Actual Cash Value (ACV) roof endorsements to the age of the roof, with triggers starting as early as eight years. This means a discrepancy of just a few years can fundamentally change rate, eligibility, and coverage terms for a portfolio.

"Reinsurance pricing is only as good as the exposure data behind it, and for most of the industry that data is still self-reported, inspected once and left to age. When reinsurers can’t verify what a carrier knows about its portfolio, they have to price the uncertainty. That means a strong book can end up looking much like a weaker one."

Attila Toth, Founder and CEO of ZestyAI.

The companies involved

ZestyAI is a specialist in climate and property risk analytics, utilizing artificial intelligence to provide high-resolution data on every property in North America. The company focuses on helping carriers and MGAs improve underwriting and rating accuracy through its AI-driven models, which analyze satellite and aerial imagery alongside other data points. ZestyAI has established a significant footprint in the US market, particularly in regions prone to wildfires and severe convective storms.

The firm recently reached a major financial milestone, becoming cash flow positive, which distinguishes it from many earlier-stage insurtech ventures still reliant on venture capital infusions. Its technology is increasingly integrated into the workflows of major carriers and reinsurers who are seeking to move away from traditional, static data sources. By providing a "verified" view of risk, ZestyAI aims to replace the self-reported information that has historically dominated the industry. The company’s models are designed to be transparent and regulatory-compliant, a critical factor for adoption in the highly regulated US insurance landscape.

What FF News has reported before

FF News has tracked ZestyAI’s rapid expansion and regulatory success. We recently reported that the company has achieved 500 regulatory approvals for its AI property risk models across 44 states, a significant hurdle for any AI-driven pricing tool. In the field, Risk Theory tapped ZestyAI to underwrite high-value California homes specifically to manage wildfire risks. We also covered how the Georgia Farm Bureau is using ZestyAI’s agentic AI to transform regulatory research. This growth follows a period of internal scaling, including when ZestyAI appointed a new CFO and SVP as the platform reached its cash flow positive milestone.

What this means from an insurtech perspective

This announcement highlights a critical friction point in the reinsurance value chain: the transition from "good enough" data to granular, AI-verified insights. For years, the industry has relied on policyholder-reported data that decays the moment it is entered. As reinsurers tighten their terms, carriers with legacy core systems and outdated inspection processes are under immense pressure. Those who cannot provide verified exposure data face higher costs of capital, regardless of their actual loss ratios. This creates a clear divide in the market between tech-forward MGAs and carriers who can prove their risk quality, and incumbents who remain tethered to the "uncertainty premium."