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Z-WATERTM

The Science of Water Loss

Z-WATER quantifies the hidden dynamics of non-weather water losses, turning everyday failures into measurable, manageable risk.

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Property-level insight into interior water risk

Z-WATER models how plumbing design, local climate, and infrastructure reliability interact to drive non-weather water and freeze losses. By capturing real-world dynamics, like how temperature swings stress pipes or how electrical grid failures amplify claims, Z-WATER delivers a scientifically grounded, property-level view of risk. Carriers gain the clarity to price accurately, reduce losses, and improve profitability across their portfolios.

19X Risk Segmentation

Built and trained on real-world claims to deliver unmatched accuracy in predicting water losses.

Intra-Territory Precision

Distinguishes risk between neighboring properties by modeling how local climate and infrastructure affect individual homes.

Frequency & Severity

Delivers separate risk scores for how likely a loss is and how costly it will be.

>97% U.S. Coverage

Scores nearly every property nationwide with consistent, property-level precision.

Regulatory-Approved & Ready

Reviewed and approved by state regulators for underwriting and rating use; deployable today with full transparency.

Transparent Intelligence

Delivers each property’s risk score and the top factors driving it for full explainability and regulator confidence.

Applied Underwriters Logo
Applied Underwriters Logo

“ZestyAI’s AI-powered risk models offer the kind of granular, verified intelligence that strengthens risk evaluation across a broad spectrum of perils, from climate-related threats to non-weather water."

Brian Voorhees

Chief Operating Officer, Applied Home National Underwriters

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How Z-WATER Works

Z-WATER is built on granular property data, advanced AI, and regulatory-grade validation to model interior water risk with scientific precision. Every stage, from data ingestion to governance, is engineered for accuracy, transparency, and insurance readiness.

Data Ingestion
Feature Engineering
Model Development
Validation & Governance

Granular, Real-World Data at Scale

Z-WATER combines diverse, high-resolution datasets to capture every factor that drives interior water and freeze losses.

Aerial Imagery

Identify roof and property features influencing water risk.

Permits & Assessor Records

Access construction detail, floor plans, and ownership type.

Climate & Infrastructure Data

Measure temperature swings, freeze cycles, and power-grid reliability.

Verified Claims Data

Used to build, train and validate the model

Transforming Raw Data Into Actionable Signals

Proprietary workflows extract and synthesize features that reveal how a property’s systems behave under stress.

Computer Vision Analysis

Detects key physical attributes using aerial imagery.

Derived Plumbing Metrics

Quantifies system design, material, and layout complexity.

Environmental Stress Indicators

Compare actual temperature extremes to local plumbing design standards.

Infrastructure Reliability Factors

Account for grid instability and outage history that amplify freeze events.

Purpose-Built for Interior Water Risk

Z-WATER applies advanced AI to predict both the likelihood and severity of non-weather water losses, using techniques proven in regulatory-approved models.

Dual-Model Architecture

Separate models for frequency and severity scores show both how likely and how costly a loss may be.

Gradient Boosted Machines

Capture complex interactions between property features, climate, and infrastructure.

Transparent Risk Drivers

Reveal the top factors influencing each property’s score for underwriting and regulatory confidence.

Tied to Expected Loss

Calibrated to real claim values for seamless use in pricing and rating models.

Proven, Audited, and Regulatory-Ready

Every model version undergoes rigorous testing and independent review to ensure accuracy, fairness, and compliance.

Out-of-Sample Validation

Tested against unseen claims data for performance stability.

Actuarial Standards Alignment

Fully documented under ASOP 23 and ASOP 38 guidelines.

Regulatory Review

Approved by state regulators for underwriting and rating use.

Continuous Monitoring

Ongoing performance tracking to ensure model stability and compliance over time.

Ready for Filing, Built for Scrutiny

Z-WATER meets the same actuarial and regulatory standards that define ZestyAI’s suite of approved models.

Reviewed and approved for use in underwriting and rating, Z-WATER comes with complete documentation and rate-filing support to streamline carrier adoption and regulatory review.

DOI Reviewed & Approved

Approved for use in underwriting and rating in multiple states; additional reviews in progress.

Actuarial Standards

Developed in accordance with ASOP 12, 23, 25, and 38 to support sound actuarial practices.

Comprehensive Documentation

Full technical documentation, validation results, and model memos available for DOI submission.

Rate Filing Support

Includes relativity analyses and filing templates to accelerate state approvals.

Fairness & Bias Testing

Tested for bias and aligned with emerging DOI guidance on AI and model governance.

See the Hidden Dynamics of Non-Weather Water Risk

Z-WATER predicts non-weather water and freeze losses with 19× predictive lift—helping carriers price accurately, prevent claims, and meet regulatory standards.  Book a demo today.