Auto Insurance Claims Data
Claim frequency, severity, and payout by vehicle model, zip code, and coverage type. The fundamental dataset for auto insurance pricing.
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Find Me This Data →Overview
What Is Auto Insurance Claims Data?
Auto insurance claims data captures the frequency, severity, and payout patterns across vehicle models, geographic regions (zip codes), and coverage types. This foundational dataset directly drives auto insurance pricing strategies and risk assessment models. Claims data encompasses liability and physical damage metrics, including modern dimensions like electric vehicle repair complexity, telematics-based usage patterns, and advanced materials that inflate repair costs. As of 2025-2026, the auto insurance market is leveraging this data alongside AI systems to accelerate claims processing, refine underwriting precision, and identify high-risk vehicle segments in real time.
Market Data
$2,144
National Average Annual Full-Coverage Premium (2025)
Source: Insurify
6% decline nationally
Premium Price Change (2025)
Source: Insurify
39 states
States with price reductions (2025)
Source: Insurify
1% to 4%
Projected premium increase (2026)
Source: Insurify
38%
Customers reporting poor insurer interactions
Source: Price & Ramey Insurance
Who Uses This Data
What AI models do with it.do with it.
Auto Insurers—Risk Pricing & Underwriting
Major carriers use claims data segmented by vehicle model, zip code, and coverage type to set premiums, identify high-risk segments (e.g., certain EV models), and refine loss ratios across personal and commercial auto lines.
Usage-Based Insurance (UBI) Programs
Insurers leverage real-time telematics and driving behavior data from connected vehicles to tailor individual premiums and expand usage-based insurance adoption.
AI-Powered Claims Processing
Claims severity and frequency data feed AI and LLM systems for rapid first-notice-of-loss triage, hybrid human-AI adjudication, and data-driven claim decisions.
Repair Cost Analysis & EV Risk Assessment
Claims data tracking repair complexity, parts costs, and specialized labor requirements informs pricing for advanced-material vehicles and high-risk EV models (Tesla, Rivian, Polestar, Lucid, Fisker).
What Can You Earn?
What it's worth.worth.
Market Context
Varies
Pricing for auto insurance claims datasets depends on granularity (vehicle model, zip code, coverage type), historical depth (5-year loss ratios, cumulative rate changes), and distribution channel. S&P Global and Insurify publish aggregated market intelligence; enterprise claims datasets command premium licensing fees based on competitive advantage and regulatory compliance requirements.
What Buyers Expect
What makes it valuable.valuable.
Geographic & Vehicle Segmentation
Claims must be classified by zip code, vehicle model, and coverage type (liability, physical damage, commercial vs. personal), supporting state-by-state and vehicle-level pricing relativities.
Severity & Frequency Metrics
Detailed claim frequency, payout amounts, and loss ratios across time periods (minimum 5-year historical coverage) enable trend analysis and forward-looking rate adjustments.
Modern Vehicle Complexity Data
Claims data must capture repair costs for advanced-material vehicles, electric vehicles, and specialized labor requirements to reflect contemporary loss-cost inflation.
Telematics & Usage Context
Integration with connected-vehicle and driving-behavior signals enhances claims correlation with usage-based insurance models and underwriting precision.
Regulatory & Audit Standards
Data must support state-level loss ratio comparisons, rate-change transparency, and compliance with explainable AI and data privacy regulations for algorithmic fairness.
Companies Active Here
Who's buying.buying.
Publishes comprehensive U.S. Auto Insurance Market Reports analyzing claim outcomes, loss ratios by state and coverage type, and insurability trends across top 20 personal and commercial auto carriers.
Use claims data to underwrite policies, set rates by vehicle model and region, and optimize loss ratios; some have shifted data strategies toward AI-powered claims processing and telematics integration.
Monitors claims severity for EV and advanced-material vehicles; classifies high-risk models (Tesla, Rivian, Polestar, Lucid, Fisker) based on repair complexity and loss-cost inflation.
Aggregates and analyzes national and state-level claims trends to forecast premium changes, identify affordability gaps by state, and publish market outlook reports.
FAQ
Common questions.questions.
What drove the 6% premium decline in 2025?
Favorable private passenger auto underwriting outcomes and reduced loss ratios contributed to the decline. S&P Global projected the strongest overall P/C insurance underwriting results in 18 years for 2025, though analysts cautioned that success is temporary due to emerging market dynamics and rising repair costs.
Why are electric vehicles classified as high-risk in claims data?
EV repair complexity, specialized labor requirements, and high parts costs drive elevated claim severity. American National Property & Casualty and other carriers have added Tesla, Rivian, Polestar, Lucid, and Fisker models to their high-risk lists due to higher and more severe claims compared with traditional vehicles.
How is AI changing claims data usage?
AI and large language models are automating first-notice-of-loss triage, enabling hybrid human-AI claims adjudication, and refining risk assessment in real time. Telematics and usage-based insurance programs are driving real-time data capture on driving behavior and vehicle usage to enhance underwriting precision and personalized pricing.
What claims data dimensions matter most to insurers?
Insurers prioritize claims data segmented by vehicle model, zip code, and coverage type (liability vs. physical damage). They also track claim frequency, severity, loss ratios, repair costs (especially for advanced-material and EV repairs), and state-level rate-change relativities to support pricing, underwriting, and regulatory compliance.
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