Insurance
Claims data, actuarial tables, risk models, underwriting decisions, and fraud indicators — insurance data is critical for AI companies building automated underwriting, claims processing, and risk assessment models.
Market Snapshot
$540M market by 2027
Market Size: $540M
CAGR: 18.2%
$540M market by 2027 in annual AI data licensing value, growing at 18.2% annually.
Key Metrics
AI in Insurance Market
$10.36B
2025 market size, projected to reach $154.39B by 2034 at a 35.7% CAGR (Fortune Business Insights). One of the fastest-growing AI verticals.
Claims Automation Savings
$6.5B/yr
Annual savings from AI-powered claims processing automation. Reduces turnaround time by up to 70% while improving accuracy.
Insurance Analytics Market
$15.75B
2025 global insurance analytics market, projected to reach $47.97B by 2033 at 15.0% CAGR (GlobeNewsWire).
Processing Speed Improvement
45%
Increase in claims processing speed achieved by Allstate-Microsoft Azure AI partnership in 2025 using cloud-native ML tools.
Fraud Detection Rate
75%
Improvement in fraud detection rates using AI/ML models versus rules-based systems. Insurance fraud costs the industry $80B+ annually in the US alone.
Underwriting Automation
60%
Percentage of straightforward underwriting decisions that AI can now automate, reducing policy issuance time from days to minutes.
The Insurance Data Opportunity
The Insurancedata opportunity.
Insurance is an industry built entirely on data-driven risk assessment, making it one of the most natural verticals for AI transformation. Every policy, claim, inspection, and actuarial calculation generates structured data that AI companies need for underwriting automation, claims processing, fraud detection, and catastrophe modeling.
The global AI in insurance market was valued at $10.36 billion in 2025 and is projected to reach $154.39 billion by 2034, growing at a staggering 35.7% CAGR. Claims processing alone accounts for the largest segment, with AI-powered automation reducing processing time by up to 70% and saving insurers an estimated $6.5 billion annually.
Insurance data is uniquely valuable because it captures risk outcomes over time. A decade of property claims data, for example, encodes granular information about weather patterns, construction quality, fraud typologies, and repair costs that no synthetic dataset can replicate. This longitudinal depth, combined with the actuarial structure insurance companies impose on their data, creates training datasets of exceptional quality.
The insurance analytics market is expected to triple from $15.75 billion in 2025 to $47.97 billion by 2033, driven by carriers' adoption of AI for real-time underwriting, parametric insurance products, and embedded insurance models that require continuous data feeds.
Data Types
What Insurance
generates.
Every insurance organization generates valuable datasets. These are the formats AI companies are actively purchasing.
Who's Buying
Who buysinsurance data.
Real Deals
Insurancedeals that
closed.closed.
Enterprise Deal
July 2025 partnership to accelerate and automate claims processing across MetLife's global markets. AI platform demonstrated measurable improvements in turnaround times, accuracy, and customer satisfaction.
Multi-year Deal
February 2025 partnership to scale AI-driven claims and fraud analytics using cloud-native tools. Achieved 45% increase in processing speed across Allstate's claims operation.
Acquisition
May 2025 acquisition to strengthen AI-powered customization capabilities for insurers. Combines Zelros's recommendation AI with Earnix's pricing and rating platform.
$2.8B Revenue
Verisk's annual revenue from insurance data analytics, including ISO statistical data, claims databases, and catastrophe modeling. The largest pure-play insurance data business globally.
Market Leader
Launched Home Claims Insights dashboard and Life Smart Path (October 2024) for underwriting analytics. Dominant provider of driver behavior data to auto insurers nationwide.
AI Use Cases
How AI usesinsurance data.
Automated Claims Processing
End-to-end claims automation from FNOL through settlement using NLP on adjuster notes, computer vision on damage photos, and ML on historical claims patterns. Reduces processing from weeks to hours.
Fraud Detection & Investigation
Graph neural networks trained on claims networks to detect organized fraud rings. Anomaly detection models flag suspicious patterns in real-time. Insurance fraud costs $80B+ annually in the US.
Dynamic Underwriting
Real-time risk assessment models combining traditional actuarial data with IoT telemetry, satellite imagery, and behavioral data to price policies at individual risk level.
Catastrophe Modeling
Physics-informed ML models trained on historical weather, claims, and property data to simulate hurricane, wildfire, flood, and earthquake losses for reinsurance pricing.
Telematics & Usage-Based Insurance
Models trained on driving behavior data (acceleration, braking, cornering, time-of-day) from connected vehicles and mobile apps to personalize auto insurance premiums.
Property Damage Assessment
Computer vision models (Tractable, Cape Analytics) trained on millions of damage photos and satellite images to estimate repair costs and assess property condition remotely.
Insurance Data Pricing
Insurance data pricing reflects the actuarial precision and regulatory complexity inherent in the industry. Historical claims datasets with outcomes data are the most valuable because they encode real-world loss experience that actuarial models depend on.
Telematics and IoT data represents a rapidly growing segment, with connected vehicle data and smart home sensor data creating new pricing tiers that did not exist five years ago.
Claims Records
$0.50 - $5.00 / record
Historical claims with cause, payment amount, duration, and outcome. Multi-year longitudinal data at premium pricing. Includes P&C, auto, and health claims.
Actuarial & Loss Data
$50K - $500K / dataset
Industry-aggregate loss ratios, frequency-severity tables, and development triangles. ISO and NCCI datasets command top pricing for certified data.
Telematics / IoT Data
$1 - $10 / device-month
Connected vehicle driving behavior, smart home sensor readings, and wearable health data. Priced per device per month with volume discounts.
Property Risk Data
$0.25 - $2.50 / property
Property characteristics, inspection scores, aerial imagery analysis, and hazard exposure data. Cape Analytics and similar providers lead this segment.
Fraud Investigation Data
$10 - $100 / case
Confirmed fraud cases with investigation notes, evidence, and outcomes. Extremely scarce and valuable for training detection models.
Regulatory & Rate Filing Data
$10K - $100K / state
State-level rate filings, actuarial justifications, and regulatory correspondence. Structured for compliance AI and regulatory intelligence platforms.
Regulatory Framework
Regulatorylandscape.
Insurance data is regulated at both the state and federal level in the US, and by individual member states plus EU directives in Europe. The state-based regulatory system in the US creates a uniquely complex compliance landscape where data sharing rules vary across 50+ jurisdictions.
The NAIC's formation of the Third-Party Data and Models Task Force in 2024 signals increasing regulatory scrutiny of AI training data sourcing in insurance, with new frameworks expected to impact how insurers license and share data with AI vendors.
State Insurance Regulations
US (50 States)
Each state has its own Department of Insurance governing data practices, rate filings, and market conduct. Data sharing agreements must comply with the specific requirements of each state where policies are written.
NAIC Model Laws & Guidelines
United States
National Association of Insurance Commissioners model laws on privacy, data security, and AI governance. Third-Party Data and Models Task Force (formed 2024) developing new AI data frameworks.
GDPR & Solvency II
European Union
GDPR governs personal data in insurance. Solvency II requires data quality standards for risk models. The EU AI Act classifies insurance underwriting and claims AI as high-risk systems.
FCRA (Fair Credit Reporting Act)
United States
Governs use of consumer report data in insurance underwriting. AI models using credit-based insurance scores must comply with adverse action notice requirements.
HIPAA (for Health Insurance)
United States
Health insurance claims and underwriting data subject to HIPAA privacy and security rules. De-identification required for any AI training use of health insurance records.
Unfair Trade Practices Acts
US States
State laws prohibiting unfair discrimination in insurance. AI models must demonstrate they do not use protected characteristics as proxies. Disparate impact testing increasingly required.
Get yourinsurancedata
appraised.
Your insurance data is exactly what AI companies need for model training. We handle the valuation, compliance, and buyer matching.
Get Your Insurance Data Appraised