I'm an insurance underwriter, and I need better data sources for risk assessment.
Data as of Sep 21, 2026 · Based on 345 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
Data as of Sep 21, 2026 · Based on 345 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
Insurance underwriters can improve risk assessment by using comprehensive analytics platforms like 
Brands AI recommends here
Named in 51% of answers
Ideal for underwriters needing comprehensive predictive models and rules-based scoring. It integrates diverse public and industry datasets, including claims history and geospatial telematics to assess risk traits.
Named in 47% of answers
Best for enterprise-wide risk assessment across P&C and life insurance. Its strength lies in vast, proprietary industry datasets that support accurate underwriting for complex specialty lines.
Named in 30% of answers
Named in 27% of answers
Recommended for property insurance teams needing deep insight into geo-risk and peril exposure. It provides targeted data enrichment to improve the accuracy of property risk-based underwriting decisions.
Source | Citation share |
|---|---|
 Advanced Analytics for Underwritinghttps://risk.lexisnexis.com/insurance/data-analytics-and-modeling/advanced-analytics-for-underwriting | 3% |
 How Insurers Use Data-Driven Risk Assessment for Smarter Underwritinghttps://www.artsyltech.com/blog/how-insurers-can-leverage-data-driven-risk-assessment-for-smarter-underwriting | 2% |
 Enhancing risk assessment in underwriting with Intelligent ...https://indicodata.ai/blog/enhancing-risk-assessment-in-underwriting-with-intelligent-document-processing/ | 2% |
 Unlocking Hyper-Personalization: 20 Alternative Data Sources for ...https://www.fico.com/blogs/unlocking-hyper-personalization-20-alternative-data-sources-insurance | 2% |
 A Needle in a Haystack: How Can Underwriters Effectively Use Alternative Data Sources to Streamline Risk Analysis? | RGAhttps://www.rgare.com/knowledge-center/article/a-needle-in-a-haystack-how-can-underwriters-effectively-use-alternative-data-sources-to-streamline-risk-analysis | 2% |
 A Tech Guide to Data Analytics in Insurance Underwriting - Intelliashttps://intellias.com/data-analytics-in-insurance-underwriting/ | 1% |
 Data and analytics key to future of insurance underwriting | McKinseyhttps://www.mckinsey.com/industries/financial-services/our-insights/how-data-and-analytics-are-redefining-excellence-in-p-and-c-underwriting | 1% |
 Data for better underwriting decisionshttps://capeanalytics.com/resources/using-external-data-to-make-better-underwriting-decisions/ | 1% |
 AI, novel data sources move underwriting into the 21st century - Insurance News | InsuranceNewsNethttps://insurancenewsnet.com/innarticle/ai-novel-data-sources-move-underwriting-into-the-21st-century | 1% |
 Leveraging technology in insurance to enhance risk assessment and policyholder risk reduction (EN)https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/12/leveraging-technology-in-insurance-to-enhance-risk-assessment-and-policyholder-risk-reduction_4844de05/2f5c18ac-en.pdf | 1% |
If you’re underwriting U.S. P&C risks, I’d build a **layered data stack** rather than rely on one vendor. The useful sources differ substantially by line and by whether you need exposure, hazard, loss, or financial data. ## A practical underwriting data stack - **Insurance loss & industry experience**
If you’re underwriting U.S. P&C risks, I’d build a layered data stack rather than rely on one vendor. The useful sources differ substantially by line and by whether you need exposure, hazard, loss, or financial data.
Insurance loss & industry experience
Verisk / ISO — statistical plans, loss costs, loss experience, catastrophe history, property characteristics, public-protection information, and underwriting data. Its PCS unit provides U.S. catastrophe-loss information going back decades.
NAIC — annual/quarterly insurer filings, industry/company financials, premiums, exposures and other regulatory data. The NAIC Financial Data Repository covers more than 98% of U.S. premiums written and more than 4,500 insurers.
Property-level hazard
NOAA/NCEI — historical weather, severe-storm events, climate records, and emerging climate-risk datasets. NOAA specifically identifies insurance/reinsurance as a major use case for its weather and climate data.
FEMA — flood zones, disaster declarations, NFIP information and other publicly available catastrophe data.
Verisk/Cotality/CoreLogic-type property datasets — property characteristics, reconstruction information, hazard enrichment, weather verification and location-level risk attributes. Cotality, for example, provides address-specific historical weather verification.
Geospatial & demographic enrichment
U.S. Census/ACS — population, housing, income, building and community characteristics.
USGS — geological, seismic, landslide and other physical-hazard information.
EPA — environmental hazards and facility information.
Local/state assessor and parcel data — ownership, property characteristics, assessed values and sometimes building information.
These can be particularly valuable when geocoding an exposure and constructing a property/neighborhood risk profile. A current insurance-data platform such as Snowflake's insurance datasets illustrates the breadth of potentially useful FEMA, Census, EPA, address and economic data.
Commercial risk
SEC filings — financial condition, litigation, geographic concentration and business information for public companies.
State corporate/property records — entity relationships, ownership and location information.
OSHA/EPA/public regulatory records — useful for certain industrial, environmental and workers' compensation exposures.
Company financial statements and credit data — particularly important for trade credit, D&O, commercial liability and other financially sensitive risks.
Automotive
Vehicle identification/specification data
Motor-vehicle records where legally available
Telematics/usage data
Accident and claims history
Road/weather/traffic exposure
Vehicle safety and recall information
Cyber
External attack-surface/exposure intelligence
Historical breach and incident databases
Industry/company financial and regulatory disclosures
Security-control questionnaires and verified technical assessments
The strongest underwriting datasets aren't necessarily the largest. Data provenance, temporal alignment, geographic resolution, completeness, and permitted use matter enormously. NAIC specifically highlights questions around the sources of big data, consumer privacy, data security, and how external data is incorporated into underwriting and machine-learning models.
For example, a ZIP-code-level loss statistic may be useful for portfolio analysis but inadequate for underwriting an individual property. Conversely, a highly granular property attribute may look predictive while being poorly validated or unstable over time.
If you tell me which line(s) you underwrite (e.g. commercial property, homeowners, GL, workers' comp, auto, cyber, professional liability) and whether you're looking for free/public sources or commercial datasets, I can build you a much more specific source matrix with dataset → variables → geographic resolution → historical depth → API/download availability → approximate cost → underwriting use case.
To upgrade your risk assessment workflow, the right data sources depend heavily on your specific line of business (P&C, commercial, cyber, life/health, etc.). Modern underwriting relies on shifting from static historical questionnaires to real-time, granular, and predictive data feeds. The industry standard data and…
To upgrade your risk assessment workflow, the right data sources depend heavily on your specific line of business (P&C, commercial, cyber, life/health, etc.). Modern underwriting relies on shifting from static historical questionnaires to real-time, granular, and predictive data feeds.
The industry standard data and analytics providers are categorized below by their core risk specialties:
To help narrow down the best integration or platform for your desk, tell me:
**Modern insurance underwriting relies on specialized data aggregators like Verisk and LexisNexis Risk Solutions for comprehensive predictive modeling and public records integration.**…
Modern insurance underwriting relies on specialized data aggregators like Verisk and LexisNexis Risk Solutions for comprehensive predictive modeling and public records integration.
Upgrading your risk assessment toolkit requires targeting specific data domains depending on whether you underwrite property/casualty (P&C), life/health, or commercial lines.
To narrow down the best platform integration for your desk, tell me:
For underwriting, I’d build a **layered data stack** rather than rely on one vendor: - **Regulatory/industry baseline:** NAIC Financial Data Repository for insurer financials, RBC and filings. [content.naic.org](https://content.naic.org/insurance-topics/financial-data-repository?utm_source=chatgpt.com) -…
For underwriting, I’d build a layered data stack rather than rely on one vendor:
Best practice: combine vendor data with your own claims/loss experience, then track data lineage, vintage, geographic resolution, model version and uncertainty separately.
Absolutely. For underwriting, I’d think about data sources in **layers** rather than looking for one “best” dataset. The highest-value combination is usually your internal loss/exposure data + authoritative public hazard data + address/property intelligence + carefully governed third-party behavioral/financial data.…
Absolutely. For underwriting, I’d think about data sources in layers rather than looking for one “best” dataset. The highest-value combination is usually your internal loss/exposure data + authoritative public hazard data + address/property intelligence + carefully governed third-party behavioral/financial data.
Internal insurance data — highest priority
Historical claims, severity/frequency, loss development
Coverage, limits, deductibles, endorsements
Exposure and location history
Inspection findings and claims narratives
Renewal/nonrenewal behavior
Broker/submission characteristics
Your own loss-cost and appetite experience
Government and regulatory data — excellent baseline
NAIC: industry data, regulatory information, predictive-model/AI guidance, and insurance-market datasets. NAIC specifically recognizes external data, predictive models and machine learning as increasingly important underwriting inputs.
State insurance departments: particularly valuable for local market conditions, catastrophe claims and policy-level aggregates. For example, Florida OIR publishes catastrophe claims data and residential market intelligence.
FEMA, NOAA/NWS, USGS, Census, EPA, OSHA, DOT and other federal datasets depending on the line of business.
Local property-tax assessor, parcel, building-permit and code-enforcement records.
Geospatial/property intelligence
Parcel boundaries and building characteristics
Construction year/type, square footage and occupancy
Roof age/material/condition
Distance to coast, rivers, fire stations and hydrants
Wildfire, flood, wind, hail, earthquake and other hazard layers
Historical imagery/change detection
Vegetation, defensible space and surrounding structures
Commercial providers can consolidate many of these into address-level underwriting feeds. For example, Moody's currently offers geocoding, exposure data, hazard data, risk scores, loss-cost data and location-intelligence APIs aimed specifically at insurance underwriting.
Catastrophe/weather data
NOAA historical and forecast weather
FEMA flood information
USGS seismic data
Hurricane/wind footprints
Hail/lightning/freeze/drought
Wildfire perimeters and severity
Climate projections
These become particularly powerful when joined to exact geocoded exposure locations, rather than ZIP-code averages.
Commercial/business risk data
Company financials and credit
Industry classification and business activities
Ownership structure
Litigation/public-record information
Bankruptcy/liens/UCC information where legally appropriate
Supply-chain dependencies
Cyber exposure
Property and operational characteristics
Specialty external data
Telematics for auto
Motor-vehicle records
Credit-based insurance information where permitted
Prescription/medical information for life underwriting
IoT/sensor data for commercial property
Satellite/aerial imagery
Building inspection data
NAIC notes that insurers are already using sources such as credit information, motor-vehicle records, prescription history and Medical Information Bureau data in accelerated life underwriting.
If you're trying to improve underwriting accuracy rather than simply acquire more data, I'd build a hierarchy like this:
| Priority | Data | Typical value |
|---|---|---|
| 1 | Your own claims + exposure history | Very high |
| 2 | Exact geocoded property/exposure data | Very high |
| 3 | Peril-specific catastrophe/hazard data | Very high |
| 4 | Property condition/inspection data | High |
| 5 | Public records/building permits | High |
| 6 | Industry/business characteristics | High for commercial |
| 7 | Telematics/IoT | High for relevant lines |
| 8 | Consumer/financial attributes | Potentially high, but much greater regulatory/fairness risk |
| 9 | Alternative/behavioral data | Highly dependent on demonstrable predictive value |
The important distinction is predictive power versus availability. A very granular dataset isn't necessarily useful if it is unstable, poorly validated, correlated with protected characteristics, or impossible to explain to a regulator.
NAIC's current guidance is particularly relevant here: insurers using external data and AI need to consider data quality, governance, transparency and potential unfair discrimination. NAIC is also developing a framework specifically addressing third-party data and predictive-model vendors.
I'd be especially interested in helping you build an address-level risk data architecture. For example:
Submission → address normalization → geocode → property attributes → hazard layers → historical events → claims/loss history → business/occupancy attributes → risk score → underwriting decision
That can be substantially more useful than simply buying another generic "risk score."
If you tell me your line of business (personal auto, homeowners, commercial property, GL, workers' comp, cyber, marine, etc.) and whether you're underwriting primarily in the U.S. or another market, I can give you a shortlist of specific free/public datasets and commercial vendors, including what each contains, API availability, approximate cost tier, and where I think each is actually useful to an underwriter.
Upgrading your data sources depends heavily on your specific line of business (Property, Casualty, Cyber, or Specialty). To sharpen your risk selection and speed up straight-through processing, `top-tier data providers and platforms are categorized below by specialty`: - Property & Catastrophe Risk: For geocoding,…
Upgrading your data sources depends heavily on your specific line of business (Property, Casualty, Cyber, or Specialty). To sharpen your risk selection and speed up straight-through processing, top-tier data providers and platforms are categorized below by specialty:
To help narrow down the ideal stack, tell me:
Iso
iso.com
Absolutely. For an underwriting workflow, I’d build a **layered risk-data stack** rather than rely on one vendor or one score. ## High-value data sources Risk need | Strong sources | What you get
Absolutely. For an underwriting workflow, I’d build a layered risk-data stack rather than rely on one vendor or one score.
| Risk need | Strong sources | What you get |
|---|---|---|
| Catastrophe / property | content.naic.org, Moody’s RMS | Hazard, vulnerability, exposure, AAL, PML/EP curves, peril-specific risk |
| Weather & climate | ncei.noaa.gov | Storm history, event footprints, hail/wind, climate trends, extreme-weather data |
| Commercial property characteristics | Moody’s, Verisk | Building characteristics, roof condition, occupancy, location intelligence, loss costs |
| Industry loss experience | core.verisk.com | Loss-cost indications, industry trends, LOB scorecards, historical experience |
| Credit / financial strength | Moody’s, major commercial credit bureaus, regulatory filings | Financial condition, credit risk, payment behavior, company stability |
| Consumer lines | Credit-based insurance scores where legally permitted, claims history, motor-vehicle data | Consumer risk segmentation, prior losses, driving/claims characteristics |
| Casualty / liability | Specialty casualty models and litigation/legal datasets | Litigation exposure, emerging liability trends, industry-specific hazards |
| Regulatory / insurer data | content.naic.org | Statutory filings, regulatory information, industry benchmarks and catastrophe research |
I'd prioritize address-level data rather than broad ZIP/county scores. For each location, try to assemble:
This mirrors the basic structure regulators describe for catastrophe modeling: hazard + vulnerability + exposure + financial terms.
For weather specifically, NOAA is worth incorporating directly into your research/data pipeline. Its insurance program provides event footprints, Storm Events Database information, and hail/wind climatologies intended to support insurance risk evaluation.
One approach I'd recommend is creating a risk-data hierarchy:
That last-but-one layer is important: a location can look acceptable by historical losses while having a very different modeled forward-looking CAT exposure. NAIC specifically notes that catastrophe models are designed to look beyond limited historical loss experience using scientific research and simulated plausible events.
For commercial property, Moody's currently offers address-level property intelligence incorporating characteristics, condition intelligence, geospatial context and catastrophe-risk signals. Moody's Verisk likewise publishes industry loss-cost and experience information, although its published statistics have important coverage and preliminary-data caveats.
If you tell me your line of business (commercial property, personal lines, workers' comp, GL, professional liability, marine, cyber, etc.) and whether you're underwriting U.S. risks or international, I can give you a much more targeted list of 20–30 specific data sources, APIs/datasets, approximate cost tier, and what underwriting decision each source improves.
Upgrading your data stack for risk assessment depends heavily on your specific line of business (property & casualty, commercial, auto, or specialty). Modern underwriting relies on blending traditional loss history with real-time geospatial, public record, and predictive behavioral analytics. Here are the…
Upgrading your data stack for risk assessment depends heavily on your specific line of business (property & casualty, commercial, auto, or specialty). Modern underwriting relies on blending traditional loss history with real-time geospatial, public record, and predictive behavioral analytics.
Here are the industry-leading data sources categorized by risk domain:
To help narrow down the best fit, tell me:
Absolutely. For underwriting, I’d think about data sources in **layers** rather than looking for one “best” dataset. The highest-value stack usually combines **exposure data + hazard data + loss experience + financial/operational information + forward-looking signals**. ## A practical underwriting data stack -…
Absolutely. For underwriting, I’d think about data sources in layers rather than looking for one “best” dataset. The highest-value stack usually combines exposure data + hazard data + loss experience + financial/operational information + forward-looking signals.
Property / exposure intelligence
Address-level geocoding
Building age, construction, roof characteristics, occupancy, square footage
Aerial/satellite imagery
Valuation and replacement-cost estimates
Protection characteristics such as fire stations, hydrants, and distance to response resources
Vendors worth evaluating include Moody's and Cotality. Moody's currently offers property characteristics, COPE information, hazard scores and location-level loss costs; Cotality offers commercial reconstruction-cost and property intelligence.
Catastrophe and natural-hazard data
FEMA flood information
NOAA historical weather and storm events
Wildfire history and fire-weather indicators
Hurricane/wind/hail exposure
Earthquake/seismic data
Drought, extreme heat and precipitation trends
NOAA specifically provides insurance-oriented Storm Events data and hail/wind climatologies that can support catastrophe modeling and risk evaluation.
For modeled catastrophe risk, I'd also compare outputs from the major commercial CAT-model providers rather than relying on a single model. NAIC describes CAT models as combining hazard, vulnerability, exposure and financial modules to estimate insured losses.
Public regulatory/insurance data
National Association of Insurance Commissioners (NAIC) is particularly useful for benchmarking.
Its P&C datasets and market-intelligence work can provide insight into market conditions, premiums, losses and insurer behavior. The recent P&C Market Intelligence Data Call covered insurers representing more than 80% of the U.S. property market.
NAIC also publishes cyber-insurance market data and catastrophe-modeling material.
Corporate / financial risk
SEC filings
Financial statements
Bankruptcy/default information
Ownership and subsidiary structures
Industry classification
Adverse media
Litigation/regulatory events
Moody's, for example, offers firmographic, financial, corporate-filings, bankruptcy/default, industry and adverse-media datasets.
Cyber underwriting
External attack/exposure intelligence
Breach history
Vulnerability and security-control signals
Industry-specific cyber loss data
Ransomware/breach trends
Internal claims data
This is an area where I'd be especially careful about data quality: CISA notes that cyber insurance has historically had far less loss history than traditional perils, and identifies commercial sources such as Advisen and NetDiligence.
Casualty / liability
OSHA violations
EPA/environmental records
Litigation and claims data
Workers' compensation loss history
Industry-specific safety data
Product recalls
Regulatory enforcement
Corporate financial health
For emerging casualty risks, proprietary catastrophe/liability datasets and models can be particularly useful; NAIC notes providers including Praedicat, AIR and Willis Towers Watson/Willis Re.
If your goal is better underwriting decisions rather than simply acquiring more data, I'd prioritize:
A particularly interesting approach is to combine your internal claims data with external granular exposure data. Commercial vendors increasingly offer APIs that can feed property characteristics, hazard scores and loss-cost estimates directly into underwriting workflows rather than forcing underwriters to research individual risks manually.
If you tell me what line you underwrite (commercial property, personal lines, GL, workers' comp, cyber, D&O, etc.) and whether you're underwriting small business, middle market, or large accounts, I can give you a much more targeted list of 20–30 specific data sources/vendors, what each contains, approximate cost tier, API availability, and how I'd use each variable in an underwriting model.
To get better data sources for risk assessment, you should move beyond traditional applications and integrate **alternative data, real-time IoT feeds, geospatial intelligence, and AI-driven document processing** . Relying solely on historical claims and manual forms often leaves blind spots that lead to mispriced…
To get better data sources for risk assessment, you should move beyond traditional applications and integrate alternative data, real-time IoT feeds, geospatial intelligence, and AI-driven document processing . Relying solely on historical claims and manual forms often leaves blind spots that lead to mispriced risks.
The most effective modern data sources and platforms are broken down by insurance line and functionality below.
🏢 Property & Casualty (P&C) Risk Data
🩺 Life, Health, & Disability Risk Data
🚗 Auto & Fleet Risk Data
💼 Commercial Lines & Specialty Risk Data
🤖 Automation & Aggregation Tools
To narrow down the best platform or vendor for your desk, tell me:
MSCI
msci.com
