I'm an insurance underwriter, and I need better data sources for risk assessment.

If your goal is better underwriting decisions rather than simply more data, I’d build a source stack around the specific risk dimensions you need to quantify.

1. Strong public/authoritative sources

FEMA's current National Risk Index is particularly useful as a screening layer: it covers 18 natural hazards and provides expected annual loss, social vulnerability and community resilience measures. Its underlying methodology works down to Census-block-level exposure before aggregating results.catalog.data.gov

One important caveat: don't treat NRI as a replacement for a property-level catastrophe model. GAO recently found areas where FEMA's NRI could be improved, and the NAIC notes that commercial cat models combine hazard, exposure, engineering and policy terms to produce loss distributions.catalog.data.gov+1

2. Commercial datasets I'd investigate

For commercial P&C underwriting, these are especially relevant:

• LexisNexis Risk Solutions — location intelligence combining weather, aerial imagery, claims and proprietary data; useful for property-level loss propensity.risk.lexisnexis.com
• Moody's — corporate/financial intelligence, ownership, KYC, adverse media, credit/default, physical climate risk and industry exposure.www.moodys.com
• Dun & Bradstreet — commercial firmographics, corporate relationships and business-risk/credit information. Its insurance offering is explicitly aimed at underwriting workflows.www.dnb.com
• TransUnion — commercial P&C data covering businesses, owners, vehicles and properties, including underwriting and portfolio-management applications.www.transunion.com

3. A practical underwriting data architecture

I'd organize the inputs into six layers:

A. Identity

• Legal entity
• Ultimate parent/beneficial owner
• Subsidiaries
• Industry/NAICS
• Locations and operating footprint

B. Exposure

• Building characteristics
• Construction
• Occupancy
• Square footage
• Values
• Vehicles/equipment
• Geographic concentration

C. Hazard

• Flood
• Wind/hail
• Wildfire
• Earthquake
• Severe convective storm
• Heat/cold
• Drought
• Environmental contamination

D. Loss experience

• Prior claims
• Industry loss history
• Loss frequency/severity
• Near-misses
• Catastrophe experience

E. Financial/legal

• Revenue and balance sheet
• Credit/default indicators
• Litigation
• Bankruptcy
• Ownership changes
• Regulatory issues
• Sanctions/KYC

F. Forward-looking risk

• Climate trajectory
• Business deterioration
• Supply-chain concentration
• Physical adaptation/resilience
• Changing land use around the insured
• Emerging regulatory/reputational risks

The big improvement is usually joining these layers at the exposure level rather than buying yet another generic risk score.

4. One thing I'd be particularly careful about

If you're incorporating alternative data or AI-generated risk scores into underwriting, create a data-governance layer alongside the underwriting model. NAIC is actively developing a framework for third-party data and models, and its guidance emphasizes understanding what data enters insurance AI systems and how it is governed.content.naic.org

So for every external variable, I'd record:

source → collection date → geographic resolution → methodology → intended use → validation against losses → known bias/limitations → regulatory constraints.

That makes a model much easier to defend to actuarial, compliance, regulators, and ultimately claims.

If you tell me your line of business (commercial property, GL, workers' comp, cyber, D&O, marine, specialty, etc.) and whether you're underwriting U.S. or international risks, I can build you a much more specific underwriter data-source matrix, including the best free sources, paid vendors, APIs, approximate costs, and which variables each source can actually provide.

If you're underwriting U.S. P&C risks, I’d build your data stack in layers: exposure → hazard → vulnerability → loss experience → economic/industry context → external validation. The strongest sources are often complementary rather than a single “risk score.”

High-value sources

Particularly useful free/public sources

1. FEMA National Risk Index

This is one of the better starting points for geographically granular natural-hazard screening. FEMA's current technical documentation describes exposure data incorporating sources including NOAA, USGS, USDA, the Army Corps of Engineers and FEMA.FEMA

2. NOAA/NCEI

Excellent for historical weather and climate observations. NOAA specifically describes its data as being used by insurers and reinsurers for catastrophe modeling and risk assessment.NCEI+1

3. NAIC

NAIC's Financial Data Repository contains roughly a decade of annual and quarterly insurer financial data, representing more than 98% of U.S. premiums and over 4,500 insurers. That's particularly useful for benchmarking, market analysis and insurer-level risk work.NAIC Content

NAIC also maintains catastrophe-modeling resources and notes the importance of combining hazard, vulnerability, exposure and financial components rather than relying simply on historical losses.NAIC Content+1

4. Census Building Permits

Useful as a proxy for changes in the underlying property stock and local construction activity. Census publishes the data monthly and annually down to county/place levels.Census.gov

5. BLS

For commercial, workers' comp and liability underwriting, BLS provides detailed occupational and industry information. Its fatal-injury data, for example, incorporates death certificates, OSHA records, workers' compensation records, police reports and other sources.Bureau of Labor Statistics

Where paid data becomes worthwhile

For an underwriting operation, I'd consider commercial providers particularly for:

• Property intelligence: parcel/building attributes, replacement cost, roof condition, occupancy and geocoding
• CAT: stochastic hurricane, wildfire, earthquake, severe-convective-storm and flood models
• Claims: industry loss databases and benchmarking
• Commercial intelligence: business ownership, financials, locations and operational characteristics
• Geospatial intelligence: high-resolution imagery, building footprints and automated property attributes
• Fraud/identity: entity resolution and application validation
• Cyber: external attack surface and security-risk telemetry

The important distinction is that a vendor's proprietary score shouldn't necessarily be treated as the underlying risk variable. Where possible, get the underlying features, methodology, vintage, geographic resolution and validation statistics.

A practical underwriting data architecture

For each risk, I'd aim for something resembling:

Risk ID → precise location → exposure characteristics → hazard → vulnerability → historical loss → controls/mitigation → economic context → model output

For a commercial property, for example:

Address → parcel/building → construction/roof/occupancy → flood/wildfire/wind/hail → distance to hazards → historical events → protection/mitigation → local economic conditions → modeled AAL/PML. That gives you considerably more transparency than feeding several opaque third-party scores into an underwriting model.

One important caveat: NAIC notes that insurers' use of granular external/big data raises questions around data provenance, privacy, consumer impact and regulatory oversight. Those issues matter when external variables directly affect eligibility or pricing.NAIC Content

If you tell me which line you're underwriting (commercial property, GL, workers' comp, auto, cyber, marine, etc.) and whether you're looking for free/public data or paid APIs/vendors, I can give you a much more specific source list, including dataset name, variables, geographic granularity, update frequency, API availability, and approximate cost tier.

Upgrading your data stack for risk assessment depends heavily on your line of business (personal vs. commercial, property vs. casualty/auto). Modern underwriting relies on a blend of foundational public records, high-resolution geospatial/peril intelligence, and AI-driven data normalization.[intellectai.com](https://www.intellectai.com/insurance-underwriting-software-guide/)[[2](https://www.youtube.com/watch?v=z6QTpO4OxA8&t=23)](https://www.youtube.com/watch?v=z6QTpO4OxA8&t=23)

The industry-standard and emerging data sources categorized by risk domain are organized below:

1. Property & COPE (Construction, Occupancy, Protection, Exposure)

• Verisk ProMetrix: Essential for commercial property. It provides modeled and field-verified data covering construction class, square footage, year built, stories, and occupancy data for millions of US commercial structures.[intellectai.com](https://www.verisk.com/products/prometrix/commercial-building-reports/)[[2](https://www.youtube.com/watch?v=z6QTpO4OxA8&t=23)](https://www.carriermanagement.com/brand-spotlight/verisk/field-expertise-paired-with-advanced-computer-modeling-empowers-data-driven-commercial-property-underwriting/)
• Moody's CAPE Property Intelligence: Uses aerial imagery and machine learning to deliver loss-predictive property characteristics, roof condition insights, and vulnerability metrics via rapid APIs.[intellectai.com](https://www.moodys.com/web/en/us/solutions/underwriting/property-underwriting.html)
• ISO Mitigation Service (PPC): The gold standard for Public Protection Classification data, evaluating local fire department capabilities, water supply infrastructure, and building code enforcement.[intellectai.com](https://www.isomitigation.com/about-us/)

2. Personal Lines, Identity, & Comprehensive Risk Scoring

• LexisNexis Risk Solutions: Aggregates thousands of public records, property attributes, claims history contributory databases (e.g., C.L.U.E.), and alternative risk/credit scoring models to evaluate individual or asset risk.[intellectai.com](https://risk.lexisnexis.com/insurance/data-analytics-and-modeling/advanced-analytics-for-underwriting)
• CoreLogic: Deep property-level data, including real estate transaction histories, hazard risk (flood, wildfire, wind), and automated valuation models (AVMs) useful for both residential and commercial underwriting.

3. Geospatial, Climate, & Catastrophe Perils

• First Street Foundation: Provides high-resolution, peer-reviewed flood, wildfire, and heat risk data (Property-level risk assessments like the Flood Factor).
• [RMS (Moody's) / AIR Worldwide (Verisk): Traditional catastrophe modeling platforms that offer granular geospatial risk footprints for secondary and primary peril tracking (severe convective storms, earthquake, hurricane).

4. Workflow & Submission Automation (AI Data Extraction)

• IntellectAI: If your bottleneck is unstructured data, tools like this use natural language processing to ingest emails, ACORD forms, loss runs, and third-party inspection reports to normalize data before it hits your risk models.[intellectai.com](https://www.intellectai.com/insurance-underwriting-software-guide/)

To help narrow down the best fit, tell me:

• What line of insurance are you underwriting (e.g., Commercial Property, Workers' Comp, Personal Auto)?
• Are you looking for API-driven straight-through processing or deep-dive engineering data for complex risks?

For insurance underwriting, the most useful data sources usually fall into a few categories: loss history, exposure characteristics, hazard intelligence, behavioral data, financial/firmographic data, and market benchmarks. The right mix depends heavily on your line of business (P&C, life, health, specialty, commercial, reinsurance).

Here are established sources to evaluate:

Examples of specific capabilities:

• LexisNexis Risk Solutions provides underwriting analytics using public records, proprietary datasets, geospatial intelligence, and driver behavior data.risk.lexisnexis.com
• Moody's offers insurance-focused data including geocoding, hazard data, exposure data, risk scores, and loss-cost information.www.moodys.com
• Verisk provides ISO-related actuarial and underwriting intelligence resources, including loss-cost and line-of-business data tools.core.verisk.com

Public / lower-cost sources worth adding

For building internal models or augmenting vendor data:

• National Oceanic and Atmospheric Administration — weather history, climate hazards
• Federal Emergency Management Agency — disaster declarations and flood-related datasets
• United States Geological Survey — hazards such as earthquakes and geology
• United States Census Bureau — demographics, housing, economic indicators
• SEC filings and company registries — commercial financial and ownership information

A practical underwriting data stack

Many carriers combine:

1. Internal policy + claims history
2. Third-party enrichment
   • property/vehicle/entity attributes
   • prior losses
   • hazard scores
3. External signals
   • weather/climate trends
   • economic indicators
   • industry trends
4. Model layer
   • frequency/severity models
   • machine learning segmentation
   • catastrophe models

A few questions would help narrow this:

• Which line are you underwriting (commercial property, casualty, auto, life, specialty, cyber, etc.)?
• Are you looking for vendor datasets, public/free datasets, or AI/modeling tools?
• What geography do you cover (US only, global, specific states)?

Upgrading your data stack depends heavily on the specific line of business you write (P&C, commercial, life/health, or specialty/cyber). Modern underwriting has shifted from static historical applications to real-time, address-specific, and behavioral datasets.[techsalerator.com](https://www.techsalerator.com/use-cases/insurance-underwriting-and-risk-assessment)

The premier data sources and analytics platforms used across the industry are categorized below by their primary use case:

• Property & Geospatial Risk (P&C / Commercial Property)
  • Verisk ProMetrix : Provides comprehensive commercial property details, building characteristics, fire protection data, and hazard evaluations at the address level.
  • CoreLogic Property Intelligence : Excellent for property-level risk, structural characteristics, replacement cost valuations, and granular natural hazard risk (flood, wildfire, wind, and earthquake).
  • Moody's RMS / AIR Worldwide : The gold standards for catastrophe (CAT) modeling, probabilistic risk assessment for climate, and severe convective storm or earthquake exposures.
• Public Records, Claims Histories & Cross-Industry Data
  • LexisNexis Risk Solutions : Essential for comprehensive public records insights, claims history exchanges (like C.L.U.E.), driving history/MVRs, and predictive scoring models spanning auto, home, and commercial.
  • Verisk RISK:check & LightSpeed : Great for automated small commercial workflows, motor vehicle record intelligence (billions of court and crash records), and detecting prior premium leakage or undisclosed losses.[techsalerator.com](https://www.verisk.com/solutions/underwriting-rating/)[[2](https://www.verisk.com/solutions/underwriting-rating)](https://risk.lexisnexis.com/insurance/data-analytics-and-modeling/advanced-analytics-for-underwriting)
• Cyber, Digital Footprint & Emerging Risks
  • BitSight or SecurityScorecard : Critical for cyber underwriting. They provide outside-in cybersecurity ratings, continuous monitoring, and IT hygiene metrics for prospective commercial insureds.
  • [Kinsale / Zywave / specialized aggregators]: Useful for qualitative commercial operations data, industry-specific benchmarking, and exposure profiling.
• Alternative, Global & Macroeconomic Data
  • Techsalerator : Provides diverse global business data, demographic layers, geolocation mapping, and foot-traffic/economic indicators for alternative risk segmentation.[techsalerator.com](https://www.techsalerator.com/use-cases/insurance-underwriting-and-risk-assessment)

To narrow these down, could you tell me:

• What line of business are you primarily underwriting (e.g., small commercial, personal auto, property/CAT-heavy, cyber)?
• Are you looking for point-of-quote automation tools or deep investigative data for complex risks?

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

• 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.Property Claim Services+1

• 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.NAIC Content

• 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.NCEI

• 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.Weather Verification Services

• 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.Snowflake Public 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

One important caveat

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.NAIC Content

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.

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.

Property and Climate Risk Data

• HazardHub (acquired by Guidewire ): Provides comprehensive property-level hazard data covering fire, flood, wind, hail, and man-made risks.
• ZestyAI : Uses artificial intelligence and aerial imagery to assess property-specific climate risks, focusing heavily on wildfire and severe weather vulnerabilities.
• CoreLogic : Delivers deep property intelligence, real estate data, automated valuation models (AVMs), and historical hazard disaster tracking.

Personal and Commercial Auto, Claims, and Loss History

• LexisNexis Risk Solutions : Essential for driving records, comprehensive loss underwriting exchange (C.L.U.E.) reports, and vehicle identification history.
• Motor Vehicle Records (MVR) Direct Providers : Access real-time state DMV violation histories, suspension tracking, and traffic infraction data.
• ISO / Verisk : Standardized commercial property and liability loss cost data, forms, and statistical aggregation.

Life, Health, and Medical Underwriting

• MIB Group (Medical Information Bureau) : Shares coded health and lifestyle history data disclosed on previous applications to prevent material misrepresentation.
• Electronic Health Records (EHR) & Clinical Labs : Vendors like Human API or RGAX streamline clinical data access, reducing reliance on slow traditional paramedical exams.rgare.com+1[rgare.com](https://www.parse.gl/prompts/p/im-an-insurance-underwriter-and-i-need-better-data-sources-for-risk-assessment--a828d51e-7691-422c-a4d2-83c7a7e6061a)

Commercial, Industrial, and ESG Risk Data

• AM Best Underwriting & Loss Control Resources : Offers safety guidelines, liability indices, and industrial classifications cross-referenced with OSHA and NAICS codes.
• MSCI ESG Industry Risk Intensity Scores : Provides quantified environmental, social, and governance risk metrics for corporate and commercial lines.web.ambest.com+1

To narrow down the best platform integration for your desk, tell me:

• What line of insurance do you underwrite (e.g., personal auto, commercial property, life/health)?
• Are you looking to automate workflows or solve a specific nondisclosure/data gap problem?

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.

A practical underwriting data stack

• 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.NAIC Content+1

• 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.FLOIR+1

• 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.Moody's+1

• 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.NAIC Content

What I'd prioritize

If you're trying to improve underwriting accuracy rather than simply acquire more data, I'd build a hierarchy like this:

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.NAIC Content+1

If you're doing P&C underwriting

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.