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

Absolutely. For underwriting, I’d build a layered risk-data stack rather than rely on a single vendor score. The best sources depend heavily on whether you underwrite commercial property, casualty, personal lines, specialty, or financial lines.

High-value sources for U.S. underwriting

A particularly strong starting point is NOAA + FEMA + NAIC. NOAA explicitly describes its environmental data as being used by insurers and reinsurers for risk evaluation, catastrophe modeling and claims validation.

For flood underwriting, FEMA's OpenFEMA ecosystem is useful, but there is an important caveat: FEMA itself warns that the publicly released NFIP coordinates aren't appropriate as direct inputs to commercial catastrophe models because of geographic truncation. I'd use those data primarily for aggregation, historical context and validation rather than treating them as precise property-level hazard coordinates.

Where I would go beyond public data

For actual underwriting decisions, proprietary data can add substantial value:

• Property intelligence: detailed building characteristics, replacement-cost estimates, roof condition/age, occupancy and neighboring exposures.
• Loss history: prior claims at the location and comparable risks.
• Hazard scores: flood, wildfire, wind, hail, earthquake, severe convective storm and other peril-specific scores.
• Business intelligence: ownership, financial strength, litigation, operational footprint and industry classification.
• Telematics/behavioral data: especially useful for auto, workers' comp and some commercial fleets.
• Industry benchmarks: loss ratios, frequency/severity and exposure-adjusted performance by class and geography.
• CAT models: probabilistic event sets rather than simply historical-event counts. NAIC describes modern CAT models as combining meteorological, geological and geographical information with exposure and policy terms to estimate insured losses.

A useful underwriting architecture

I'd structure the data into five layers:

1. Exposure — What exactly is being insured?
2. Hazard — What can happen at that location?
3. Vulnerability — How badly would the exposure be affected?
4. Financial impact — What does that translate to in insured loss under the actual policy terms?
5. Historical validation — Does the model agree with observed losses?

That distinction matters. A property can have high flood hazard but relatively low expected loss because of elevation, construction, mitigation and coverage terms. Conversely, a moderate hazard can become a severe underwriting risk when the building is vulnerable or the insured value is wrong.

NAIC's description of CAT modeling follows essentially this exposure → damage → financial-loss framework.

If you tell me what line you underwrite (e.g., commercial property, GL, workers' comp, cyber, E&S, marine, etc.) and whether you're looking for free/public sources or paid vendor data, I can build you a much more specific underwriter's data-source matrix, including source, fields available, geographic resolution, update frequency, cost/access, and how I'd use each source in an underwriting workflow.

To give you the most accurate recommendations, the right data sources depend heavily on your specific line of business. Modern underwriting relies on a mix of legacy aggregators and real-time alternative data sources.

The primary, high-utility data providers and platforms are organized below by insurance sector and risk type:

🏢 Property & Casualty (P&C) Risk

• Property & Peril Analytics: Look into Verisk and CoreLogic RiskMeter for hyper-local climate, wildfire, flood, and structural risk scoring.
• Climate & AI Imagery: Platforms like ZestyAI and HazardHub leverage high-resolution satellite imagery and AI to assess roof conditions and vegetation clearance without needing physical inspections.
• IoT & Telematics: Incorporate real-time streaming data from smart home water-leak sensors or commercial fleet telematics to transition from historic risk modeling to dynamic behavioral risk pricing.

🩺 Life, Health, & Disability Risk

• EHR & Digital Health Data: Instead of waiting on manual physician statements, utilize Electronic Health Records (EHR) and automated clinical lab feeds through specialized health data aggregators.
• Prescription & Medical Claims: LexisNexis Risk Solutions and the Medical Information Bureau (MIB) provide instant, historical health risk tracking via prescription histories and prior medical claims.
• Alternative Dental Data: Newer risk models integrate data from dental networks, which helps capture smoker status and baseline health trends for younger demographics who rarely see a primary care physician.

💼 Commercial Lines & Business Risk

• Industry & Hazard Indices: AM Best's Underwriting & Loss Control Resources provides specific cross-references to NAICS/OSHA codes alongside liability hazard indices.
• ESG & Supply Chain Risk: Use MSCI ESG Industry Risk Intensity Scores or Prewave to analyze supply chain vulnerabilities and corporate governance liabilities.
• Firmographic Data: Access business registers and automated firmographic scrapers like Veridion to verify operational details, location data, and revenue metrics.

🔄 Processing Unstructured Data

If you are struggling with the sheer volume of broker emails, PDFs, and handwritten notes, look into Intelligent Document Processing (IDP) tools. Platforms like Indico Data use AI to extract meaningful risk metrics out of unstructured text, cutting down manual evaluation times.

To narrow this down, could you tell me:

• What specific line of insurance do you underwrite (e.g., Commercial Property, Auto, Life, Cyber)?
• What specific gaps or blind spots are you experiencing with your current data?

I can give you a highly targeted list of API providers or software integrations built for that niche.

Absolutely. For underwriting, I’d build a layered data stack rather than rely on a single vendor score. The right mix depends heavily on whether you’re underwriting P&C, commercial, life/health, specialty, or cyber.

High-value data sources

• National Association of Insurance Commissioners (NAIC) — excellent for insurer financials, statutory filings, RBC information, market data, regulatory developments, and benchmarking. Its Financial Data Repository contains 10 years of quarterly/annual data covering more than 4,500 insurers and over 98% of U.S. premiums written.
• Federal Emergency Management Agency (FEMA) National Risk Index — particularly useful for property underwriting. It provides expected annual loss and hazard information for 18 natural hazards, with data down to Census-tract level. The technical methodology is especially useful because it combines hazard frequency with exposure/consequence rather than simply assigning a generic hazard score.
• NOAA — historical and current weather/climate observations, extreme precipitation, hurricanes, drought, wildfire-related climate indicators, temperature trends, etc. Particularly valuable when you're trying to distinguish current exposure from historical loss experience.
• U.S. Census / American Community Survey — demographic, housing, socioeconomic and geographic variables. Useful for exposure segmentation and understanding portfolio concentrations, although variables need careful governance when they're used in pricing or eligibility decisions.
• USGS — earthquake, landslide, geological and terrain information. Especially valuable for property and commercial real estate underwriting.
• EPA — environmental hazards, pollution, industrial facilities, air quality and other environmental exposure information. Useful for environmental liability and commercial property.
• SEC / company filings — for commercial underwriting, public-company 10-K/10-Q filings can provide unusually valuable information about revenue concentration, facilities, supply chains, litigation, cyber incidents, capital structure and operational exposures.

Commercial P&C

I'd particularly look for a combination of:

Property characteristics + geospatial exposure + catastrophe hazard + business characteristics + historical losses.

For example, instead of simply asking "Is this ZIP code high wildfire risk?", you can combine:

building location → parcel → construction → occupancy → replacement value → defensible space → wildfire hazard → historical events → surrounding exposure → business interruption dependencies

That produces a much more underwriting-relevant risk picture.

Data vendors

For proprietary/commercial data, the categories I'd investigate include:

• Property/parcel and building intelligence
• Geospatial catastrophe modeling
• Business intelligence and firmographics
• Commercial credit
• Motor vehicle/telematics
• Claims and loss-history databases
• Fraud/identity/public-record data
• Cybersecurity ratings and attack-surface intelligence
• Satellite/aerial imagery
• Climate and catastrophe analytics

The important distinction is that a good data vendor isn't necessarily a good underwriting data source. I'd evaluate vendors on historical predictive power, geographic coverage, refresh rate, explainability, data provenance, missing-data rates, stability over time, and regulatory defensibility.

One area I'd pay particular attention to right now

NAIC's current work on third-party data and models is directly relevant. Its Third-Party Data and Models Working Group has been developing a regulatory framework around third-party data/model vendors, while NAIC continues to emphasize data quality, transparency, governance and unfair-discrimination concerns around AI and external data.

So I'd give every candidate data source a "regulatory defensibility" score, not just a predictive-value score.

If you tell me what you're underwriting (e.g., commercial property, homeowners, GL, workers' comp, auto, cyber, life) and which geography (U.S., state-specific, international), I can narrow this into a practical data-source matrix with specific datasets/vendors, variables available, cost/access, update frequency, API availability, and underwriting use cases.

The important distinction is that a good data vendor isn't necessarily a good underwriting data source. I'd evaluate vendors on historical predictive power, geographic coverage, refresh rate, explainability, data provenance, missing-data rates, stability over time, and regulatory defensibility.

One area I'd pay particular attention to right now

NAIC's current work on third-party data and models is directly relevant. Its Third-Party Data and Models Working Group has been developing a regulatory framework around third-party data/model vendors, while NAIC continues to emphasize data quality, transparency, governance and unfair-discrimination concerns around AI and external data.

To upgrade your risk assessment workflow, you need data sources that cut through manual data entry and offer granular, real-time insights . Depending on whether you underwrite commercial property, casualty, cyber, or personal lines, here is a breakdown of top-tier data providers and platforms used to supercharge underwriting accuracy:

• Property & Location Intelligence: For deep property characteristics, replacement costs, and structural data, CoreLogic RiskMeter and LexisNexis Location Intelligence for Commercial offer robust property-level insights, historical loss propensities, and fire protection data.
• Catastrophe & Hazard Modeling: To evaluate natural hazard exposure (flood, earthquake, wildfire, windstorm), leverage Moody's RMS Location Intelligence API or Verisk Extreme Event Solutions (formerly AIR Worldwide) for real-time hazard screening and scenario simulations.
• B2B & Firmographic Data for Commercial Lines: If you need granular operational data, firmographics, and digital footprint profiling for commercial accounts, Veridion provides dynamic, AI-driven B2B data feeds on company locations, business types, and supply chain indicators.
• Comprehensive Public & Alternative Datasets: For wide-ranging demographic, macroeconomic, and alternative risk signals, platforms like Techsalerator aggregate multi-channel global data to fill gaps in traditional underwriting files.

To help me narrow down the best fit, could you tell me:

• What line of business are you primarily underwriting (e.g., commercial property, general liability, cyber, personal)?
• Are you looking for API integrations for automated workflows or standalone web platforms for manual review?

Absolutely. For insurance underwriting, I’d build a layered data stack rather than depend on a single commercial score. The strongest sources vary by line of business, but these are particularly useful:

Particularly valuable starting points

1. FEMA National Risk Index — excellent free baseline

FEMA's NRI provides expected annual loss and hazard information for 18 natural hazards, with data available at census-tract and county levels. The underlying methodology goes down to the Census-block level for many calculations.

I'd use it as a screening/benchmark layer, rather than treating the NRI score itself as an underwriting decision variable.

2. Moody's — strong commercial property layer

Moody's currently offers geocoding, exposure data, hazard data, risk scores, loss-cost data and a Location Intelligence API. Its newer property intelligence incorporates characteristics and geospatial imagery, which is particularly useful when you need address-level information that isn't present in public datasets.

3. Verisk — particularly useful for property underwriting

Its underwriting data can include roof age/condition, materials, shape, replacement cost, equipment, tree coverage and other property characteristics. That's useful for converting relatively coarse hazard information into physical vulnerability.

Don't overlook the governance layer

If you're incorporating third-party data into automated or semi-automated underwriting, I'd maintain a data dictionary containing:

• source and vendor
• collection/update frequency
• geographic resolution
• permitted use/licensing
• missing-data rate
• historical backfill availability
• model/score methodology
• known limitations
• validation results against your own claims
• potential proxy/disparate-impact concerns

That's increasingly important because regulators are paying close attention to insurers' use of external data, predictive models and AI. The NAIC specifically highlights data quality, governance, transparency and potential unfair discrimination in its current guidance.

A practical architecture

For P&C/property, I'd consider something like:

Submission → address/geocode → property characteristics → hazard → vulnerability → historical loss → financial/occupancy → internal claims → underwriting model

The key distinction is between hazard and vulnerability. For example, FEMA can tell you a location has substantial flood risk, but property elevation, foundation, construction, roof condition, replacement cost and prior losses can dramatically change the actual insurance risk.

If you tell me your line of business (commercial property, personal lines, workers' comp, auto, general liability, cyber, etc.) and whether you're underwriting U.S. risks, I can narrow this to the 10–15 highest-value data sources, including which are free vs. commercial, geographic granularity, APIs/downloads, and what underwriting variables each can provide.

Upgrading your data stack for risk assessment depends heavily on your line of business (P&C, commercial property, personal lines, or specialty). Modern underwriting relies on shifting from static, manual inspections to real-time property intelligence , geospatial hazard mapping, and predictive analytics.

The industry's top data sources and platforms are organized below by their specific risk-assessment strengths:

• Property Intelligence & COPE Data (Construction, Occupancy, Protection, Exposure)
  • LexisNexis Risk Solutions : Exceptional for comprehensive property, auto, and commercial data records. Their Flyreel tool uses AI and computer vision for interior/exterior property assessments, while Location Intelligence provides robust loss-propensity scores.
  • Nearmap : Provides high-resolution aerial imagery and location data updates. Highly effective for evaluating roof conditions, surrounding tree hazards, and structural changes without ordering a physical field inspection.
  • Archipelago : An AI-native platform that automates the ingestion, cleansing, and gap-analysis of complex commercial property COPE data and geohazards.
• Geospatial, Location, and Catastrophe Risk
  • Verisk LOCATION : The gold standard for granular, address-level risk data in the U.S.—covering fire protection (PPC), building code enforcement (BCEGS), crime scores, and localized weather perils.
  • Moody's RMS : Essential for catastrophe risk modeling, probabilistic hazard simulations, and tracking portfolio-level exposure to natural disasters like floods, earthquakes, and wildfires.
  • Insurity SpatialKey : Embeds geospatial intelligence directly into your daily underwriting workbench to visualize accumulated portfolio exposure and geographic risk overlap.
• Alternative Data & Integration Marketplaces
  • Snowflake Data Marketplace : Rather than relying on a single siloed vendor, many modern carriers plug directly into Snowflake to query third-party alternative datasets—including real-time weather feeds, demographic shifts, and economic indicators—right inside their cloud core.

To help narrow down the best fit, what line of business (e.g., commercial property, workers' comp, personal auto) are you underwriting, and are you looking to integrate via API/workbench or leverage standalone risk-scoring reports?

• Property & Catastrophe Data
  • CoreLogic : Essential for property-level risk, hazard risk scoring (flood, wildfire, wind), and reconstruction cost valuations.
  • Verisk (ISO) : Industry standard for property data, fire protection class ratings, and comprehensive building characteristics.
  • Hazard Hub : Provides easy-to-integrate APIs for property risk scores covering natural and man-made perils (floods, fires, earthquakes, crime).
• Geospatial & Location Intelligence
  • Esri ArcGIS : Excellent for spatial analytics, mapping risk concentration, and visualizing exposures relative to specific hazards.
  • Google Maps Platform / Radar : Useful for precise geocoding, distance-to-coast/hydrant calculations, and neighborhood-level contextual insights.
• Identity, Fraud & Consumer Insights
  • LexisNexis Risk Solutions : Powerful for driving history, claims history ( melalui C.L.U.E.), identity verification, and pre-fill data for personal and commercial lines.
  • A-PLUS (Verisk) : Another premier database for property and auto loss history claims.
• Commercial & Business Intelligence
  • Dun & Bradstreet (D&B) : Crucial for commercial lines underwriting to evaluate business viability, financial stress indicators, ownership structures, and operational history.
  • Moody's Analytics / S&P Global : Provides macro-economic data, industry risk scores, and corporate financial health metrics for large commercial or specialty risks.
• Telematics & Dynamic Data (Auto/Workers' Comp)
  • Arity : Focuses on driving behavior and behavioral risk analytics derived from mobile and connected car data.
  • Cambridge Mobile Telematics (CMT) : Another top-tier telematics provider for measuring real-time driving risk, speeding, and distraction.

If you let me know your line of business (e.g., personal auto, commercial property, cyber, workers' comp) and your integration constraints (API vs. batch processing), I can narrow down which data sources will give you the highest ROI.

Absolutely. For underwriting, I’d think about data in layers rather than looking for one “best” source. The strongest setup combines authoritative public data, proprietary risk data, claims/loss experience, and your own exposure information.

A practical data stack for Canadian P&C underwriting

A particularly useful source is CatIQ: its Canadian catastrophe database contains insurer-supplied loss information going back to 2008, along with meteorological and damage information, while its exposure database provides industry exposure information.

For property underwriting, I'd also prioritize address-level geocoding. OSFI's climate-risk work specifically emphasizes accurate geocoding because physical-hazard layers need to be joined to individual exposures at an appropriate geographic precision.

Data I'd add to an underwriting model

For a property risk, for example:

Location

• Exact geocode
• Elevation/topography
• Distance to coast, river, lake and other water
• Distance to fire station
• Road accessibility
• Wildland–urban interface

Building

• Year built
• Construction type
• Roof age/material
• Number of stories
• Basement
• Square footage
• Occupancy/use
• Heating/electrical/plumbing characteristics
• Recent renovations

Hazard

• Flood
• Wildfire
• Hail
• Wind
• Tornado
• Earthquake
• Freeze
• Severe precipitation
• Historical event frequency/severity
• Forward-looking climate metrics

Exposure / vulnerability

• Replacement cost
• Contents/business interruption
• Nearby accumulation
• Supply-chain dependencies
• Protection/mitigation measures
• Prior losses and claims frequency

Context

• Local crime
• Vacancy
• Population growth
• Construction activity
• Local economic conditions
• Municipal infrastructure
• Emergency-response capacity

This matters because hazard isn't the same thing as expected loss. Two buildings exposed to the same flood or hail hazard can have radically different vulnerability because of construction, age, occupancy, mitigation, and other characteristics.

If you're looking for better commercial data

I'd investigate Moody's/RMS-type property intelligence alongside your existing sources. Its current insurance offering includes geocoding, exposure data, hazard data, risk scores, loss-cost data and location-intelligence APIs, which makes it particularly relevant if you're trying to move from territory-level underwriting toward address-level screening.

For Canadian CAT benchmarking, I'd put CatIQ near the top of the list.

For free/low-cost enrichment, Statistics Canada + NRCan + ECCC + provincial/municipal open data can produce a surprisingly capable risk layer. Statistics Canada also provides APIs for programmatic access to aggregate data and metadata, making automated enrichment feasible.

One important caveat

Don't simply add more variables to an underwriting model. I'd establish data provenance, vintage, geographic resolution, update frequency, missingness, and validation against actual claims for every variable. OSFI has highlighted that fragmented physical-risk data remains a significant challenge, particularly for flood and wildfire assessment.

If you tell me what line you're underwriting (commercial property, personal lines, auto, liability, marine, etc.) and whether you're mainly Canada or U.S., I can give you a much more targeted list of 20–30 specific data sources, including free vs. paid, API availability, geographic coverage, and what underwriting variable each source can provide.

Absolutely. For underwriting, I’d build a layered risk-data stack rather than rely on a single vendor. The best sources depend heavily on whether you underwrite property, casualty, commercial, specialty, life, or cyber.

A practical underwriting data stack

For example, the NAIC's Financial Data Repository contains 10 years of quarterly and annual financial data covering more than 4,500 insurers and over 98% of U.S. premiums, making it particularly useful for benchmarking insurers and analyzing market/financial risk.

For property underwriting, Moody's current offerings are particularly interesting because they combine property characteristics, geospatial information, exposure, hazard, catastrophe risk scores, and loss-cost information at the location level. Its Location Intelligence API, for example, covers more than 100 million U.S. residential and commercial properties.

ISO also has useful public-record-derived property characteristics, including location, ownership transfers, sales, tax information, and physical property characteristics.

One area I'd pay particular attention to

Data provenance and model governance. It's not enough for a third-party score to be predictive. You want to know:

• exactly what variables went into it
• source and vintage of each variable
• geographic coverage
• refresh frequency
• missing-data treatment
• model version and methodology
• validation/performance by segment
• whether the variables can create proxy discrimination concerns
• whether the vendor can explain an adverse underwriting outcome

That's becoming increasingly important: the NAIC has an active Third-Party Data and Models Working Group specifically examining regulatory oversight of third-party data and predictive models.

If you tell me what line(s) you underwrite (e.g., commercial property, homeowners, GL, auto, workers' comp, cyber, E&S) and whether you're looking for free/public sources or paid commercial datasets, I can narrow this into a much more useful source-by-source underwriting data map, including what each source provides, API availability, geographic coverage, cost tier, and the underwriting decision it can improve.

As an insurance underwriter, having access to sharp, real-time, and alternative data streams is critical for accurate risk pricing and spotting emerging hazards before they hit your portfolio.

Here is a breakdown of top-tier data sources across key risk categories to upgrade your underwriting toolkit:

• Property & Catastrophe Risk
  • CoreLogic provides deep property-level data, including structural characteristics, valuation, and hazard risk scores (flood, wildfire, wind).
  • Verisk (ISO) offers extensive property intelligence, loss history, and hazard mitigation metrics (like local fire protection class data).
  • HazardHub (a Guidewire brand) delivers comprehensive property-level hazard risk data covering perils like fire, wind, water, and earth movement via API.
  • Zesty.ai specializes in AI-driven climate and property risk analytics, particularly famous for hyper-local wildfire risk scoring.
• Geospatial & Climate Intelligence
  • Planet Labs delivers high-frequency, global satellite imagery to assess post-disaster damage or monitor property/land-use changes over time.
  • First Street Foundation offers flood, wildfire, and heat risk data at an individual property level, projecting future climate impacts.
  • RMS (Risk Management Solutions) and AIR Worldwide remain the gold standards for catastrophe modeling and portfolio-level risk accumulation.
• Commercial & Financial Stability
  • Dun & Bradstreet (D&B) gives robust corporate linkage, financial stress scores, and payment history for commercial underwriting.
  • Experian Business and Equifax Business supply commercial credit reports, UCC filings, and public record data to gauge financial health.
  • Owler or ZoomInfo can provide alternative business intelligence on company growth trajectories, employee headcount shifts, and executive changes.
• Auto & Mobility Data
  • LexisNexis Risk Solutions is essential for driving history, MVRs (Motor Vehicle Records), and claims history sharing pools.
  • Verisk’s Lightbox/Telematics solutions help ingest connected-car data for usage-based insurance (UBI) and behavioral risk profiling.
• Identity, Fraud & Public Records
  • LexisNexis / Thomson Reuters CLEAR offer massive public records databases for background checks, asset verification, and identity authentication.
  • Sift or Socure provide fraud prevention and digital footprint analytics for digital-first intake processes.

If you let me know what specific line of business you underwrite (e.g., commercial property, personal auto, cyber, or specialty lines) and what current gaps you have (e.g., API integration vs. batch data, or pricing vs. selection), I can narrow these down or suggest implementation approaches.