Solutions / Commercial Insurance
The operational truth layer for commercial insurance.
135M operating companies, classified and continuously refreshed. One data contract for underwriting, pricing, claims, and fraud.
Trusted by global intelligence, risk, and procurement teams
The gap
Every function runs on data that goes stale.
Registries tell you what a business once was. Loss history tells you what it once cost. Pick your role and see where the gap shows up in the work.
Application data goes stale the moment it is filed.
Submissions land on registry profiles that are already out of date. Underwriters spend roughly 14 hours a week validating long-tail SMBs, and premium adequacy drifts silently between bind and renewal.
Our strong point
Continuous refresh across 135M operating companies. SMB long tail included.
Blind spots
Questions only Veridion can answer
Each one goes dark on registry or loss-history data alone.
How many logistics insureds opened a new warehouse this quarter that isn't on the schedule?
Which insured manufacturers added lithium-ion or hazardous product lines in the last six months?
What share of my active construction companies are still operating, not dormant or shell?
How many insureds had a material ownership change in the last 180 days nobody flagged?
What share of my portfolio is misclassified because NAICS codes went stale since bind?
If I re-underwrote my SMB book on current data, how much would need a material adjustment?
Can I auto-classify and bind embedded-insurance SMBs at 95%+ without manual review?
What share of my restaurant insureds operate secondary locations missing from our records?
How many workers-comp insureds had headcount shift 30%+ since last renewal?
How many low-risk-coded insureds are doing high-risk activities detectable on the web?
Was the insured operating at claimed capacity on the date of loss?
Can I cross-check this claim against registries, taxonomies, and digital signals in one pass?
What would my SMB classification accuracy be on a full re-match against the live universe?
How many of my unknown-entity flags clear with one global firmographic provider?
Which applicants bound last month have no digital footprint despite claiming active operations?
How many bound policies sit on entities whose ownership shifted since bind, raising a sanctions flag?
In production
From submission to claim.
New business
Long-tail SMBs are thin and miscoded in registries. Underwriters burn hours on manual validation.
Match & Enrich resolves every submission against 135M operating companies, classified across every mainstream taxonomy.
Try asking your book this
Of my new SMB submissions this week, how many are in the long tail my current vendor cannot match?
Proof
Northbridge, top-10 Canadian P&C carrier
Data depth
The data that makes this possible.
- Coverage
- Book-wide change detection. Not annual pulls.
- Entity
- Dormancy, shell, and ownership mismatches surfaced at KYB.
- Classification
- Mid-term drift on lithium-ion, hazardous, heavy machinery lines.
- Ownership
- Sanctions flags within the week of change, not at next renewal.
- Location
- Aggregation exposure, flood and wildfire zone flagging per site.
- ESG
- Climate-tilt pricing. Disclosure-ready provenance on every signal.
Continuous refresh, 135M operating companies
Registry + digital footprint, fused
Product-level granularity
Ultimate parent across registries and the digital footprint
Site-level location graph
Classified to UNEP FI taxonomy
Taxonomy breadth
Every coding system on your rate tables.
Underwriting, rating, and regulatory filing each run on different coding systems. Veridion classifies every operating company against all of them in parallel, with confidence scores per field.
NAICS
North American Industry ClassificationUS regulatory filings, ISO rates
SIC
Standard Industrial ClassificationLegacy US; embedded in carrier systems
ISIC
Intl. Standard Industrial ClassificationUN standard for cross-border books
NACE
Nomenclature of Economic ActivitiesEU baseline, carriers and reinsurers
NCCI
National Council on Compensation InsuranceWorkers-comp class codes
IBC
Insurance Bureau of CanadaCanadian commercial-lines codes
UNSPSC
UN Standard Products & ServicesProduct / procurement taxonomy
Veridion
Proprietary business tagsFiner-grained activity beyond standard schemes
Customer impact
What changes when the data is current.
Customer story
A second featured customer impact story will render here once the customerImpact fetcher is wired.
Integration
Four surfaces, one contract.
Submission pre-fill
Populate the bind form with 50+ classified fields before the underwriter sees it.
Book monitoring
Continuously refreshed slice of your portfolio with change flags on location, revenue, ownership, and product lines.
Claim verification
Pull the historical digital footprint to confirm operating capacity at the date of loss.
ESG and climate underwriting
Live UNEP FI signals with disclosure-ready provenance, priced into the same data contract.
Governance
Auditable by design.
- Robots.txt-compliant sourcing. No personal data. Every attribute is traceable to the source signal that produced it.
- Ownership links support aggregation-exposure and sanctions screening across the global registry + digital footprint.
- Confidence scores and signal provenance are exposed on every field so regulatory data-quality reviews land clean.
FAQ
Commercial insurance, answered.
Next step
See the data before you commit.
Run a data-quality review against your current enrichment layer. Attribute fill-rate, match-rate delta, freshness gap on your actual book of business.
Solutions
Explore other solutions
Each workflow runs on the same living company intelligence. Pick the one closest to your team's problem.