Service · Insurance data

Insurance Data Scraping Services

Published policy structures and exclusions, without faking quote requests.

Insurance data scraping is the automated collection of publicly published insurance information — product terms and coverage structures, exclusions and limits, published fee schedules, policy wording documents and regulatory filings. Individual quoted premiums are not publicly available, and we do not submit fabricated personal details to obtain them.

Most insurance data vendors quietly generate synthetic quote requests with invented personal details. That produces premiums for people who do not exist, and it is both a terms problem and a data quality problem. We work from what insurers actually publish.

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Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.

✓ Published documents and filings✓ No fabricated quote requests✓ Free pilot sample in 48 hours

insurance_products_2026-08-05.jsonl LIVE FEED

{"insurer":"Example Insurance plc", "insurer_id":"aw-ins-GB-1180", "product_line":"motor", "product_name":"Comprehensive Plus", "tier":"premium", "coverage":{"windscreen":true, "courtesy_car":true,"eu_cover_days":90, "personal_belongings_limit":300}, "excess_standard":350, "excess_young_driver":500, "key_exclusions":["track_use","commercial_hire"], "fees":{"policy_admin":35.00, "mid_term_adjustment":25.00,"cancellation":55.00}, "doc_version":"IPID-2026-07", "source_ref":"pdf:policy-wording-2026-07.pdf#p14", "observed_at":"2026-08-05T06:09Z"} {"insurer_id":"aw-ins-GB-1180", "filing_type":"solvency_public_disclosure", "period":"FY2025","published_at":"2026-04-18"}

2 of 214,800 product-insurer rows · run 2026-08-05T06:00Zpolicy wording extracted 92.8% · schema v4.1

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Written and reviewed by the Actowiz Solutions Data Engineering team Published 5 August 2026 Last updated 5 August 2026 Reading time 11 min

Key facts at a glance

What it is: Managed collection of publicly published insurance product terms, coverage structures and filings
Coverage structure: What is covered, limits, sub-limits, excesses and optional add-ons as published
Exclusions: Key exclusions extracted from policy wording documents, structured as comparable fields
Fees: Policy admin, adjustment, cancellation and instalment fees from published schedules
Document tracking: Policy wording and product information document versions with change detection
Filings: Public regulatory disclosures and solvency publications where published
Explicit exclusion: No fabricated quote requests, no synthetic personal data, no individual premiums
Who it's for: Insurer product teams, brokers, insurtech, reinsurers, regulators and researchers

92.8%policy wording extraction ratewith page references

Structuredexclusions as comparable fieldsnot free text

Versioneddocuments with change detectionclause-level

No synthetic quotesstated boundarynot a coverage gap we hide

Key takeaways

What it is: Managed collection of publicly published insurance product terms, coverage structures and filings
Coverage structure: What is covered, limits, sub-limits, excesses and optional add-ons as published
Exclusions: Key exclusions extracted from policy wording documents, structured as comparable fields
Fees: Policy admin, adjustment, cancellation and instalment fees from published schedules
Document tracking: Policy wording and product information document versions with change detection
Filings: Public regulatory disclosures and solvency publications where published

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is insurance data scraping, and why do we refuse to generate quotes?

Insurance data scraping is the automated collection of publicly published insurance information: product and tier structures, coverage details and limits, excesses, exclusions from policy wording documents, published fee schedules, product information documents and regulatory disclosures.

What it is not, in our practice, is the generation of premiums. That distinction deserves explaining because it is the main way insurance data vendors differ.

The synthetic quote problem

Individual premiums are not published. They are calculated per applicant from personal details submitted to a quote engine. To obtain premiums at scale, a vendor must submit fabricated applicant details — invented ages, addresses, vehicles, claim histories — repeatedly.

It breaches terms. Every quote engine's terms require accurate information. Submitting invented details is a straightforward violation.
It pollutes the insurer's data. Fabricated quote requests enter pricing and conversion analytics as real demand signals, degrading the insurer's own models.
The output is of limited validity anyway. A premium for a fictional 34-year-old in a fictional postcode with a fictional vehicle tells you about the rating engine's response to one synthetic point, not about market pricing.
It creates exposure for the buyer. If your competitive analysis rests on data obtained this way, that becomes a problem attributable to you.

We decline this work. It costs us business and it is the right call.

What published data supports instead

Coverage and exclusion comparison, which is where products genuinely differ and where the analysis is thinner than pricing analysis. Two motor policies at similar premiums can differ materially on windscreen cover, courtesy car provision, EU cover days, personal belongings limits and young driver excess — and those differences are all published.

Fee schedules matter for the same reason: policy admin, mid-term adjustment and cancellation fees materially affect total cost and are published but rarely compared systematically. Document version tracking then reveals when a competitor quietly narrows cover or adds an exclusion.

What we collect

Six categories of insurance data

Coverage and exclusion structuring is the largest use case, because pricing comparison is not legitimately available.

Coverage structures

What is actually covered, in comparable form.

Covered perils and benefits
Limits and sub-limits
Optional add-ons and their scope
Territorial and duration limits
Tier and product line mapping

Exclusions & conditions

Where products genuinely differ.

Key exclusions extracted from wording
Conditions precedent and warranties
Excess structures by scenario
Claims notification requirements
Exclusion change detection between versions

Fees & charges

Total cost beyond the premium.

Policy administration fees
Mid-term adjustment charges
Cancellation and lapse fees
Instalment and credit charges where published
Renewal fee structures

Documents & versions

Published wording, tracked over time.

Policy wording documents
Product information documents
Summary of cover documents
Version identifiers and effective dates
Clause-level change detection

Insurer & market reference

Who is in the market, with what.

Insurer and underwriter identity
Product line coverage by insurer
Distribution channel presence
Public regulatory register status
New product launch detection

Public filings & disclosures

Regulated publications, structured.

Public solvency and financial disclosures
Regulatory register entries
Published complaints data where available
Public conduct and enforcement notices
Filing period and publication dates

Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

Exclusions normalised into comparable codes with original wording retained
Clause-level change detection across policy wording document versions
Published fee schedules extracted from documents, not marketing pages
Page-level source references on every extracted term
Premium benchmarking requirements identified and ruled out before contracting
Source discovery, scoping and a written collection plan
Free pilot on your own sources before any commitment
Full pipeline build, hosting and proxy infrastructure
Schema design, validation and sampled human QA on every run
Ongoing maintenance when source layouts change — our cost, not yours
Delivery to your warehouse, bucket, SFTP or API endpoint
Documented methodology and compliance notes for your legal review

× Not included — stated upfront

Quoted premiums, and any submission of fabricated personal details to quote engines
Synthetic or modelled premium data presented as market pricing
Broker, insurer or agent authenticated systems
Bespoke commercial placements with unpublished manuscript wording
Anything behind a login, paywall or credentialed session
Personal data beyond a documented lawful basis
Licensed third-party datasets we do not hold rights to
Guarantees about fields a source simply does not publish

Schema

Insurance data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 130+ and varies by product line.

Deliverable schema — v4.1 core fields (full dictionary: 130+ fields across product lines)
Field	Type	What it captures	Refresh
`insurer / insurer_id`	string	Insurer as published and a stable identifier for longitudinal joins	Every run
`product_line / product_name / tier`	enum / string	Normalised line, the insurer's product name and tier positioning	Weekly
`coverage`	object	Covered benefits with limits and sub-limits, keyed by normalised benefit name	Weekly
`excess_standard / excess_variants`	decimal / object	Standard excess and scenario-specific excesses such as young driver	Weekly
`key_exclusions`	array	Exclusions extracted from policy wording, normalised into comparable codes	Weekly
`fees`	object	Published fee schedule keyed by fee type, from documents rather than marketing pages	Weekly
`optional_addons`	array	Available add-ons with their scope and any published pricing	Weekly
`doc_version / doc_effective_from`	string / date	Document version identifier and effective date, for change tracking	Weekly
`source_ref`	string	Reference to the document and page each extracted term came from	Every run
`filing_type / period`	enum / string	For regulatory disclosure collection, the filing class and reporting period	Per filing
`change_flags`	array	Detected differences versus the prior document version, at clause level	Weekly

Exclusions are normalised into comparable codes rather than delivered as free text, because comparing exclusions across insurers is the point and free text does not support it. The original wording and its page reference are retained alongside.

Coverage

Sources, lines and markets we collect from

Insurance is intensely national and regulated. Coverage is built market by market and line by line.

Motor insuranceHome and contentsTravel insurancePet insuranceHealth and PMILife and protectionCommercial propertyPublic and professional liabilityCyber insuranceMarine and cargoInsurer product pagesPolicy wording document librariesProduct information documents (IPID-type)Broker product pagesAggregator product listingsPublic regulatory registersSolvency public disclosuresPublic complaints publicationsRegulator enforcement notices

Quoted premiums are excluded from every engagement. Where a client needs premium benchmarking, market study publications from regulators and licensed industry data are the legitimate routes, and we will say so. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market	Why demand concentrates here
United Kingdom & Ireland	Product information documents are standardised and published, which makes structured coverage and exclusion comparison genuinely viable.
Germany, France & Netherlands	Strong disclosure requirements and detailed published policy wording, widely used for proposition benchmarking.
United States	State-level filing regimes make some rate and form filings public, though availability varies considerably by state and line.
United Arab Emirates, Saudi Arabia & India	Fast-growing insurance markets where structured competitive product data barely exists yet.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

Assessing product value and cover narrowing across a market requires harmonised published terms data.

What we deliver

Harmonised coverage, exclusion and fee datasets with source references, suitable for market study work.

Metric that moves

Analysis coverage

Use cases

How insurance data gets used in practice

Four patterns, with the outcome each is judged on.

Coverage and exclusion benchmarking

Coverage benefits, limits, excesses and exclusions are extracted from published documents and normalised into comparable fields, so products can be compared on what they actually cover rather than on marketing summaries.

Outcome: Product positioning assessed on structural cover differences rather than on premium alone.

Cover narrowing detection

Policy wording documents are versioned and compared at clause level, so a competitor adding an exclusion or reducing a limit is detected as an event with the specific clause identified.

Outcome: Competitor cover changes caught when published instead of during a periodic review.

Total cost comparison including fees

Published fee schedules — admin, mid-term adjustment, cancellation, instalment charges — are extracted from documents and attached to product records.

Outcome: Cost comparison reflecting fees that materially affect what customers pay but rarely appear in headline comparison.

Market structure and product launch tracking

Insurer product line presence is tracked over time with new product and tier launches detected, alongside public regulatory register status.

Outcome: Market entry and product innovation visible as it happens rather than through trade press.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Insurer · UK

A competitor narrowed cover and nobody noticed for a quarter

Situation

Competitor monitoring tracked pricing and marketing pages, so a policy wording change that added an exclusion went unnoticed until it appeared in a broker conversation.

What we ran

Policy wording versioning with clause-level change detection, exclusions normalised into comparable codes with original wording and page references retained.

Result

Cover changes across the competitor set began surfacing at publication with the specific clause identified.

Insurtech · EU

Product comparison was built on marketing summaries

Situation

The platform compared products using insurer marketing copy, which described benefits without limits, sub-limits or exclusions.

What we ran

Structured coverage with limits and sub-limits, excess variants by scenario, and exclusions extracted from published wording documents.

Result

Comparison moved onto published policy structure rather than marketing description.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 48-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

Real extraction from your actual sources
Returned inside two business days
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us for this work

Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Build vs buy

Should you build insurance data collection in-house or hire it as a service?

Policy wording extraction with clause-level change detection is specialist work, and it never stops.

In-house build vs self-serve tool vs Actowiz managed service
Consideration	In-house scraping team	Generic proxy / DIY tool	Actowiz managed feed
Time to first usable data	6–12 weeks of engineering before anything is trustworthy	Days, but output needs manual cleanup before use	Free pilot in 48 hours, production in 5–10 business days
Who fixes it when a source changes	Your engineers, at the cost of their roadmap	You do — tools report failures, they don't resolve them	We do, same business day, inside the retainer
Data quality assurance	Whatever your team has time to build	None beyond HTTP success	Schema validation plus sampled human QA on every run
Compliance documentation	Rarely produced, then requested urgently by legal	Not provided; terms risk sits with you	Sources, method and lawful basis documented for review
Accountability	Distributed across a team with other priorities	A support ticket queue	A named engineer and an account owner
True annual cost	Engineer salaries, proxies, hosting, ongoing maintenance	Low licence fee plus significant hidden analyst time	One fixed monthly retainer, quoted after scoping

Why exclusion comparison is more useful than premium comparison anyway

Buyers arrive wanting premium data. It is understandable and it is not the most useful thing available, even setting aside how it would have to be obtained.

What premium data would and would not tell you

A premium is a function of the applicant. Two insurers quoting the same synthetic applicant differently tells you about their rating of that one point in a multi-dimensional space. It does not tell you their appetite across a portfolio, and it certainly does not tell you why they differ.

What coverage and exclusion data tells you instead

Why premiums differ. A cheaper policy excluding track use, capping personal belongings at £300 and applying a higher young driver excess is a different product, not a better price.
Where competitors are retreating. Cover narrowing is a leading indicator of appetite change, and it is published before it shows in pricing.
Structural gaps you can occupy. If every competitor excludes something a segment needs, that is a proposition opportunity visible directly in the documents.
Total cost reality. Fee schedules can move real customer cost meaningfully, and they are published while rarely compared.

Clause-level change detection is what makes this operational. When a competitor republishes wording, we identify the specific clauses that changed rather than reporting that a new version exists. Product teams act on the clause, not on the version number.

For premium benchmarking specifically, regulator market studies and licensed industry data are the legitimate routes, and we point clients there rather than manufacturing a substitute.

Extracting structure from policy wording, which resists structuring by design

Policy wording documents are written by lawyers to be precise and defensible, not to be parsed. Extracting comparable structure from them is the technical core of this service and it is genuinely difficult.

What makes it hard

Exclusions are scattered. Rarely one list. Conditions, definitions, general exclusions and section-specific exclusions all constrain cover, sometimes interacting.
Definitions change meaning. A term defined narrowly in the definitions section can substantially alter an apparently broad benefit twenty pages later.
Limits are conditional. A single benefit can carry different limits by scenario, territory or endorsement.
Language varies while meaning coincides. Two insurers exclude the same thing in entirely different words, which defeats keyword matching.
Endorsements override. Endorsements and schedules modify the base wording, and the base document alone is not the policy.

How we approach it

Exclusions are normalised into comparable codes so cross-insurer comparison works, with the original wording and a page-level source_ref retained alongside every code. Nothing is delivered as a bare classification you cannot audit back to the document.

Our extraction rate is around 92.8%, and where confidence is low the field is flagged rather than populated. In insurance, a wrongly coded exclusion is worse than a gap: it could inform a proposition decision or, worse, advice. We publish the rate rather than a rounded claim precisely because the residual matters.

How it works

How an insurance data engagement goes live in 5 to 10 business days

Lines, markets and insurer set are scoped first, and any premium benchmarking requirement is identified and ruled out before contracting.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We collect publicly published insurance product pages, policy wording and product information documents, and public regulatory disclosures. We do not submit fabricated personal details to quote engines, generate synthetic premium data, or access broker or insurer authenticated systems. Every extracted term carries a document and page reference.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment	What we hold ourselves to
Pilot turnaround	A real sample from your own sources within 48 hours of scoping, at no cost.
Go-live	Production collection running within 5–10 business days of sign-off.
Delivery punctuality	99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response	Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality	Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation	A named engineer and an account owner, not a shared ticket queue.
Change requests	Field additions and source changes handled inside the retainer, not re-quoted.
Exit	Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
QA is part of the service. Schema validation and sampled human review run before delivery, every run.
Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.

Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Synthetic quote: A premium obtained by submitting fabricated applicant details to a quote engine. It breaches quote engine terms, pollutes insurer conversion analytics, and produces premiums for people who do not exist.
Cover narrowing: A reduction in what a policy covers, delivered through revised wording rather than price. It is a leading indicator of appetite change and is published before it appears in pricing behaviour.
Normalised exclusion code: A standard code assigned to an exclusion so it can be compared across insurers who word the same exclusion completely differently. Original wording and page reference are retained alongside.

FAQ

Insurance data scraping: frequently asked questions

What product, pricing and compliance teams ask during evaluation.

No. Premiums are calculated per applicant from personal details submitted to a quote engine, so obtaining them at scale requires submitting fabricated applicant details repeatedly. We decline that work.

It breaches quote engine terms, pollutes the insurer's own conversion analytics with fake demand, produces output of limited validity anyway, and creates exposure that would attach to you. For premium benchmarking, regulator market studies and licensed industry data are the legitimate routes.

Usually by generating synthetic quote requests with invented personal details, at scale. Some are transparent about it; most are not, and describe it as market pricing data.

Worth considering what that means for you: your competitive analysis would rest on data obtained through terms violations, and the premiums would be responses to fictional applicants rather than observations of market pricing. We would rather lose the work than have you find that out later.

What they cover and what they exclude, in comparable structured form: benefits with limits and sub-limits, excess structures by scenario, exclusions normalised into comparable codes, optional add-ons, and published fee schedules.

This is arguably more useful than premium data. Two policies at similar premiums can differ materially on windscreen cover, courtesy car, EU cover days, personal belongings limits and young driver excess — and those differences explain the premium gap rather than just observing it.

Yes, and this is one of the more valuable outputs. Policy wording documents are versioned and compared at clause level, so an added exclusion or reduced limit is flagged with the specific clause identified rather than just reporting that a new version exists.

Cover narrowing is a leading indicator of appetite change and it is published before it shows in pricing behaviour, which makes it useful well ahead of any market commentary.

Around 92.8%, and we publish the figure because the residual matters. Where confidence is low the field is flagged rather than populated.

Extraction is hard by design: exclusions are scattered across conditions, definitions and section-specific lists; definitions narrow apparently broad benefits; and two insurers exclude the same thing in completely different language. We normalise into comparable codes while retaining the original wording and page reference, so every code is auditable back to the document.

Yes, where wording is published — commercial property, liability, cyber, marine and cargo among others. Commercial lines are frequently better documented publicly than personal lines because buyers demand wording upfront.

The limitation is bespoke placement: much commercial cover is negotiated with manuscript wording that is never published. We collect standard published wordings and are explicit that these do not represent bespoke placements.

Yes, where published publicly. Public solvency and financial disclosures, regulatory register entries, published complaints data where a regulator publishes it, and public conduct or enforcement notices.

These are published deliberately for public access, which makes them among the more comfortable sources in this category. We structure them with period identifiers and publication dates so longitudinal comparison works across filing cycles.

Not in the deliverable. We collect published product documents and public filings, neither of which contains personal data. We do not submit personal or fabricated personal details anywhere, and we do not collect broker or agent individual contact details.

This makes insurance one of the cleaner categories from a data protection standpoint, provided the vendor is not generating synthetic quotes. That is the practice that introduces the problem, and it is precisely the practice we decline.

We quote individually. The drivers are insurer count, product line count, market count, and document extraction depth — policy wording extraction with clause-level change detection is the labour-intensive component.

A single line across a competitor set in one market sits at the lighter end. Multi-line, multi-market coverage with full wording extraction and change detection sits considerably higher. One scoping call, a free pilot on your own competitor set within 48 hours, then a fixed monthly quote. Request a quote.

See real insurance product data for your own competitor set

Send us a line and a competitor list. We return structured coverage, exclusions and fees with page references within 48 hours.

Get a free insurance data sample Book a 20-min scoping call

Free pilot, no card, no obligation. We will not include premiums, and we will explain why on the call.

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