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Insurance

Reach policyholders before risk changes

Most insurers react at renewal. By then, the customer is already shopping.

BrightCat lets you act earlier.

Signals of change before policies shift. Detect risk movement in the week it begins.

The risk changes before you see it: unless you see it first.
The problem

Risk changes before you see it

Policy changes are triggered by renewals, claims, and inbound updates. But the biggest trigger is a move: and you only see it after it happens.

By that point: the customer is already shopping, the risk profile has shifted, and you're competing on price.

The shift

Move from reactive underwriting to early detection

A property listing signals a change in residence, a change in risk profile, and a new policy opportunity.

A property listing is the earliest confirmed signal of a change in household risk.

4–12 weeks before
Property listed for sale
The decision to move has been made. Risk is about to change. BrightCat captures this signal immediately.
1–4 weeks before
Policy decisions being made
New home insurance, new auto coverage, new property value: all being evaluated. This is your window.
After the move
Too late
Renewal triggers fire. Competitors have already quoted. You're competing on price, not timing.
How you win

Act before policies are rewritten

Early Policy Acquisition
Quote new policies before customers start shopping. First quote wins the policy.
Renewal Interception
Engage before renewal cycles trigger competitive quotes. Retain the customer before they leave.
Risk Reassessment
Detect changes in property type, value, and exposure early: before claims reveal the gap.
Portfolio Visibility
Track where risk is entering and exiting your book: at the property level.
The difference

Traditional insurance data vs. PreMover data

Traditional approach
Reacts at renewal
Lags behind customer movement
Limited visibility into change
Risk profile updated after the fact
vs
BrightCat PreMovers
Identifies change at listing
Captures intent 4–12 weeks early
Full property lifecycle visibility
Risk shift detected before renewal

Property data fields for insurance workflows

Specific BrightCat fields and products relevant to this vertical. Every field listed below is queryable through Snowflake SQL, MCP natural language, or available in the weekly flat-file delivery.

Signal Product Field / Logic What it tells you
Pre-mover signalListingslisting_status = NEWPolicyholder about to move — retention trigger
Investor-property flagListings + SoldSale-to-rent conversion within 180 daysUndisclosed rental use, higher risk profile
Price change velocityListingspricechangepct, cumpricechgpctCollateral value movement on insured properties
Dual-listing detectionCommercialis_dual_listed = TRUEOwner distress signal on commercial portfolio
Repeat-sale pairsCoreBoth sale prices + dates + property IDAVM inputs for replacement cost estimation
Days on marketListingsdays_on_market, cum_domMarket liquidity proxy for insured properties

Sample query

-- Insurance retention: identify policyholders about to move
SELECT c.policy_id, c.policyholder_name, c.insured_address,
       b.asking_price, b.property_type, b.listing_status,
       b.days_on_market
FROM policyholder_table c
JOIN BRIGHTCAT_LISTINGS.PRODUCT.listings_weekly b
  ON c.postal_code = b.postal_code
  AND c.street_number = b.street_number
WHERE b.listing_status = 'NEW'
  AND b.file_date = (SELECT MAX(file_date)
                     FROM BRIGHTCAT_LISTINGS.PRODUCT.listings_weekly);

Abstract example. Exact column names provided with access provisioning.

P&C underwriting and risk scoring with property data

Canadian P&C insurers use property-level data to sharpen underwriting, validate claims, and monitor portfolio risk between renewal cycles. BrightCat's weekly pipeline provides signals that feed directly into underwriting workflows, catastrophe exposure models, and claims validation processes.

Underwriting and risk assessment

Property listing data reveals risk-relevant changes that traditional loss-history and credit-based models miss. A property entering the market signals occupancy change, which alters vacancy risk and hazard exposure. Price reductions may indicate deferred maintenance or neighbourhood-level distress. Sale-to-rent conversions within 180 days flag undisclosed rental use — a material change in risk profile for a homeowner policy. BrightCat surfaces these signals at the property level, weekly, across all ten Canadian provinces.

Claims validation and fraud detection

When a claim is filed, the property's listing and transaction history provides independent verification. Was the property listed for sale before the loss event? Did the asking price change significantly in the weeks prior? Was there a recent ownership transfer that wasn't disclosed? BrightCat's longitudinal history — covering 5.8M+ properties since 2014 — gives claims teams a second source of truth beyond the policyholder's own declarations.

Catastrophe exposure and portfolio monitoring

For portfolio-level risk, BrightCat data feeds concentration analysis and exposure monitoring. Property type, location, assessed value, and transaction velocity across a geographic area provide early indicators of market stress. Dual-listed commercial properties flag owner distress. Vacancy detection through listing status changes identifies properties at elevated peril for vandalism, water damage, and fire. These signals complement traditional catastrophe models by adding real-time market activity to static hazard maps.

Replacement cost estimation

Accurate replacement cost requires current market data. BrightCat's 194,000+ verified repeat-sale pairs provide the transaction-level inputs that AVMs and replacement cost calculators need. Unlike aggregate indices, BrightCat delivers the raw property-level pairs — both sale prices, both dates, same property — so actuarial teams can build their own models rather than depending on third-party black-box valuations.

Products in action

Built on real market signals

Three BrightCat products power the insurance acquisition and risk lifecycle.

PreMovers
Identify households preparing to move. The earliest signal of a policyholder about to change coverage.
Listings
Track property changes and value signals. Every listing, every price change: weekly.
Sold
Validate final property value and exposure. Confirmed transactions, not estimates.
AI + Delivery

Detect risk changes instantly

Insurance teams use AI to detect risk changes, trigger outreach, and adjust policies as signals appear.

Snowflake Marketplace
Query PreMover data alongside your policyholder base
MCP Connector
AI agents query and act on risk signals directly
Secure Data Share
Enterprise-grade delivery, no pipelines

Updated weekly to reflect current market activity.

The consequence

If you wait for renewal, you lose the policy

Most insurers react late, compete on price, and lose visibility into early change.

The advantage is timing: not pricing.

Most insurers compete after risk has already changed. By then, the customer has already quoted elsewhere.

Common questions

About property data for insurance

How do insurers use property data to reduce churn?
When a policyholder's property is listed for sale, they are about to move: and likely to shop for new coverage. BrightCat's PreMover signals detect this at the point of listing, 4–12 weeks before the move, giving retention teams time to intervene.
How does PreMover data improve insurance policy acquisition?
PreMover data identifies households before they move, allowing insurers to quote new policies before customers begin shopping with competitors.
Can property listing data help with risk reassessment?
Yes. Listing data includes property type, value, and location changes that signal a shift in risk profile before renewal cycles.
How is BrightCat data delivered to insurance teams?
BrightCat data is available via Snowflake Marketplace, Secure Data Share, MCP connector for AI systems, and structured files. Insurance teams can query PreMover data directly alongside their own policyholder data.
Is PreMover data predictive or modeled?
No. PreMovers are based on real listings, not predictive models. Every signal corresponds to an actual property that has entered the market.
How often is PreMover data updated?
PreMover data is updated weekly, capturing new listings and status changes across 5.8M+ Canadian properties.
How does BrightCat data support P&C underwriting?
BrightCat provides property-level signals that feed underwriting risk assessment: occupancy changes via listing status, vacancy detection through market withdrawal patterns, sale-to-rent conversions that flag undisclosed rental use, and price change velocity that indicates collateral value movement. These signals supplement traditional loss-history and credit-based underwriting models with real-time market activity data.
Can BrightCat data be used for claims validation?
Yes. The longitudinal property history — covering 5.8M+ properties since 2014 — provides independent verification during claims investigation. Claims teams can check whether a property was listed for sale before a loss event, whether ownership recently changed, or whether the asking price shifted significantly in the weeks prior to a claim.
Does BrightCat provide data for catastrophe exposure modelling?
BrightCat provides the property-level market activity layer that complements static hazard maps: transaction velocity by geography, vacancy and occupancy change detection, dual-listed commercial distress signals, and concentration analysis inputs. These feed portfolio-level catastrophe exposure models alongside traditional peril data.
How does BrightCat help with replacement cost estimation?
BrightCat delivers 194,000+ verified repeat-sale pairs — the same property sold twice with both prices and dates confirmed. These pairs are the raw inputs actuarial teams need for AVM calibration and replacement cost calculators, without depending on third-party aggregate indices.

Still have questions? Talk to our team.

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