The underwriting
data waterfall›
A new framework for insurance economics — and a pricing model that ties data cost to bound business, not transaction volume.
Most carriers pay for pre-bind data on every applicant — including the ones who never convert. With Auto shopping rates at a 19-year high and Life application activity setting Q1 records, that waste is compounding. This brief lays out a better order for personal lines and a per-closed-policy alternative to per-transaction pricing.
Get the technical brief
Improve your underwriting data economics.
At a 3% bind rate, 97% of your data spend produces no premium.
The brief walks through how a layered, per-closed-policy waterfall reorganizes that spend against bound business — and what the per-policy economics look like once non-converting applicants stop showing up on the bill.
You’re paying full data costs on quotes that never bind. Here’s the alternative.
Inside: the four-layer pre-bind waterfall, the per-closed-policy pricing model, and the math behind both — built for Auto, Home, and Life underwriting.
Why the cost curve is bending the wrong way
Pre-bind data spend is growing faster than premium across Auto, Home, and Life. We unpack the two structural forces driving it — and why pricing inefficiency makes every year more expensive than the last.
How underwriting data is used across lines
A side-by-side of how Auto, Home, and Life carriers use MVR, criminal, and identity data — and where the same infrastructure gaps show up across all three.
The gaps in today's pre-screen solutions
Three structural limitations in single-vendor pre-screens: coverage that's narrower than carriers realize, records that age before they're used, and signals that no single vendor connects.
A four-layer waterfall — and a pricing model that changes the math
A layered pre-bind framework that concentrates spend where it produces underwriting value, paired with a per-closed-policy alternative to per-transaction billing.
Before the MVR: a four-layer pre-bind waterfall.
Each layer answers one risk question and screens out applicants before the next, more expensive layer runs. By the time a full MVR triggers, it runs only against applicants who passed every cheaper screen above it.
The infrastructure to do this differently exists today.
A substantial portion of the criminal records data personal lines carriers already use comes from Checkr Trust. Carriers can now access that data directly, through a single API, on a per-closed-policy basis — paying for data only when an applicant binds.
The criminal coverage runs about 20% broader than incumbent providers, addressing the one-in-five criminal records that national-only databases miss. Source data refreshes continuously, while reseller data typically lags by 30 days or more. The People Data Graph resolves records that single-vendor pre-screens can't match to a specific applicant — surfacing risk signals that would otherwise sit unused.
See the full framework
The framework, the pricing logic, and the math behind both.
Download the brief › Or book a 20-minute analysis of your current data spend ›
