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. 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 ›
