Train on real outcomes
Findigs trains the model on post-lease performance from 400K+ units, so the weights reflect how renters actually paid.
Findigs scores every application against post-lease performance from 400K+ units, so each tier predicts on-time payment from real outcomes.
A static credit score predicts general credit behavior, not whether a renter actually pays rent on time.
Findigs assigns each application a risk tier trained on post-lease performance, reflecting how comparable renters actually paid.
The model trains on real lease outcomes across 400K+ units, so the tier sharpens with every lease the network sees.
The risk tier feeds decisioning, where it sits on every automatic decision and, soon, the rent guarantee.
A risk tier is Findigs' prediction of how likely a renter is to pay on time. Findigs trains the tier on real post-lease performance across 400K+ units, not on a static bureau score.
Findigs trains the model on post-lease performance from 400K+ units on the network, so the factors and weights reflect on-time payment, delinquency, and lease completion across real cohorts.
A credit score predicts general credit behavior from a bureau file. A Findigs risk tier predicts on-time rent payment from how comparable renters actually paid, with the factor breakdown surfaced on every decision.
The risk tier feeds decisioning, where it sits on every automatic decision and will feed the rent guarantee when that product ships.
Yes. The model sharpens with every lease the network sees, so the tier on the next application reflects the lessons of the last.
Verify, underwrite, and return an automatic yes or no, around the clock.
Same applications, two outputs. Where decisioning pulls ahead of a report.
Playbooks and research for operators who want to fill more units.
Every decision is handled under FCRA, encrypted in transit and at rest, applied as consistent policy with an individualized assessment, and logged to a full audit trail.
See how Findigs decisions every application automatically.