The Frozen Score
The nightly job scored this customer low risk at 2 a.m., and nothing has rescored them since.
Nightly job scores every customer. This one is low risk.
Where this shows up
Why the feature store gets it wrong
Feature stores compute features ahead of time on a schedule, which keeps serving fast. But each feature is only as recent as its last run, and they all inherit the same delay whether they can afford it or not.
By the time the decision ran, its context was outdated. We call that a context gap.
Score the customer on what they just did.
Tacnode keeps the signals a transfer check reads current at the moment it runs, including recent profile changes, new payees and velocity. You decide per feature how fresh it needs to be: some are computed on demand, others sit in a view on their own refresh schedule. The hold is written back too, so the next check knows about it.
If an engineer asks how
Per-feature freshness control: on-demand against the latest committed data for the features that matter, incrementally refreshed views on a schedule you set for the rest.
Questions to put to your team
- How old is the oldest feature your transfer check reads?
- Which of those features can’t wait for the nightly job?
- What does your system do in the minutes after a credential change?
Is your transfer check telling this lie?
Bring it to a demo, and we’ll trace what it reads and how old that data is by the time it runs.
Book a demo →