Customers · Card

Typology-tagged labelsfor first-party fraud and ATO.

Card programs ride on one ML model. The ceiling on that model is your labels. Roe investigates every dispute and returns typology-tagged labels your DS team can train on.

What you get

Built for the way
your team actually works.

Every for card issuers & processors deployment ships with the same evidence-first reasoning, audit trail, and policy fit as the rest of Roe.

Per-case typology labels

Every case ships with a tag your model needs. 1P vs 3P, friendly fraud, ATO, social-engineered ATO, card-not-present, synthetic. Not a binary fraud / not-fraud flag.

1P vs ATO disambiguation

The two typologies that move loss the most are the hardest to label. Roe reads claim, device, behavior, and merchant context. Calls them apart with cited reasoning.

Training-set on demand

Backfill historical cases for a model retrain, or stream new labels as they close. Your DS team gets a clean, versioned dataset, not a queue export.

Drops into your pipeline

Lands labels in Snowflake, S3, or Kafka next to the features that drove them. No new pane to learn. No new vendor risk review.

30+

Typology tags out of the box

100%

Labels with cited evidence

Versioned

Backfill or stream into your warehouse

See Roe investigate
your real cases.

Book a demo