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Stop the synthetic identities before cards ship

You’re the ai / ml engineer. Your team is in the room. Printed Aug 6, 2026.

Stopping a new fraud pattern can require a decision before the cleanest labels exist.

Stronger screening can reduce losses while also excluding legitimate businesses with unusual profiles.

Who you’d be doing this for

“We can’t wait weeks for a card program, but I get why you need to be careful.”

Akinyi Rwigamba · VP of Finance

Leads finance at a legitimate newly incorporated company seeking rapid access to controlled corporate cards.

What is at stake

Losses have risen 2.6-fold from synthetic identities that pass today's onboarding rules, and a blunt fix would reject real founders. You set the thresholds and the review evidence before a cutover date that is already fixed.

Why it isn’t already fixed

Every obvious fix costs something else. That’s the part you’d have to decide.

  • loss containment vs. legitimate access
  • fast cutover vs. verified evidence
  • model precision vs. adversarial adaptation
  • automated decisioning vs. manual review capacity

Why Ramp

At Ramp, this matters because financial access and loss prevention often depend on the same decision path.

Written with these in mind

Fraud ML engineerRisk decisioning engineerResponsible machine-learning specialist

Not your kind of problem? 8 more at Ramp, or browse every organization.

This is the setup. The work is inside.

Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.