Ramp
AI / ML EngineerComplexAug 6, 2026

Detect synthetic identities while preserving approvals

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.

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 pulls against what

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

What is at stake

A fixed production cutover must reduce fraud while keeping legitimate businesses moving through onboarding

Why Ramp

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

Written for

Fraud ML engineerRisk decisioning engineerResponsible machine-learning specialist

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.