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Bringing AI in at Affirm

Trace account errors while engineers still verify every cause

You’re the software engineer. Your team is in the room. Printed Oct 15, 2026.

Faster investigation can conflict with the proof required before an account outcome changes.

The investigation workflow must shorten time spent assembling evidence while preserving an engineer's judgment over causes and fixes.

Who you’d be doing this for

“I checked the app twice and still had to wonder whether my refund changed the payment I owe.”

Takumi Lim · Affirm Card holder

A budget-conscious shopper affected when a settled transaction leaves their balance, refund, or repayment status unclear.

What is at stake

Cardholders are waiting longer when settled transactions produce account outcomes engineers must reconstruct from scattered evidence. You have to weigh investigation speed against the evidence an engineer needs before changing a customer record.

Why it isn’t already fixed

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

  • faster triage vs verified causes
  • broad evidence access vs customer-data boundaries
  • engineer judgment vs ranked AI output
  • repeatable handover vs bespoke pilot design

Why Affirm

Affirm Card depends on accurate post-transaction records because every card swipe can create a distinct loan and repayment outcome.

Written with these in mind

forward deployed engineerAI systems engineertechnical solutions architect

Not your kind of problem? 65 more at Affirm, 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.