Cut investigation effort about 60% on the Amazon Pay latency project
While the org moved Amazon Pay top-up fraud checks to a new engine to cut latency, I reworked the tool that compares old and new fraud variables so engineers on two teams and the ML scientists could find and fix mismatches fast.
- Fraud and risk
- Payments
- AI
~60%
less engineer effort
self-reported
~86% / ~89%
fraud-check latency cut at p90 / p99
team launch, June 2018
What would have happened
Engineers hunt by hand for why the new engine disagreed with the old one, and the migration slows.
The call
Make the tool generic and backward compatible, with filters and sample orders attached to every mismatch.
What I did
Added customer-group and variable filters, sample order IDs for each mismatch and precision control, kept existing jobs running, and sent daily reports to the ML team.
What changed
Investigation effort dropped about 60%, my manager said the changes helped both teams, and the team's launch later cut fraud-check latency about 86% at p90.
Proof
The original documents are held in my private record and can be shared on request.
What people said
“Thanks for improving the tool. I think the changes you have made are generic to help both teams”
“Big customer win!”