Impact
After rollout we saw a significant drop in chargebacks and manual reviews on the segments covered by the engine. In practice that meant blocking or flagging hundreds of high-risk orders per month that previously would have gone to manual review or slipped through.
Performance
The requirement was that risk evaluation must not be a bottleneck to checkout, so we targeted under ~200 ms end-to-end per transaction for online scoring (including feature enrichment and external calls). Most requests stay well below that in production.
Architecture & Continuous Growth
Eval Risk is a large-scale digital product that combines REST APIs, Event-Driven Architecture, Microservices, and Real-Time Alerts into a cohesive fraud detection platform. The system is continuously evolving:
- REST APIs handle synchronous risk scoring on critical checkout paths
- Event-Driven backbone allows multiple consumers (rules engine, ML scoring, analytics, notifications) to react asynchronously
- Microservices architecture enables independent scaling and deployment of different components (feature enrichment, model inference, alert processing)
- Real-time alerting system notifies fraud analysts immediately when high-risk patterns are detected
This architecture allows us to add new fraud detection models, integrate external data sources, and evolve business rules without disrupting the core payment flow.