Griffin, an Anti-fraud Risk Rule Engine Making Billions of Predictions Daily

This post details the evolution of Grab's fraud detection system from a manual, code-based rule implementation to an in-house rule engine called Griffin. Griffin allows data scientists and analysts to self-service rule creation and deployment via a web portal, significantly improving efficiency and accuracy. The system handles billions of predictions daily with low latency, supporting complex, dynamic datasets and offering features like scenarios, actions, segments, and dynamic values. The architecture separates data orchestration from rule-based prediction, and Python was chosen for its expressive power, with optimizations to ensure low latency.