Inside Stripe Radar: Building Fraud Detection at Scale
Stripe engineer Ryan Drapeau, who works on Payment Intelligence, offers a detailed look into the engineering behind Stripe Radar, the company's fraud detection system. The post walks through the architecture and decision-making processes involved in building a system designed to handle payments at Stripe's enormous scale.
While the source article is structured as an in-depth technical explainer, the key takeaway is the complexity of running real-time machine learning models to distinguish legitimate transactions from fraudulent ones. The development process involves balancing model accuracy with the low-latency requirements of payment processing.
The piece also highlights related engineering work at Stripe, including a companion project called Shepherd, which adapted the open-source Chronon framework to manage ML feature development at scale. This infrastructure work is crucial for feeding timely, accurate data into fraud detection models.
Other related posts cover the creation of Stripe Credits, a programmable and auditable method for handling prepaid and Stripe-issued credits, showing a broader pattern of building custom financial infrastructure from the ground up.
