EP1: Fraud Detection at Scale — Stripe Radar Case Study

Author(s): Euclidean AI

Originally published on Towards AI.

Welcome to the first episode of ‘Holy AI?!’, your go-to newsletter for all things AI and data science. In today’s episode, we’re diving into a crucial topic: Fraud Detection at Scale, and we will go through a case study on Stripe’s fraud detection system — Radar.

Fraud is a major issue for businesses of all sizes. Whether it’s credit card fraud, identity theft, or account takeovers, the cost of fraud can be staggering. It’s not just about financial loss; fraud can damage a company’s reputation and erode customer trust. According to a report, businesses lose billions of dollars annually due to fraudulent activities.

Traditional fraud detection methods rely heavily on rule-based systems. These systems are static and can only catch known fraud patterns. But fraudsters are constantly evolving, and static rules can’t keep up. This is where AI steps in. With machine learning and AI, we can build dynamic systems that adapt and learn from new data, making fraud detection more robust and efficient.”

Source: Stripe Training

Let’s take Stripe Radar as an example. Stripe built an AI-driven system to combat fraud on their payment platform. Here’s how they did it:

Data Collection

Stripe collects vast amounts of transaction data, and it assesses over 1,000 characteristics… Read the full blog for free on Medium.

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Published via Towards AI