A walkthrough for building a real-time fraud detection system using Tinybird and Retool. Covers the four core components: ingesting transaction streams (via Kafka, Tinybird Events API), processing and analyzing data for fraud risk using SQL pipes, exposing analysis as HTTP APIs, and visualizing trends in dashboards. Includes multiple example SQL queries for detecting suspicious patterns such as high-velocity transactions, unusual locations, large amounts, off-hours activity, declined transactions, and high-risk IPs. Also touches on modern trends like ML-based anomaly detection and streaming platforms like Apache Flink.
Table of contents
Why does fraud detection need to be real-time?How do you build a real-time fraud detection system?An example real-time fraud detection system with Tinybird and RetoolMore example SQL queries for real-time fraud detectionRecent Innovations and Trends in Real-Time Fraud DetectionSummaryFAQs1.4K Impressions