SQL PATTERNS FOR DETECTING TRANSACTION FRAUD

The article details SQL approaches to flag suspicious transaction behavior without requiring machine learning or complex infrastructure. Key patterns include identifying velocity anomalies—multiple transactions in short timeframes—unusual geographic patterns, and deviations from customer baseline spending. The techniques focus on structured data analysis using standard SQL operations, making them accessible to teams with basic database expertise. Patterns target common fraud vectors like card testing, account takeover, and money laundering indicators. The post has generated substantial discussion on Hacker News, with 40 comments exploring additional fraud signals and implementation challenges. Security practitioners note the value of layering these SQL-based checks with other fraud detection methods. The guide addresses both real-time monitoring and historical analysis approaches, helping teams choose implementations based on their infrastructure and latency requirements.