SQL remains the unsung hero of transaction fraud detection, outperforming flashy AI tools for many teams. A senior data engineer outlines six practical patterns using generic transaction tables, focusing on velocity checks, impossible travel detection, and merchant anomaly spotting. Examples include tracking cardholder activity within sliding windows and calculating haversine distances between locations to flag impossible travel scenarios.

The patterns adapt across industries: from government benefit programs to e-commerce. One key example uses Snowflake's QUALIFY clause to monitor five-minute transaction bursts, while another compares merchant activity against historical baselines using rolling averages. Specific thresholds like 600 mph travel speed and three-times-normal merchant card usage spikes help separate signal from noise.

For developers, these queries offer immediate value - no complex ML models required. The author emphasizes practical implementation details, like adjusting time windows for different fraud types and handling false positives through whitelists. The post's technical depth makes it particularly valuable for engineers building payment systems.

Key entities: Snowflake, haversine, SQL. Primary keyword: SQL fraud detection. Secondary keywords: transaction fraud patterns, payment system security, data engineering tools.