🔗 Six SQL patterns I use to catch transaction fraud

Fraud detection largely relies on SQL rather than machine learning or hype-driven technologies. The author shares six effective SQL patterns applicable to various transaction systems, from credit cards to e-commerce. The first pattern addresses velocity, identifying rapid usage of a stolen card, suggesting fraud. The second pattern, impossible travel, flags transactions occurring in far-apart locations within an implausibly short time frame. Another pattern identifies amount anomalies where specific transaction amounts correlate strongly with fraudulent behavior. The article also highlights suspicious merchants exhibiting unusual activity and off-hours spending, which indicates potential unauthorized use. Finally, a framework involving window functions allows for more complex fraud detection by aggregating signals from these patterns. The author notes that utilizing these patterns collectively enhances the ability to uncover fraudulent transactions effectively. This practical SQL-focused approach provides a foundation for building robust fraud detection mechanisms.