Elevator Pitch

• Fraud detection in transaction logs is largely about running targeted SQL patterns (often with window functions) to surface suspicious “shapes” like rapid spending, impossible travel, and merchant spikes.

Key Takeaways

• Six practical SQL patterns cover common fraud signals: velocity, impossible travel, amount anomalies, suspicious merchants, off-hours behavior, and window-function “primitives” to chain signals.
• Individual rules have blind spots and false positives; combining multiple signals and scoring transactions is more reliable than any single pattern.
• Investing in reusable windowed features lets analysts iterate on fraud hypotheses as simple SQL filters, speeding detection from “weeks to hours.”

Most Memorable Quotes

• “Fraud detection in transaction data is mostly SQL.”
• “If a card swipes in Chicago and seven minutes later swipes in Los Angeles, one of those swipes is fake.”
• “The moment your analysts can express new fraud hypotheses as SQL filters instead of engineering tickets, your iteration loop drops from weeks to hours.”