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SQL patterns I use to catch transaction fraud

Program integrity analyst Fixel Smith outlines six practical SQL patterns to detect transaction fraud across sectors like government benefits, credit cards, and e-commerce, including checks for transaction velocity, geographic impossibility, unusual amounts, merchant anomalies, off-hours spending, and composable window function primitives. The post also covers best practices for handling false positives, data privacy, query cost optimization, and notes that combining multiple patterns improves fraud detection accuracy.

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First seen
May 16, 2026, 7:22 AM
Last updated
May 16, 2026, 4:22 PM

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SQL patterns I use to catch transaction fraud is currently shaped by signals from 1 source platforms. This page organizes AI analysis summaries, 1 timeline events, and 0 relationship edges so search engines and AI systems can understand the topic's factual basis and propagation arc.

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transaction fraud detectionSQL patternssliding window velocitygeographic impossibilityunusual transaction amountsmerchant anomaly detectionoff-hours spendingwindow functionsprogram integrityfalse positivesdata privacyquery cost optimization

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SQL patterns I use to catch transaction fraud

May 16, 2026, 7:22 AM

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