Lucas and Luna dive into how quantum machine learning is being used to detect complex financial fraud that classical systems miss. They focus on a real 2025 pilot by JPMorgan Chase using a 127-qubit IBM quantum processor to analyze transaction patterns across 50 million accounts, flagging synthetic identity fraud rings with 30% fewer false positives than traditional ML models. The episode explores the specific quantum advantage in detecting non-linear correlations across sparse datasets, the current hybrid classical-quantum workflow, and why fraud detection might be one of the first profitable quantum applications. No hype — just the numbers and the engineering reality.

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