Turn a Fraud Use Case Into a Supervised Learning Problem

Raw request: use machine learning to stop fraud. Available data: card age, merchant type, transaction amount, device fingerprint, prior disputes, and later-confirmed fraud outcomes. Supervised learning starts with a label and an inference moment, not with a generic desire for AI. The common trap is to define the problem too broadly. "Stop fraud" sounds strategic, but it hides multiple possible decisions: approve or block a transaction, queue it for review, or prioritize downstream investigation. If you skip that distinction, you will either choose the wrong label or include features that exist only after the decision has already passed. 1. Name…

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