Event-level transaction records rather than daily summaries, with identifiers that stay stable across the ledger, switch and acquirer feeds. For machine learning payment fraud detection, six to twelve months of history with confirmed fraud and chargeback labels is a realistic baseline. Device and session signals, identity and authentication outcomes, and relationship data between cards, accounts and beneficiaries add most of the remaining accuracy. Outcome labels returning continuously are what hold that accuracy in place after launch.