Real-Time Risk & Delayed Feedback

The Real-Time Risk Anti-Fraud Architecture & Delayed Feedback Correction framework defends financial platforms against adversarial syndicates, account takeover, and promo abuse under extreme sample skew and label feedback latency; it combines: 1) A 10ms Real-Time Decision Pipeline (device fingerprinting, in-memory AST rule engines, Flink streaming window features, and sub-3ms XGBoost scoring); 2) Delayed Feedback Modeling (DFM): fraud labels arrive with weeks of delay (victims report stolen cards days later); treating unlabeled recent transactions naively as negatives introduces severe survival bias; DFM jointly models conversion probability $P(Y=1)$ alongside an exponential delay distribution $P(E=1|Y=1, T)$, mathematically unbiasing delayed labels.

Payment chargeback prevention, loan delinquency prediction, and adversarial botnet detection.

Naively labeling un-reported transactions as legitimate negatives poisons training data; delayed feedback modeling and 10ms low-latency rule-ML pipelines unmask stealthy fraud rings.

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