Fraud prevention is a latency game. A model that’s 99% accurate but answers in two seconds is useless at the point of payment — the transaction has already gone through. Real-time fraud scoring has to return a decision in tens of milliseconds, every time.
Latency comes from distance
The slowest part of most scoring architectures isn’t the model; it’s the round trip. If a payment has to leave your systems, cross the internet to a vendor cloud, get scored and come back, you’ve spent your entire latency budget on network hops.
Near-data scoring removes the round trip. The model is served in-memory inside your data plane, next to the transaction stream. There’s no external call in the hot path — decisions come back in sub-100ms.
Fresh models, no data movement
Fraud patterns shift weekly. Models need frequent retraining, which normally means moving more data more often. Because training also runs in the data plane, models stay current on fresh transactions that never leave. The data-science agent handles the retraining loop; the weights stay home.
Explainability for investigators
A blocked payment triggers a customer call and, sometimes, a chargeback dispute. SHAP explanations tell investigators why a transaction scored high, turning a black-box block into a defensible decision — the same explainability that underpins credit scoring.
The architecture in one line
Score where the data lives, retrain where the data lives, and keep the model in memory. You get accuracy and speed without turning your payment stream into an export.
See the fraud solution or read about account-takeover detection.