رصد المعاملات التي لا تتفق مع النمط

المشكلة

Payment teams needed to identify unusual transactions in highly imbalanced data without overwhelming investigators with false alerts. Imbalance is what makes this deceptive: a model that flags nothing is over 99% accurate, so headline accuracy is not merely uninformative but actively misleading. The binding constraint is also not detection but investigator capacity - an alert nobody has time to open is indistinguishable from an alert that was never raised.

ما بنيناه

We built an anomaly-detection service over credit-card transaction data using preprocessing, feature scaling, isolation-based models, evaluation against labeled events, and configurable risk thresholds feeding review queues.

ما توقّف عليه

The service kept its own boundary explicit. Feature anonymisation is a privacy control, and it constrains how fully a score can be explained - so the output drives prioritisation and threshold policy, while acting adversely on a customer still requires interpretable customer, merchant, device, and transaction evidence.