Cutting Fraud Losses 62% with a Hybrid AI Risk Engine

The client's legacy rules-only fraud engine was both under-catching sophisticated fraud rings and over-flagging legitimate customers, generating an 11% false-positive rate that overwhelmed the manual review team and frustrated genuine users with unnecessary friction.

Hurain Technologies built a hybrid risk engine combining the client's proven rule set with supervised machine learning models trained on historical transaction and fraud-label data, deployed behind a real-time stream-processing layer. Models ran in shadow mode for four weeks before enforcement to validate precision and recall against live traffic.