AI-Powered Fraud Detection for a Banking Leader
The Challenge
A global investment bank was losing significant sums to increasingly sophisticated fraud schemes. Its rule-based systems generated too many false positives, frustrating legitimate customers while struggling to keep pace with evolving attack patterns.
Our Solution
We deployed a real-time machine-learning pipeline that scores transactions as they happen, combining graph neural networks for relationship mapping with anomaly detection for behavioral analysis — built to flag emerging fraud without slowing down genuine activity.
Representative Outcomes
“A modern, real-time approach to fraud lets risk teams catch more of what matters while getting out of legitimate customers’ way.”
