Solution:
We created a machine-learning model using historical transaction data to detect anomalous patterns in real time. The model was integrated with the client’s transaction processing system, allowing for continuous monitoring and automated alerts on suspicious activities.
Outcome:
The solution led to a 30% reduction in fraud losses, significantly improving the company’s ability to detect and prevent fraudulent activities while minimizing false positives.