3x Reduction in Compliance False Positives

The Challenge

A commodities trading operation struggled with legacy rule-based surveillance generating excessive false positives. Analysts spent more time clearing noise than investigating genuine risk events, inflating compliance costs.

Our Solution

We deployed deep learning models trained on structured trade data and unstructured alternative signals. The system integrated with existing compliance workflows and provided explainable risk scores for audit readiness.

Measurable Results

• False positive rate reduced by threefold in compliance monitoring.
• Alert triage time improved by 68% for the surveillance team.
• Estimated £12M in risk exposure avoided through earlier pattern detection.
• Regulatory reporting cycles accelerated with automated evidence packaging.