AI-Driven Fraud Detection Platform
The Challenge
A Malta-based financial services firm was struggling with manual transaction monitoring processes that could not keep pace with growing transaction volumes. The existing rules-based system generated high false positive rates — consuming analyst capacity on legitimate transactions — while simultaneously failing to detect sophisticated fraud patterns that spanned multiple channels. Regulatory pressure from the FIAU for more robust transaction monitoring added urgency to the problem.
Our Approach
redskios designed a machine learning pipeline for real-time transaction scoring, combining supervised models trained on the firm's historical fraud cases with unsupervised anomaly detection for identifying novel patterns. We worked closely with the compliance team to ensure the platform's risk scoring aligned with their investigation workflow and regulatory reporting obligations.
The Solution
We delivered a .NET-based fraud detection platform running on Azure, with Azure Machine Learning models scoring every transaction in real time via a low-latency API. The platform ingested transaction data from multiple channels through Azure Event Hubs, applied ensemble ML models for risk scoring, and surfaced flagged transactions in an analyst dashboard with full transaction context, network visualisation, and investigation tools. Automated suspicious activity report generation streamlined FIAU submissions.
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