Addo AI Slashes Fraud False Positives by 25% for Top Financial Services Firm

Financial services firms lose billions annually to fraud, with rules-based systems generating overwhelming false positives that bog down teams. Addo AI tackled this for a leading payments provider, delivering a machine learning framework that cut false alerts by 25% while ensuring MAS compliance.

The Fraud Detection Challenge

Payment processors face mounting pressure from sophisticated fraud in real-time transactions. Traditional rules-based systems flag legitimate activity as suspicious, creating alert fatigue and compliance risks under Monetary Authority of Singapore (MAS) guidelines. The client needed automation that distinguished true anomalies without disrupting operations.

Addo AI’s Precision Framework

We built a hybrid system combining domain-specific rules with advanced machine learning:

• User Behavior Profiling: Analyzed historical transaction data to establish baselines for each user.
• Deep Convolutional Auto-Encoders: Detected subtle anomalies by learning normal patterns and flagging deviations.
• Real-Time Alerts: Integrated profiles with ML models for instant notifications on unusual activity.

Hosted on Azure infrastructure, the solution used Python Django for scalable backend processing and Azure Blob Storage alongside VMs for data handling.