ML Fraud Detection Integration

A retail financial services provider cut false positives by 84% and reached 99.1% fraud detection precision by integrating a custom ML model directly into their payments infrastructure.

The Challenge

The firm's existing rule-based fraud detection system was generating a 23% false positive rate, blocking legitimate customer transactions and generating significant complaint volumes under FCA Consumer Duty. Meanwhile, genuine fraud losses were increasing as fraudsters adapted to the static rule set. The compliance team needed a system that could demonstrate explainability for every block decision under FCA requirements, while simultaneously reducing both fraud losses and false positives.

The Trovix Approach

Trovix built a custom ML fraud detection model trained on 18 months of the firm's own transaction history, incorporating 140 behavioural and contextual features. The model was integrated directly into the firm's payments processing pipeline via a low-latency API, returning a fraud probability score and contributing feature explanation for each transaction within 80 milliseconds.

The existing rule-based system was retained as a secondary layer. Trovix implemented a continuous retraining pipeline that updates the model weekly on new transaction data, ensuring the model adapts to evolving fraud patterns without manual intervention.

Technical Architecture

Outcome

Within 12 weeks of production deployment, fraud detection precision reached 99.1% and the false positive rate fell from 23% to 3.7% — an 84% reduction. Legitimate customer transaction blocks fell sharply, reducing complaints and improving NPS. The compliance team now has a complete explainability log for every fraud decision, satisfying FCA audit requirements.