Business case
Our client, a payment fintech processing substantial transaction volumes, was experiencing escalating fraud losses threatening its unit economics and merchant relationships. The existing rule-based system was generating excessive false positives while missing sophisticated fraud patterns.
The new detection system dramatically reduced fraud losses while significantly decreasing false positive rates, fundamentally transforming the client’s risk profile. This operational excellence became a key differentiator in merchant acquisition and enabled expansion into higher-risk verticals.
We designed and implemented a machine learning-based fraud detection engine leveraging ensemble methods and real-time scoring. Our approach included feature engineering from transaction patterns, device fingerprinting and behavioural analytics; development of adaptive risk scoring models with continuous learning capabilities; and integration of external data sources including consortium intelligence. We established MLOps infrastructure for model monitoring and retraining, and we measured business impact through comprehensive testing frameworks.