Financial Services
AI-Powered Fraud Detection for a FinTech Scale-Up
Client: UK FinTech Scale-Up
A payments company needed to move from batch-processed fraud rules to real-time ML-powered detection — without disrupting a live transaction pipeline serving high daily volume.
The Challenge
The client had a growing engineering team that had spent months developing an ML fraud model in a Jupyter notebook. The model showed promise in offline testing, but the team lacked production ML experience to deploy it into a live payment pipeline. Meanwhile, rule-based fraud detection was generating excessive false positives, blocking legitimate transactions and creating customer friction.
The Solution
We embedded directly with the engineering team to bridge the gap between prototype and production. The notebook model was rebuilt on production-grade infrastructure with real-time scoring, integrated directly into the payment authorisation flow. We designed a monitoring framework to track model drift and false positive rates, with automated alerting and a human-in-the-loop escalation path for edge cases.
Our Approach
- 1Audited the existing Jupyter notebook model for production readiness and identified critical gaps in feature engineering, latency, and data pipeline reliability
- 2Designed and built a real-time feature store to serve model inputs at transaction time, replacing batch-processed features that were hours stale
- 3Rebuilt the model serving layer on production infrastructure with sub-100ms latency targets, integrated into the payment authorisation flow
- 4Implemented shadow-mode deployment: the ML model ran alongside existing rules for two weeks, allowing direct comparison without risk to live transactions
- 5Built a monitoring dashboard tracking model accuracy, false positive rate, and latency — with automated alerts for drift detection
- 6Trained the engineering team on model retraining workflows and incident response for ML systems
Outcomes
- Real-time fraud detection deployed to production within 12 weeks of engagement start
- Meaningful reduction in false positive rate compared to the previous rules-based system
- Sub-100ms model inference latency, meeting payment pipeline SLA requirements
- Engineering team fully capable of retraining and monitoring the model independently
- Shadow-mode validation provided confidence for stakeholder sign-off before live deployment
Technologies & Capabilities
Have a similar challenge?
Let's discuss how we can help your team ship AI into production.
Book a Strategy Call