FinSight — Real-Time AI Fraud Detection for Fintech
Detecting financial fraud in under 50ms with 97.3% precision using real-time ML.
The Challenge
Legacy rule-based fraud detection missing sophisticated fraud patterns
A UK-based fintech payment processor was suffering $2.1M in annual fraud losses from a legacy rule-based detection system that couldn't adapt to evolving attack patterns. The rule engine produced a 23% false positive rate — blocking legitimate transactions and damaging customer trust. The business needed a real-time ML system that could detect novel fraud patterns, adapt automatically, and make decisions in under 100ms for every transaction.
Our Approach
Ensemble ML fraud model with real-time feature engineering and adaptive retraining
Behavioral Feature Engineering
Built a real-time feature engineering pipeline that computes 120+ behavioral signals per transaction — velocity patterns, device fingerprints, geolocation anomalies, and historical user behavior — all within 10ms.
Ensemble Fraud Detection Model
Trained an ensemble model combining Gradient Boosting, LSTM neural networks, and isolation forests on 18 months of labeled transaction data (4.2M transactions), achieving 97.3% precision and 94.8% recall.
Sub-50ms Inference API
Built a FastAPI inference service with Redis feature caching and model quantization, delivering fraud scores in under 50ms at 99th percentile — compatible with real-time payment authorization flows.
Adaptive Retraining Pipeline
Implemented an MLOps pipeline on AWS SageMaker with automated weekly retraining triggered by performance drift detection, ensuring the model adapts to new fraud patterns without manual intervention.
The Solution
A real-time fraud detection system that learns, adapts, and stops losses continuously
FinSight's ML fraud detection system replaced the legacy rule engine entirely and now evaluates 100% of transactions in real time. The 23% false positive rate dropped to 1.8%, recovering millions in blocked legitimate revenue. Fraud losses fell 78% in the first year. The adaptive retraining pipeline means the system improves every week as new fraud patterns emerge.
Project Details
Tech Stack
Services Provided
The impact was immediate and measurable. Within 90 days of going live, our fraud losses had dropped by over 60% and our false positive rate — which was killing customer trust — fell from 23% to under 2%. Qodeon Labs built something that our in-house team had struggled to even scope properly. Exceptional work.