Challenge
A mid-sized financial services company experienced rising chargeback rates but lacked real-time fraud monitoring. Manual reviews were slow and missed subtle patterns.
Solution
Kairolytics built a scalable fraud detection system using streaming transaction data, feature engineering for behavioral patterns, and a gradient boosting model to flag suspicious transactions instantly.
Implementation Steps
- Architecture Review: Evaluated existing data flows and integrated Kafka for real-time streaming.
- Feature Engineering: Created 50+ behavioral features including frequency, velocity, and anomaly scores.
- Model Training: Tuned a LightGBM model to achieve 92% precision on test data.
- Alert Dashboard: Developed a React-based dashboard for live alerts and case management.
Results
- Identified $4.2M in fraudulent transactions within the first quarter of deployment.
- Reduced false positives by 45%, saving investigation time.
- Enabled rapid response with sub-second alerting, boosting team productivity by 30%.