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Neuro Jump

Real-Time Fraud Detection System for Financial Transactions

AI / ML

Real-Time Fraud Detection with Kafka and MLOps

We designed and implemented a real-time fraud detection system capable of processing over 10,000 transactions per minute with 99.2% accuracy and less than 0.1% false positives, leveraging gradient boosting and deep learning models. Our solution included behavioral feature engineering, real-time inference via Apache Kafka, and a robust MLOps pipeline supporting automated retraining, A/B testing, and performance monitoring—ensuring continuous improvement and reliability at scale.

Conclusion

This project showcases our expertise in building high-performance, AI-driven fraud detection systems that operate reliably at scale. By integrating advanced modeling, real-time inference, and a full MLOps pipeline, we delivered a solution that not only detects fraud with high accuracy but continuously evolves to meet emerging threats.