Aim of the Project

To build a production-ready, real-time fraud detection system capable of processing 500K+ daily transactions with high accuracy and low latency. The system provides instant fraud detection, automated monitoring, and comprehensive explainability for regulatory compliance while preventing millions in fraudulent transactions.

Life Cycle of the Project

Designed and implemented a scalable microservices architecture using Apache Kafka for real-time streaming and Spark for distributed processing. Built ensemble machine learning models (Random Forest + XGBoost + LightGBM) achieving 94.2% accuracy with less than 3% false positive rate.

Developed advanced feature engineering pipeline with 50+ behavioral and statistical features including transaction velocity, amount deviation, merchant patterns, and geographical anomalies. Implemented real-time feature store using Redis for sub-100ms inference latency.

Created comprehensive monitoring and alerting system with Grafana dashboards tracking model performance, drift detection, and system health metrics. Integrated SHAP explainability for transparent decision-making and regulatory compliance.

Deployed production-ready infrastructure using Docker and Kubernetes with automated CI/CD pipelines, MLflow model registry, and automated retraining capabilities. Achieved horizontal scalability handling 10K+ requests per second with 99.9% uptime.

Live Application Demo

Experience the real-time fraud detection system in action through our interactive web application. The demo showcases the complete user interface for transaction processing, risk analysis, and fraud detection results with detailed explanations.

Try the Live Demo: Interactive Fraud Detection System

Results from the Project

Successfully deployed a production system processing 500K+ daily transactions with 94.2% precision, 91.8% recall, and 93.0% F1-score. Achieved sub-100ms response times with automated fraud prevention saving $2.1M+ annually. The system maintains less than 3% false positive rate while providing real-time monitoring and explainable AI compliance.

Check out the Detail Project Overview on GitHub Repository

Technologies Used
Core ML Stack: Python 3.8+, Scikit-learn, XGBoost, LightGBM, Random Forest, SHAP
Streaming & Infrastructure: Apache Kafka, Spark Streaming, Redis, PostgreSQL
API & Deployment: FastAPI, Docker, Kubernetes, MLflow, Grafana
DevOps & Monitoring: GitHub Actions, Prometheus, Streamlit, Docker Compose