DEVELOPMENT AND IMPLEMENTATION OF A REAL-TIME FRAUD DETECTION SYSTEM USING KAFKA STREAMING AND XGBOOST ALGORITHM

Abstract

In the digital banking era, the frequency and sophistication of fraudulent transactions have increased significantly, posing substantial risks to financial institutions and customers. Traditional fraud detection systems often struggle to provide timely and accurate identification of fraudulent activities, resulting in financial losses and damaged trust. This project proposes a realtime bank transaction fraud detection system that integrates Apache Kafka with advanced machine learning techniques to address these challenges effectively. Machine learning models trained on historical transaction data are deployed to analyze incoming transaction streams in real time. These models utilize features such as transaction amount, location, time, and user behavior patterns to distinguish between legitimate and fraudulent transactions. By continuously updating and fine-tuning the models with new data, the system improves its accuracy and adapts to evolving fraud tactics. In conclusion, this project demonstrates the feasibility and effectiveness of combining Kafka’s real-time data streaming with machine learning-based classification for robust fraud detection in banking transactions. The proposed solution not only enhances fraud detection accuracy but also ensures minimal latency, scalability, and adaptability, making it a vital tool in securing financial operations.