Secure Banking Transactions Through Real-Time Fraud Detection Using Apache Kafka and Machine Learning
TL;DR
This project presents a real-time bank transaction fraud detection system that combines Apache Kafka with Machine Learning to identify suspicious transactions as they occur and reduces the delay associated with traditional batch-processing methods and enables faster decision-making.
Abstract
The rapid growth of digital banking has increased the number of online transactions, making fraud detection an important challenge for financial institutions. This project presents a real-time bank transaction fraud detection system that combines Apache Kafka with Machine Learning to identify suspicious transactions as they occur. Apache Kafka is used to stream transaction data continuously, while a Random Forest classifier analyzes each transaction and predicts whether it is genuine or fraudulent. Before training the model, the transaction dataset is cleaned, encoded, and normalized to improve prediction accuracy. A Flask-based web application provides an easy-touse interface for generating transaction streams and displaying fraud detection results in real time. The proposed system reduces the delay associated with traditional batch-processing methods and enables faster decision-making. Its modular design also allows the system to handle increasing transaction volumes, making it suitable for modern banking environments that require secure, reliable, and scalable fraud detection solutions.