A scalable machine learning framework for big data-based financial fraud detection

Abstract

The rapid growth of digital transactions has made financial fraud increasingly sophisticated, which in turn has created a situation where the demand for sophisticated and scalable detection systems is urgent. This research presents a scalable machine learning framework for big data–based financial fraud detection, which is able to handle extremely large and fast-moving transactional datasets and still provide real-time responsiveness. The framework combines distributed big data technology with advanced machine learning algorithms to reveal hidden patterns, spot anomalies, and adjust to the changing nature of fraud. Feature engineering, ensemble learning, and imbalance-handling strategies are some of the techniques that have been applied to improve accuracy and robustness. The system uses parallel processing to achieve fast training and testing, thus making it possible to use in the context of large-scale finance. The results from the experiments show a considerable enhancement in precision, recall, and speed of detection when compared to the traditional methods. The framework suggested is a trustworthy one, along with the features of being scalable and high-performance, thus it can effectively support modern financial systems in implementing a proactive and data-driven approach to fraud prevention