Fraud Detection in Mobile Phone Recharge Transactions Using K-Means and T-SNE Visualization

Abstract

The surge in digital transactions has introduced vulnerabilities in mobile recharge systems, making them susceptible to fraudulent activities that compromise financial security and operational integrity. This study presents to address these challenges by employing a novel fraud detection framework that integrates K-Means clustering and t-Distributed Stochastic Neighbour Embedding (t-SNE) visualization. This work advances the field by integrating scalable, unsupervised learning techniques with robust visualization tools, offering a practical framework for fraud detection in mobile recharge systems. Leveraging a dataset of over 200,000 transactions, this research systematically identifies anomalies indicative of fraudulent behaviour, focusing on key transactional attributes such as processing times, geographic patterns, and error frequencies. The methodology begins with data preprocessing to ensure consistency, followed by the application of K-Means clustering to partition transactions into meaningful clusters. To enhance interpretability, t-SNE visualization is employed, enabling a clear representation of high-dimensional data and the identification of anomalous patterns. A comparative analysis with Autoencoders highlights the strengths of K-Means in terms of computational efficiency, interpretability, and clustering quality, as evidenced by higher Silhouette Scores (0.6215) and lower Davies-Bouldin Index values (0.7074). The combination of K-Means and t-SNE enables service providers to identify fraudulent activities with greater precision, offering actionable insights to mitigate financial risks. This study not only addresses the critical need for robust fraud detection systems but also lays a strong foundation for future advancements through the integration of hybrid models and enhanced feature engineering, demonstrating its adaptability to similar domains.