Data Science Strategies for Fraud Detection in Telecommunication Networks

Abstract

The rapid growth of telecommunication networks has led to an increase in fraud activities, posing significant challenges to service providers. Telecommunication fraud encompasses a wide range of malicious activities, such as identity theft, unauthorized access, and manipulation of billing systems, which can lead to significant financial losses. In this research paper, we explore the application of data science strategies in detecting and mitigating fraud in telecommunication networks. Various techniques including machine learning, statistical analysis, and anomaly detection are discussed in the context of telecommunication fraud detection. The paper highlights the importance of leveraging large-scale data, real-time processing, and advanced analytical methods to improve the accuracy and efficiency of fraud detection systems. We also evaluate the effectiveness of different algorithms and provide insights into their strengths and weaknesses. Finally, we discuss the future trends in fraud detection and the role of emerging technologies in combating fraud in telecommunication networks.