Abstract:
The incidence of fraudulent operations has been increasing on a global scale, leading to substantial financial losses for firms, which can result in serious financial harm. Researchers have proposed several ways in different applications. Examining these methodologies can facilitate a more comprehensive comprehension of the issue. This research aims to explore the potential of Artificial Intelligence and Machine Learning in enhancing fraud detection in the field of telecommunications. This paper provides an overview of several forms of fraud in the telecommunications industry and explores the application of Artificial Intelligence and Machine Learning approaches for fraud detection. This study evaluates the performance of state-of-the-art AI and ML techniques, including Decision Tree, Random Forest, ADA Boost, and XG Boost, using machine learning methods.
