Real-Time Anomaly Detection in Telecommunications: Advanced Data Mining Techniques for Fraud Identification

Recently, the telecom fraud issues are so intense that it has become difficult for industry professionals, not just because of monetary loss but because it tarnishes the reputation of the firm as well. In any case, real time analysis and monitoring systems are critical in curbing such transactions and activities. The main purpose of this paper is to analyze how mining processes can help in detecting telecommunication fraud from the interactions of the users, which generate significant amounts of data. In this paper, different methods of machine learning are applied: clustering, decision trees and anomaly detection to identify unusual call patterns, unauthorized use, and impersonation. To detect suspicious actions, a combination of time and frequency data from various mobile operators is used for model training and testing. The goal of this study is to improve the accuracy of fraud detection systems by applying Naive Bayes, Support Vector Machines (SVM), and deep learning networks. Furthermore, this paper indicates the possibilities of incorporating these models into the current framework of network security architecture to measure the level of scalability of such solutions in an advanced fraud environment. Along with many other findings, these results reveal that data mining can handle monitoring and prevention of frauds in real-time. This observation shows the importance of updating the fraud detection mechanisms within the telecommunication industry.