Telecom fraud detection with big data analytics
Duygu Sinanc Terzi, Şeref Sağıroğlu, Hakan Kılınç · International Journal of Data Science · 2021
The rapid development in telecom has also led to an increase in fraud activities, which causes both revenue and reputation losses. For this reason, this paper proposes a new telecom fraud detection model based on behaviour deviations of users expressed through time-varying signatures. In line with the similarity of these deviations to known frauds, a suspect list has been created and reported to fraud experts for the final decision. The proposed model was developed with the MapReduce parallel programming paradigm, which provides simplicity and flexibility for large-scale applications. Finally, the model was applied on call detail records of a telecom company. The obtained results have shown that the proposed approach detects the telecom frauds with 86% success and is suitable for application into a fraud management system for real-world implementation.