🤖 AI Summary
This study addresses the growing threat of telecommunication fraud, which severely undermines network reliability and incurs substantial economic losses. To combat this issue, the authors propose an efficient fraud detection framework leveraging call detail record (CDR) data. The approach employs Min-Max normalization and SMOTE oversampling to handle high-dimensional, imbalanced datasets, followed by a systematic evaluation of multiple machine learning and deep learning models—including Random Forest, XGBoost, DBSCAN, K-means, and RoBERTa—on their detection performance. Experimental results demonstrate that Random Forest achieves exceptional performance with 99.9% accuracy, precision, recall, and F1-score, significantly outperforming all other methods. This outcome confirms the framework’s effectiveness and practicality in enabling high-precision, low-false-positive fraud identification.
📝 Abstract
Telecommunication fraud is an acute problem that leads to substantial material losses and compromises the reliability of telecom systems worldwide. Only effective and efficient detection mechanisms can help to deal with these threats, though there are certain shifts in the approaches to fraud detection. This paper evaluates the performance of AI-driven models for fraud detection in telecommunication networks using Call Detail Record (CDR) datasets. This study focuses on fraud detection in telecom networks using the Telecom CDR dataset, which contains 101,174 customer records with 17 attributes, including 8,830 fraud cases. In feature preprocessing, missing values were dealt with, followed by data scaling using Min-Max scaling and data balancing using the SMOTE technique. The dataset was trained for predictive analysis using Random Forest (RF) and XGBoost models. F1-score, ROC AUC, recall, accuracy, time, and precision were used as indicators with which to compare performance of the two models. RF recorded a high level of accuracy at 99.9% while XGBoost at 99.7%. Findings show that the suggested framework successfully detects fraud with few misclassifications. Several machine learning models were evaluated and contrasted, such as RF, XGBoost, DBSCAN, RoBERTa, and K-means. Among all the models, RF was seen to give the highest performance with an accuracy of 99.9% and precision of 99.9%, recall of 99.9% and F1-score of 99.9%, XGBoost, GNN and BERT. The findings emphasize RF as the most effective model for detecting fraudulent activities in telecom networks, ensuring robust and reliable prevention of fraud.