Fraud is a widespread issue in our society and affects private as well as government entities. Every year, telecom operators lose millions of dollars to fraudsters, and thus, use fraud management systems to detect and prevent fraud. Early such systems were based on a rule-based approach, however, it is not the best for detecting fraud nowadays. A rule-based approach is reactive in nature and has many drawbacks, and machine-learning techniques can help to overcome such limitations. In this paper, we have explored many machine-learning models, like XGBoost, random forest, support vector machine, decision tree, and artificial neural network, to detect telecom frauds. We have compared the performance of these five supervised machine-learning algorithms. We have used call detail records as the primary source of information to feed into these models. Our research reveals highly encouraging results and provides valuable insight into the performance of the individual models, which might translate into a guide for the Telcos in their proactive journey for fraud prevention. The experimental results indicate that XGBoost achieves better accuracy than other classifiers (99.9%), while random forest has the second-highest accuracy. The algorithms with the third, fourth, and lowest accuracy are decision tree, support vector machine, and artificial neural network, respectively. Hence, we can say that XGBoost is the more appropriate classification model for telecommunication fraud detection.