This paper proposes a Credit Card Fraud Detection system based on Operational & Transaction features using Support Vector Machine (SVM) and Random Forest (RF) classifiers. In this system, in the first phase, the operational features of users are extracted, and then a random forest classifier is used to classify the features into benign and suspected. In the second phase, the transaction features of users are extracted from the user records, and then the M-class SVM classifier is applied to classify the features into benign and suspected. The performance of the system is evaluated in terms of standard measures precision, accuracy, recall, and F-1 score. By results, it was shown that both RF and SVM classifiers achieve a higher detection rate with good accuracy.