As electronic commerce has gained widespread popularity, payments made for users' transactions through credit cards also gained an equal amount of reputation. Whenever shopping through the web is made, the chance for the occurrence of fraudulent activities are escalating. In this paper, we have proposed a three-phase scheme to detect fraudulent activities. A profile for the card users based on their behavior is created by employing a machine learning technique in the second phase extraction of a precise communicative pattern for the card users depending upon the accumulated transactions and the user's earlier transactions. A collection of classifiers are then trained based on all behavioral pattern. The trained collection of classifiers are then used to detect the fraudulent online activities that occurred. If an emerging transaction is fraudulent, feedback is taken, which resolves the drift's difficulty in the notion. Experiments performed indicated that the proposed scheme works better than other schemes.