Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They have somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. The work presented in this paper provides an empirical study and analysis of supervised learning techniques, that Logistic regression, K nearest neighbours, SVM, Random forest, Naïve Bayes , on a bench mark credit card transaction dataset. The performance results have been evaluated and compared to identify the best predictive technique. The techniques have been used to detect whether a given transaction is fraudulent or not.