Aim: The goal of this study is to provide an improved accuracy for credit card fraud detection using pipelining and ensemble learning methods in logistic regression compared with support vector machines algorithm to detect credit card fraud and comparing their accuracy. Materials and Methods: The sample size for logistic regression (N=10) and for support vector machines algorithm (N=10) was iterated 20 times to predict credit card fraud. Results: logistic regression has significantly better accuracy (98%) compared to support vector machines accuracy (93%)The statistical significance difference 0.00 (p<0.05 independent sample test) value states that the results in the study are significant. Conclusion: The results depicted that logistic regression provides good results in detection of credit card fraud over support vector machines.