Aim: To Predict the Transaction data simulator for Credit Card Fraud detection using Novel Logistic Regression Algorithm and Compare the accuracy rate with Support Vector Machines Algorithm. Materials and Methods: Using the gradient boosting builds on tree and Support vector Machine algorithm, this study implements the process to detect the fraudulent and to get the best accuracy by comparing the algorithms. Here the G-power test analysis was carried out with a confidence interval of 80% and the sample size for the two groups are 20. Result: Novel Logistic Regression algorithm was done using Data Quality issues and Support Vector Machine Algorithm with the Accuracy of 94.20% and 76.00% respectively. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.002 (p<0.05) by performing independent samples t-tests. Conclusion: Novel Logistic Regression performs significantly better than the Support Vector Machines in Credit Card Fraudulent detection.