Aim: To enhance the accuracy in credit card fraud detection using Novel Logistic Regression and Gradient Boosting Ensemble Classifier. Materials and Methods: This study contains Novel Logistic Regression and Gradient Boosting Ensemble Classifier. Each algorithm consists of a sample size of 70 and the study parameters include alpha value 0.05, beta value 0.2 and the power value 0.8. Their accuracies are compared with each other using different sample sizes also. Results: The Novel Logistic Regression is 93.59% more accurate than Gradient Boosting Ensemble Classifier of 92.70% in detecting fraudulent transactions. Significance value for accuracy and loss is 0.030 (p<0.05). Conclusion: The Novel Logistic Regression model is significantly better than Gradient Boosting Ensemble Classifier in detecting fraudulent transactions. It can be also considered as a better option for credit card fraud detection.