Recent developments in e-payment systems have led to increased financial fraud, such as credit card fraud. It is therefore essential to implement detection mechanisms for credit card fraud. However, due to the unbalanced class distribution in the credit card dataset, the machine learning techniques are used to train the model based on the majority class, resulting in inaccurate fraud predictions. Therefore, this paper mainly focuses on processing unbalanced data using the oversampling technique called Elbow Fuzzy Noice filtering SMOTE (EFN-SMOTE). This method divides the dataset into multiple clusters. The number of clusters is determined by an algorithm known as the Elbow method, after which noise filtering is applied to each cluster, after that, we use the SMOTE in each cluster to synthesize a new minority instance based on the nearest majority instance of each minority instance to effectively perceive the decision boundary which leads to a balanced database by oversam-pling technique. On the other hand, the result show that EFN-SMOTE achieved better classification performance using Artificial Neural Network (ANN) with four hidden layers with 0.999 accuracy, 0.998 precision, 0.999 sensitivity, 0.998 specificity, 0.999 F-measure, and 0.999 G-Mean.