This research proposes an innovative approach to enhance credit card fraud detection by integrating Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE). The study addresses the inherent imbalance in credit card transaction data, which poses a significant challenge for traditional fraud detection models. By combining NN's ability to capture complex patterns with SMOTE's method of generating synthetic minority class instances, the research aims to improve precision, recall, and F1-score. The methodology involves preprocessing the dataset, which includes European card transactions from September 2013, to address class imbalance and analyze key features. Preprocessing steps include feature standardization, random undersampling, feature correlation analysis, outlier detection, and t-SNE clustering. The NN model is designed with an input layer, multiple hidden layers using ReLU activation functions, and a sigmoid activation function in the output layer for binary classification. SMOTE is used to generate synthetic minority class instances, enhancing the robustness of the model. The experimental results demonstrate that the NN + SMOTE model outperforms traditional models in identifying fraudulent transactions, with superior precision, recall, and F1-score. The study contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.This research proposes an innovative approach to enhance credit card fraud detection by integrating Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE). The study addresses the inherent imbalance in credit card transaction data, which poses a significant challenge for traditional fraud detection models. By combining NN's ability to capture complex patterns with SMOTE's method of generating synthetic minority class instances, the research aims to improve precision, recall, and F1-score. The methodology involves preprocessing the dataset, which includes European card transactions from September 2013, to address class imbalance and analyze key features. Preprocessing steps include feature standardization, random undersampling, feature correlation analysis, outlier detection, and t-SNE clustering. The NN model is designed with an input layer, multiple hidden layers using ReLU activation functions, and a sigmoid activation function in the output layer for binary classification. SMOTE is used to generate synthetic minority class instances, enhancing the robustness of the model. The experimental results demonstrate that the NN + SMOTE model outperforms traditional models in identifying fraudulent transactions, with superior precision, recall, and F1-score. The study contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.