This study proposes an innovative approach combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance credit card fraud detection. Credit card fraud detection is a critical challenge in the financial sector due to the imbalance in transaction data, where fraudulent transactions represent a small fraction of total transactions. The study addresses this imbalance by integrating NN and SMOTE, which improves the model's ability to accurately identify fraudulent transactions. The dataset used consists of European card transactions, with a severe imbalance, where only 0.172% of transactions are labeled as fraudulent. The study includes data preprocessing steps such as feature standardization, random undersampling, feature correlation analysis, outlier detection, and t-SNE clustering. The NN model is designed to capture intricate patterns in the data, with a structure including an input layer, multiple hidden layers with ReLU activation, and a sigmoid output layer for binary classification. SMOTE is used to generate synthetic instances of the minority class, creating a more balanced dataset for training. The study evaluates the performance of the models using metrics such as precision, recall, and F1-score. Results show that the NN+SMOTE model outperforms traditional models in terms of precision, recall, and F1-score, demonstrating its effectiveness in handling imbalanced datasets. The study contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities. The integration of NN and SMOTE provides a robust solution for credit card fraud detection, highlighting the importance of addressing class imbalance through advanced techniques. The results emphasize the need for sophisticated methods to enhance the accuracy and efficiency of fraud detection systems in the digital era.This study proposes an innovative approach combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance credit card fraud detection. Credit card fraud detection is a critical challenge in the financial sector due to the imbalance in transaction data, where fraudulent transactions represent a small fraction of total transactions. The study addresses this imbalance by integrating NN and SMOTE, which improves the model's ability to accurately identify fraudulent transactions. The dataset used consists of European card transactions, with a severe imbalance, where only 0.172% of transactions are labeled as fraudulent. The study includes data preprocessing steps such as feature standardization, random undersampling, feature correlation analysis, outlier detection, and t-SNE clustering. The NN model is designed to capture intricate patterns in the data, with a structure including an input layer, multiple hidden layers with ReLU activation, and a sigmoid output layer for binary classification. SMOTE is used to generate synthetic instances of the minority class, creating a more balanced dataset for training. The study evaluates the performance of the models using metrics such as precision, recall, and F1-score. Results show that the NN+SMOTE model outperforms traditional models in terms of precision, recall, and F1-score, demonstrating its effectiveness in handling imbalanced datasets. The study contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities. The integration of NN and SMOTE provides a robust solution for credit card fraud detection, highlighting the importance of addressing class imbalance through advanced techniques. The results emphasize the need for sophisticated methods to enhance the accuracy and efficiency of fraud detection systems in the digital era.