Abstract
Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrate that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This research contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.
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