  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 Synthet ic 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 demonstrat e 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
rese arch contributes to the ongoing efforts to develop effective and efficient
mechanisms for safeguarding financial transactions from fraudulent activities.
