Comparative Analysis of ANN, RNN, and GRU for Credit Card Fraud Detection

The rapid rise of digital financial transactions has significantly increased the risk of credit card fraud, which poses substantial threats to financial institutions and consumers. This paper presents a comparison of three machine learning models—Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Gated Recurrent Units (GRU)—for detecting fraudulent credit card transactions. The study evaluates the models using four key performance metrics: Sensitivity, Specificity, Accuracy, and Error Rate. The findings reveal that the GRU model outperforms both ANN and RNN, achieving an impressive accuracy of 99.9%. These results highlight the potential of GRU in developing robust and efficient credit card fraud detection systems.