A Novel Approach for Credit Card Fraud Detection using Gated Recurrent Unit (GRU) Networks

Mobile payment systems have become increasingly popular with the rise of smartphones, but this has also attracted fraudsters. As fraud rates continue to rise, researchers have turned to machine learning methods to detect and analyze fraudulent transactions effectively. The purpose of this study is to propose the use of the Gated Recurrent Unit (GRU) is an effective method for analyzing and detecting patterns in sequential data, particularly for applications such as fraud detection in mobile payment systems. The GRU is chosen for its ability to capture temporal dependencies in data while being computationally efficient compared to other recurrent neural network variants like LSTM. This study will explore the implementation of GRU, evaluate its performance, and compare it with existing algorithms to highlight its advantages in real-time fraud detection scenarios. The proposed GRU method provides an efficient and effective approach for detecting fraud in credit card transactions by leveraging its ability to process and learn from sequential data. The use of GRUs can improve detection accuracy and efficiency, contributing to more robust fraud detection systems, and ultimately helping protect consumers and financial institutions from financial losses.