Credit Card Fraud Detection Using an Ensemble Deep Learning Approach: CNN, LSTM, and DNN
DOI:
https://doi.org/10.32350.umt-air.61.04Keywords:
Machine learning, deep learning, Convolutional Neural Networks (CNN), Long Short-Term Memory, dense neural network (DNN), voting ensemble classifier, Credit card fraud detection (CCFD)Abstract
The prevalence of credit card fraud, resulting in substantial financial losses for both banks and customers, poses a significant challenge to the financial sector. This issue has been exacerbated by the surge in internet transactions, which complicates the task of fraud detection. Numerous machine learning methods, such as the extreme learning method, decision tree, Random Forest, Support Vector Machine, and Logistic Regression, have been employed in recent research. However, the current landscape now embraces deep learning algorithms, which enhance accuracy and performance. This paper introduces a credit card fraud detection system that harnesses Deep Learning Algorithms to scrutinize transactional data and identify potential instances of fraudulent activity, thereby addressing this concern. The system employs a range of features to construct a real-time fraud detection model. A comprehensive empirical analysis has been conducted, encompassing the latest model, variations in the number of epochs, and hidden layer configurations. The system's evaluation includes parameters such as accuracy, precision, recall, and F1 score. The results substantiate the system's efficacy in detecting credit card fraud, outperforming existing systems. The proposed method offers a reliable and effective means of real-time credit card fraud identification, boasting an exceptional accuracy rate of 99.95%.
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