Improving credit card fraud detection using machine learning and GAN technology

¹ Al-Mustaqbal University, Babil, Iraq
² Computer Center Babylon University, Babil, Iraq
³ Communication Engineering Islamic University, Wardanieh, Lebanon
⁴ University of Alkafeel, Najaf, Iraq

^* Corresponding author: najwan.thaeir@mustaqbal-college.edu.iq

Abstract

The motivation behind this study stems from identifying contemporary challenges associated with prosecuting electronic financial crimes. Highlights ongoing efforts to identify and address credit card fraud and fraud as there are many credit card fraud issues in the financial industry. Traditional methods are no longer able to keep up with modern methods of tracking the behavior of credit card users and detecting suspicious cases. Artificial intelligence technology offers promising solutions to quickly detect and prevent future fraud by credit card users. Datasets used to detect financial anomalies are affected by imbalances in financial transactions, and this study aims to address the imbalance of financial fraud datasets using adversarial algorithm techniques and compare them with the most commonly used methods in the scientific literature.The results showed that the function of the adversarial algorithm is consistent in several ways, including allowing researchers and interested parties to determine data growth rates, which helps bring the dataset closer to real-time data from financial markets and banks. This study proposes a hybrid machine learning model consisting of three machine learning algorithms: decision trees, logistic regression, and Naive Bayes algorithm, and calculates performance metrics such as accuracy, specificity, precision, and F1 score. Experimental results reveal varying degrees of accuracy in fraud detection. Model testing using the SMOTE method recorded an accuracy of 98.1% and an F-score of 98.3%. On the other hand, the oversampling and under sampling test methods showed similar performance, with the two methods recording an accuracy of 94.3 and 95.3 and an F-score of 94.7 and 95.1, respectively. Finally, the GAN method excelled, receiving a test score and accuracy of 99.9%, as well as exceptional precision, recall, and F1 score. As a result, we conclude that the GAN method is able to balance the data set, which in turn is reflected in the performance of the model in training and the accuracy of predictions when tested. Historical transaction analysis identifies behavioral patterns and adapts to evolving fraud techniques. This approach enhances transaction security and protects against potential financial losses due to fraud. This contribution allows financial institutions and companies to proactively combat fraudulent activities.