(untitled) (International Journal of Information Technology and Computer Engineering, 2025)
Credit cards are now potentially the most popular mode of payment for both offline and online purchases thanks to new developments in electronic commerce systems and communication technology; as a result, there is much more fraud involved with such transactions. Every year, fraudulent credit card transactions cause businesses and individuals to lose a lot of money, and con artists are constantly looking for new tools and techniques to commit fraud. Researchers face a difficult task when trying to identify credit card theft since criminals are quick-thinking and inventive. The dataset provided for credit card fraud detection is severely unbalanced, making it difficult for the system to detect fraud. The use of credit cards is quite important in today’s economy. It is an essential component of every family, company, and global enterprise. While using credit cards responsibly and safely can have many benefits, engaging in fraudulent behaviour can have a negative impact on your credit and finances. There have been several solutions proposed to address the escalating credit card theft. The increased use of electronic payments is now significantly impacted by the detection of fraudulent transactions. As a result, methods that are efficient and effective for identifying fraud in credit card transactions are required. Gradient Boosting Classifier, a machine learning methodology, is suggested in this research as a smart method for identifying fraud in credit card transactions. The experimental results show that the suggested approach worked better than other machine learning algorithms and reached the maximum accuracy performance, with training accuracy of 100% and test accuracy of 91%