Credit Card Fraud Detection By IJISRt

Abstract

While credit card fraud and abuse are the way becoming more common, the convenience using of there credit for the way online purchases has also improved. These fraudulent activities pose a severe financial danger to both credit the are card users and way they using it financial institutions. The first thinking aims to do of this research project is to recognize and put an end to these kinds of fraudulent activities. It addresses a broad range of subjects, including the frequency of false positives, imbalanced datasets, evolving fraud trends, and restricted public data access. The literature now under publication offers a range of machine learning-based methods, including logistic regression, decision trees, random the forests, support to the vector of the +machines, and XG Boost, with the purpose of identifying credit card fraud. However, these methods often exhibit lower accuracy rates, highlighting the need for more advanced deep learning algorithms in order to effectively lower fraud losses. Therefore, the of way they primary thinks objective of this is to improve fraud detection abilities through which the way this application most be of state- of-the-art deep learning algorithms. An assessment way it should be of the research project reveals better outcomes, including optimized AUC curves, precision, f1-score, and accuracy. The ultimate objective there are is to create models that will greatly improve credit card fraud detection and prevention. This research focuses on advanced deep learning techniques in to the way it is order they provide the to more reliable and accurate fraud detection mechanisms, enhance security for credit card users, and lower financial risks for financial institutions when conducting online transactions.