A Novel Methodology for Credit Card Fraud Detection using KNN Dependent Machine Learning Methodology

Abstract

Credit cards offer a convenient and efficient option for online transactions; however, their increasing use has led to a rise in credit card fraud, resulting in significant financial losses for both cardholders and financial institutions. This research aims to identify such frauds by considering various criteria, including the availability of public data, high-class disparity statistics, changes in fraudulent processes, and high false alarm rates. With the growth of e-payments, fraudsters have resorted to various tactics such as fake emails and data breaches to steal money during online transactions. Although these methods are inaccurate, cutting-edge machine-learning algorithms must be used to reduce fraud losses. Therefore, this study's primary focus is on the recent advancements in machine learning algorithms for credit card fraud detection. The research paper aims to investigate the application of machine learning algorithms in distinguishing between genuine and fake online transactions. In the paper, KNN is compared to other machine-learning methods for detecting credit card fraud. The proposed approach achieved an accuracy of 99.95%, a precision of 97.2%, a recall of 85.71%, and an F1-score of 90.3%.