Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms

Currently, fraud detection is employed in numerous domains, including banking, finance, insurance, government organizations, law enforcement, and so on. The amount of attempts has recently grown significantly, making critical when it comes to protecting your personal information or sensitive data. There are several forms issues, such as stolen credit cards, forged checks, deceptive accounting practices, card-not-present (CNP), This article introduces the prevention (CCFDP) method for dealing with CNP utilizing big data analytics. In order deal suspicious behavior, proposed CCFDP includes two steps: Process (FDP) process (FPP). FDP examines system detect harmful after which FPP assists preventing malicious activity. Five cutting-edge methods used step: random undersampling (RU), t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), singular value decomposition (SVD), logistic regression learning (LRL). For conducting experiments, needs balance dataset. overcome this issue, Random Undersampling used. Furthermore, better presentation, must lower dimensionality characteristics. procedure employs t-SNE, PCA, SVD algorithms, resulting a speedier training improved accuracy. (LRL) model by evaluate success failure probability fraud. Python implement suggested mechanism. We validate efficacy hypothesized mechanism based on testing results.