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

Razaque A. Frej M.B.H. Bektemyssova G. Amsaad F. Almiani M. Alotaibi A. Jhanjhi N.Z. Amanzholova S. Alshammari M.
January 2023 MDPI

Applied Sciences (Switzerland)
2023 #13 Issue 1

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

Big data analysis , CNP , fraud detection , fraud prevention , LRL , PCA , RU , SVD , t-SNE

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Department of Cyber Security, International Information Technology University, Almaty, 050000, Kazakhstan
Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, 06604, CT, United States
Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
Department of Computer Science, Joshi Research Center, University of Wright, Dayton, 45435, OH, United States
Department of Management Information System, Gulf University for Science and Technology, Kuwait City, 32093, Kuwait
Computers and Information Technology College, Taif University, Taif, 21974, Saudi Arabia
School of Computer Science, Taylor’s University, Subang Jaya, 47500, Malaysia

Department of Cyber Security
Department of Computer Science and Engineering
Department of Computer Engineering
Department of Computer Science
Department of Management Information System
Computers and Information Technology College
School of Computer Science

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