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Bassam Kasasbeh
Applied Science Private University

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Multilayer perceptron artificial neural networks-based model for credit card fraud detection Bassam Kasasbeh; Balqees Aldabaybah; Hadeel Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp362-373

Abstract

Nowadays, credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.
Co-Authors Balqees Aldabaybah Hadeel Ahmad