Abstract
The wide acceptability and usage of credit card-based transactions can be attributed to improved technological availability and increased demand due to ease of use. As a result of the increased adoption levels, this domain has become profitable and one of the most popular targets for fraudsters who use it to conduct regular exploitations or assaults. Merchants and financial processing providers that sell credit cards suffer substantial financial damages as a result of credit card theft. Because of the possibility of large casualties, it is one of the most serious risks to these organizations and individuals. Credit card fraudulent transaction can be viewed as a binary classification task in which a supervised machine learning technique could be used to analyze and classify a credit card transaction dataset into genuine or fraudulent cases. Therefore, this study explored the use of Artificial Neural Network (ANN) for credit card fraud detection. ULB Machine Learning Group dataset that has 284, 315 legitimate and 492 fraudulent transaction were used to validate the proposed model. Performance evaluation results revealed that model achieved a 100% and 99.95% classification accuracy during training and testing respectively. This affirmed the fact that ANN model could be efficiently used to predict credit card fraudulent transactions.
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Authors appreciate Covenant University Centre for Research, Innovation and Development for sponsoring the publication of this article.
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Akande, O.N., Misra, S., Akande, H.B., Oluranti, J., Damasevicius, R. (2021). A Supervised Approach to Credit Card Fraud Detection Using an Artificial Neural Network. In: Florez, H., Pollo-Cattaneo, M.F. (eds) Applied Informatics. ICAI 2021. Communications in Computer and Information Science, vol 1455. Springer, Cham. https://doi.org/10.1007/978-3-030-89654-6_2
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