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2021 | OriginalPaper | Chapter

A Supervised Approach to Credit Card Fraud Detection Using an Artificial Neural Network

Authors : Oluwatobi Noah Akande, Sanjay Misra, Hakeem Babalola Akande, Jonathan Oluranti, Robertas Damasevicius

Published in: Applied Informatics

Publisher: Springer International Publishing

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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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previous chapter A Chatterbot Based on Genetic Algorithm: Preliminary Results

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Title: A Supervised Approach to Credit Card Fraud Detection Using an Artificial Neural Network
Authors: Oluwatobi Noah Akande
Sanjay Misra
Hakeem Babalola Akande
Jonathan Oluranti
Robertas Damasevicius
Publisher: Springer International Publishing
Book: Applied Informatics
Print ISBN: 978-3-030-89653-9

Electronic ISBN: 978-3-030-89654-6

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-89654-6_2

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