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International Conference on Applied Informatics

ICAI 2021: Applied Informatics pp 26–38Cite as

Machine Learning Classification Based Techniques for Fraud Discovery in Credit Card Datasets

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1455))

Abstract

The frequency of credit card-based online payment frauds has increased rapidly in recent years, forcing banks and e-commerce companies to create automated fraud detection systems that perform mining on massive transaction logs. Machine learning appears to be one of the most promising techniques for detecting illegal transactions since it uses supervised binary classification algorithms appropriately trained using pre-screened sample datasets to differentiate between fraudulent and non-fraudulent cases. This study aims to concentrate on machine learning (ML) methods thereby proposing a credit card fraud discovery scheme to detect fraud. The ML techniques employed are Decision Tree (DT) and K-Nearest Neighbor (KNN) ML classification techniques. The performance outcomes of the two ML classification techniques are evaluated depending on accuracy, precision, specificity, recall, f1-score, and false-positive rate (FPR). The area under the ROC curve (AUC) of the receiver operating characteristics (ROC) curve was similarly drawn built on the confusion matrix for both classifiers. The two classification techniques were evaluated and compared using the performance metrics mentioned earlier and it was demonstrated that the KNN technique outperformed that of the DT with a greater ROC curve value of 91% for KNN and 86% for DT. It was concluded that KNN is considered a better ML classification technique that can be employed to discover credit card fraudulent activities.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Roseline Oluwaseun Ogundokun

  2. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

  3. Directorate of Financial Services, Agricultural and Rural Management Training Institute, Ilorin, Nigeria

    Opeyemi Eyitayo Ogundokun

  4. Center of ICT/ICT, Covenant University, Ota, Ogun State, Nigeria

    Jonathan Oluranti

  5. Kaunas University of Technology, Kaunas, Lithuania

    Rytis Maskeliunas

Authors
  1. Roseline Oluwaseun Ogundokun
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  2. Sanjay Misra
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  3. Opeyemi Eyitayo Ogundokun
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  4. Jonathan Oluranti
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  5. Rytis Maskeliunas
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Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogotá, Colombia

    Hector Florez

  2. Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Buenos Aires, Argentina

    Ma Florencia Pollo-Cattaneo

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Ogundokun, R.O., Misra, S., Ogundokun, O.E., Oluranti, J., Maskeliunas, R. (2021). Machine Learning Classification Based Techniques for Fraud Discovery in Credit Card Datasets. 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_3

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  • DOI: https://doi.org/10.1007/978-3-030-89654-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89653-9

  • Online ISBN: 978-3-030-89654-6

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