Abstract
In this article, the problem of proper identification of the frauds in credit card transactions is investigated. The transactions are represented in the form of the data set where one of the variables describes the outcome – the fraud or the genuine action. Five state-of-the-art computational intelligence models are used to identify and predict the fraud. The accuracy, the receiver operating characteristic (ROC) and precision-recall (PR) analysis are utilized to measure the efficiency of the models. It is shown what performance measures must be selected in order to correctly detect fraudulent transactions.
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Acknowledgements
This article is based upon work from COST Action 17124 DigForAsp, supported by COST (European Cooperation in Science and Technology), www.cost.eu and is partially financed by Rzeszow University of Technology, within the subsidy for the maintenance and development of research potential (UPB) and Grant for Statutory Activity from Faculty of Physics and Applied Computer Science of the AGH University of Science and Technology in Cracow.
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Kusy, M., Kowalski, P.A. (2022). Detection of Fraudulent Credit Card Transactions by Computational Intelligence Models as a Tool in Digital Forensics. In: Harmati, I.Á., Kóczy, L.T., Medina, J., Ramírez-Poussa, E. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems 3. Studies in Computational Intelligence, vol 959. Springer, Cham. https://doi.org/10.1007/978-3-030-74970-5_24
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