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Credit Card Fraud Detection with Unsupervised Algorithms

Maria R. Lepoivre, Chloé O. Avanzini, Guillaume Bignon, Loïc Legendre, and Aristide K. Piwele
ECE Paris School of Engineering, Paris, France
Abstract—According to international credit card organisms such as VISA, there are more and more credit card frauds, both in quantity and in amount. To cure the problem, an anti-fraud project is developed using a combination of two unsupervised algorithms: Principal Component Analysis and SIMPLEKMEANS algorithm. To augment model accuracy, geographic positions of the transaction and of the client are added to traditional studied data, as everybody is fully connected with smartphones nowadays and as such tendency is growing up for a near future. Good results are obtained for proposed model on created test data base by achieving the foreseeing results and getting the classification of possible frauds.

Index Terms—data mining, credit card, fraud detection, principal component analysis, SIMPLEKMEANS algorithm

Cite: Maria R. Lepoivre, Chloé O. Avanzini, Guillaume Bignon, Loïc Legendre, and Aristide K. Piwele, "Credit Card Fraud Detection with Unsupervised Algorithms," Vol. 7, No. 1, pp. 34-38, February, 2016. doi: 10.12720/jait.7.1.34-38
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