Deep Neural Networks for Detection of Credit Card Fraud
Angelin Lalev ()
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Angelin Lalev: D.A. Tsenov Academy of Economics, Svishtov, Bulgaria
Conferences of the department Informatics, 2019, issue 1, 263-274
The purpose of this paper is to present preliminary results from ongoing study, concerning application of Deep Neural Networks (DNN) to the detection of credit card fraud. The main approach is testing the change of different archi-tectural parameters of the neural network. The influence of the change on the performance and convergence of the network is measured. Some of the chosen parameters are the depth of the network, the width of each layer and the chosen activation functions in each layer. The main results of this study indicate that deep neural networks with moderate number of hidden layers in combination with techniques of oversampling are performing markedly better in detecting fraudulent transactions than networks with only one hidden layer. The used sample dataset is imbalanced, which means that the DNN training on the dataset has tendency to overfit. The results of this study are useful for other researchers and practitioners who try to analyze real datasets in order to detect card frauds.
Keywords: credit card fraud; deep neural networks; DNN; imbalanced dataset (search for similar items in EconPapers)
JEL-codes: C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:vrn:katinf:y:2019:i:1:p:263-274
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