Abstract
Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.
Highlights
The detection of credit card fraud has recently spread due to increased fraud that can be described as a deliberate ploy committed to achieve some kind of gain, usually based on money
We study and analyze the history of experiments and research of scholars and experts on fraud, after which the proposed system will be presented and discussed briefly Credit card fraud can be described as illegal usage of Mastercard information for online purchase
Some machine learning methods can be employed to collecting data, which is what we really discuss in this article [1]
Summary
The detection of credit card fraud has recently spread due to increased fraud that can be described as a deliberate ploy committed to achieve some kind of gain, usually based on money. In recent times the use of electronic devices in payment methods such as credit cards, as a result of which, credit card fraud has increased [1], as the majority of people have become shopping through the Internet and pay and pay their bills from credit cards and pay as well, and people can get money and transfer them using their online banking systems. All this technology our lives are easier and faster, despite all these positive aspects.
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