Detecting Credit Card Fraud using Machine Learning

Authors

  • Arjwan H. Almuteer College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Asma A. Aloufi College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Wurud O. Alrashidi College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Jowharah F. Alshobaili College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Dina M. Ibrahim College of Computer, Qassim University, Buraydah, Saudi Arabia and Faculty of Engineering, Tanta University, Tanta, Egypt

DOI:

https://doi.org/10.3991/ijim.v15i24.27355

Keywords:

Fraud detection, CNN, LSTM, Auto Encoder

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.

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Published

2021-12-21

How to Cite

Almuteer, A. H., Aloufi, A. A., Alrashidi, W. O., Alshobaili, J. F., & Ibrahim, D. M. (2021). Detecting Credit Card Fraud using Machine Learning. International Journal of Interactive Mobile Technologies (iJIM), 15(24), pp. 108–122. https://doi.org/10.3991/ijim.v15i24.27355

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Section

Papers