Detecting Credit Card Fraud using Machine Learning

Detecting Credit Card Fraud using Machine Learning

Fraud detection

CNN

LSTM

Auto Encoder

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.

Almuteer, Arjwan H.

Aloufi, Asma A.

Alrashidi, Wurud O.

Alshobaili, Jowharah F.

Ibrahim, Dina M.

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 24 (2021); pp. 108-122

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2021-12-21

Copyright (c) 2021 Arjwan H. Almuteer, Asma A. Aloufi, Wurud O. Alrashidi, Jowharah F. Alshobaili, Dina M. Ibrahim

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article