Credit card fraud detection using deep learning technique

Pillai, Thulasyammal Ramiah; Hashem, Ibrahim Abaker Targio; Brohi, Sarfraz; Kaur, Sukhminder; Marjani, Mohsen

Abstract

Credit card fraud detection is growing due to the increase and the popularity of online banking. The need to detect fraudulent within credit card has become as a serious problem among the online shoppers. The multi-layer perceptron (MLP) machine learning algorithm is used to identify the credit card fraud. We have used the various parameters of the MLP to compare the performance of MLP. The aim of this paper is to design a high performance model to detect the credit card fraud using deep learning techniques. We found that logistic and hyperbolic tangent activation function offer good performance in detecting the credit card fraud. The logistic activation function performs better when there are 10 nodes, the sensitivity is 82% and when there are 100 nodes, the sensitivity is 83% respectively in the 3 hidden layer model. However, hyperbolic tangent activation function performs better when there is 1000 nodes, the sensitivity is 82% in all the number (1, 2 and 3) of hidden layers. This study will give us a guidance on how to choose a best model to obtain optimum results with minimum cost in deep learning.

Citation

Pillai, T. R., Hashem, I. A. T., Brohi, S., Kaur, S., & Marjani, M. (2019). Credit card fraud detection using deep learning technique. In 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA). https://doi.org/10.1109/ICACCAF.2018.8776797

• Conference Name: 2018 4th International Conference on Advances in Computing, Communication and Automation, ICACCA 2018
• Conference Location: Subang Jaya, Malaysia
• Start Date: Oct 26, 2018
• End Date: Oct 28, 2018
• Acceptance Date: Sep 27, 2018
• Online Publication Date: Jul 29, 2019
• Publication Date: Jul 29, 2019
• Deposit Date: Sep 9, 2022
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Series ISSN: 2642-7354
• Book Title: 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA)
• ISBN: 9781538671672
• DOI: https://doi.org/10.1109/ICACCAF.2018.8776797
• Keywords: Credit Card, Machine Learning, Analytics, deep learning, Artificial neural network, Machine learning algorithms, Neurons, backpropagation, bank data processing, credit transactions, fraud, multilayer perceptrons
• Public URL: https://uwe-repository.worktribe.com/output/9942029
• Publisher URL: https://ieeexplore.ieee.org/document/8776797
• Related Public URLs: https://ieeexplore.ieee.org/xpl/conhome/8766334/proceeding