Credit Card Fraud Detection Using Deep Learning Technique
Thulasyammal Ramiah Pillai, Mohamed Hashem, Sarfraz Nawaz Brohi, Sukhminder Kaur, Mohsen Marjani · 2018
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.