People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. The detailed empirical analysis is carried out using the Credit card fraud dataset for fraud detection. Deep Convolutional Spiking Neural Network is applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out using variations in the number of hidden layers, periods, and recent specimens.
Credit card fraud detection Dataset
Image
#online, #Transaction, #Efficient, #Facility, #Usage, #Capacity, #Engaged, # significant, # financial, #algorithms, #Procedure, #development #purpose, # Convolutional, # Spiking, #Misuse, #Detection, #Network, #Neural ,# empirical, #Automated, #Parameters, #Accuracy, #Efficiency, # Analysis, #Intensity, # Comprehensive, # Variations, #Content, # Specimens
[1].Nicolas Racchi, Alberto Parrvicini, Guido Walter Di Donato in 2021-Fraud Prevention and Detection on Heterogeneous Information Networks with Deep Graph Infomax –IEEE [2].Hangjun Zhou, Guang Sun, Sha Fu, Linli Wang in 2021. Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec IEEE.