Credit Card Fraud Detection with a Cascade Artificial Neural Network and Imperialist Competitive Algorithm

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 25
Year of Publication: 2014
Authors:
Morteza Kolali Khormuji
Mehrnoosh Bazrafkan
Maryam Sharifian
Seyed Javad Mirabedini
Ali Harounabadi
10.5120/16947-6736

Morteza Kolali Khormuji, Mehrnoosh Bazrafkan, Maryam Sharifian, Seyed Javad Mirabedini and Ali Harounabadi. Article: Credit Card Fraud Detection with a Cascade Artificial Neural Network and Imperialist Competitive Algorithm. International Journal of Computer Applications 96(25):1-9, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Morteza Kolali Khormuji and Mehrnoosh Bazrafkan and Maryam Sharifian and Seyed Javad Mirabedini and Ali Harounabadi},
	title = {Article: Credit Card Fraud Detection with a Cascade Artificial Neural Network and Imperialist Competitive Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {25},
	pages = {1-9},
	month = {June},
	note = {Full text available}
}

Abstract

Credit Card Fraud is one of the biggest threats to business establishments today. This paper presents a cascade artificial neural network for the recognition of credit card fraud detection. This system aims at attaining a very high recognition rate and a very high reliability, In other words, excellent recognition performance of credit card fraud detection was obtained. Then, One solution was proposed: utilizing a cascade artificial neural networks for enhancing recognition rate and reducing rejection rate. The gating networks (GNs) are used to congregate the confidence values of three parallel artificial neural networks (ANNs) classifiers. The Imperialist Competitive Algorithm (ICA) is a new evolutionary algorithm which was recently introduced and has a good performance in some optimization problems. The weights of the GNs are trained by the Imperialist Competitive Algorithm (ICA) to achieve the overall optimal performance. The experiments conducted on the database from a large Brazilian bank produced encouraging results: high accuracy of 98. 56% with minimal rejection in the last cascade layer.

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