A Comparative Study of Chebyshev Functional Link Artificial Neural Network, Multi-layer Perceptron and Decision Tree for Credit Card Fraud Detection

With introduction of online transaction the fraudulent activities through World Wide Web have increased rapidly. It's not only affecting common people but also making them lose huge amount of money. Online transaction basically takes place between merchant and customer, and in this case neither customer nor the card needs to be present at the time of transaction so merchant does not know that whether the customer in the other end is an authorized person or fraudster, so it may lead to an unusual transaction. This kind of online transaction can be easily done using stolen credit card information of a cardholder. To detect status of the current transaction it is imperative to analyze all the previous transactions made by a genuine card holder to know the kind of pattern he/she uses. Based on these patterns new transaction can be categorized as either fraud or legal. There are few data mining techniques which help us to detect a certain pattern on complex and large data sets. In this paper it is proposed to compare Decision Tree, Multi-Layer Perceptron (MLP) and Chebyshev functional link artificial neural network (CFLANN) in terms of their classification accuracy and elapsed time for credit card fraud detection.

[1]  Bo Yuan,et al.  Application of Decision Trees in Mining High-Value Credit Card Customers , 2008 .

[2]  Zhang Jia,et al.  Research on Data Preprocessing In Credit Card Consuming Behavior Mining , 2012 .

[3]  Tej Paul Bhatla,et al.  Understanding Credit Card Frauds , 2003 .

[4]  Rüdiger W. Brause,et al.  Neural data mining for credit card fraud detection , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[5]  Ekrem Duman,et al.  A cost-sensitive decision tree approach for fraud detection , 2013, Expert Syst. Appl..

[6]  Jagdish Chandra Patra,et al.  Computationally efficient FLANN-based intelligent stock price prediction system , 2009, 2009 International Joint Conference on Neural Networks.

[7]  Yong Hu,et al.  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..

[8]  Siddhartha Bhattacharyya,et al.  Data mining for credit card fraud: A comparative study , 2011, Decis. Support Syst..

[9]  Alex ChiChung Kot,et al.  Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Ekrem Duman,et al.  Detecting credit card fraud by genetic algorithm and scatter search , 2011, Expert Syst. Appl..

[11]  Y. Sahin,et al.  Detecting credit card fraud by ANN and logistic regression , 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

[12]  S. Mishra,et al.  Chebyshev Functional Link Artificial Neural Networks for Denoising of Image Corrupted by Salt and Pepper Noise , 2009 .

[13]  Ekrem Duman,et al.  Detecting credit card fraud by decision trees and support vector machines , 2011 .

[14]  JhaSanjeev,et al.  Data mining for credit card fraud , 2011 .

[15]  Tao Guo,et al.  Neural data mining for credit card fraud detection , 2008, 2008 International Conference on Machine Learning and Cybernetics.