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.