Predicting Internet Banking Effectiveness using Artificial Model

Abstract

This research aims at building a prediction model to predict the effectiveness of internet banking (IB) in Qatar. The proposed model employs the aspect of hybrid approach through using the regression and neural network models. This study is one of the fewest to evaluate the effectiveness of IB through adopting two data mining approaches including regression and neural networks. The regression analysis is used to optimize and minimize the input dataset metrics through excluding the insignificant attributes. The study builds a dataset of 250 records of internet banking quality metrics where each instance includes 8 metrics. Moreover, the study uses the rapidminer application in building and validating the proposed prediction model. The results analysis indicates that the proposed model predicts the 88.5% of IB effectiveness, and the input attributes influence the customer satisfaction. Also, the results show the prediction model has correctly predict 68% of the test dataset of 50 records using neural networks without regression optimization. However, after employment of regression, the prediction accuracy of satisfaction improved by 12% (i.e. 78%). Finally, it is recommended to test the proposed model in the prediction in other online services such as e-commerce.