Bank customer classification in Indonesia: logistic regression vis-a-vis artificial neural networks

Abduh, Muhamad and Dahari, Zainurin and Omar, Mohd. Azmi (2012) Bank customer classification in Indonesia: logistic regression vis-a-vis artificial neural networks. World Applied Sciences Journal , 18 (7). pp. 933-938. ISSN 1818-4952

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Abstract

This paper aims to identify factors distinguish Islamic and conventional bank customers in Indonesia. It tries to relate between bank customers’ religiosity, assessment upon certain factors such as bank performance, bank advertisement and main reasons of using banking services towards their decision on which bank they had joined. Logistic regression and neural networks models are used to answer the research questions based on 520 customers reside in Jakarta. Data collection is done through a direct survey using self administered questionnaire. The results from logistic regression and neural networks models demonstrate that shariah compliant issues, customers’ awareness on the fatwa announced by National Ulama Council on the impermissibility of bank interest, safety of fund as main reason of using banking services and customers’perception on bank advertisement are the significant factors which classify the bank customers in Indonesia. Nonetheless, neural network classifies better than logistic regression.

Item Type:	Article (Journal)
Additional Information:	6777/25482
Uncontrolled Keywords:	Neural networks % Classification % Bank customers % Logistic regression % Indonesia
Subjects:	H Social Sciences > HA Statistics > HA29 Theory and method of social science statistics H Social Sciences > HG Finance > HG1501 Banking
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Institute of Islamic Banking & Finance (IIiBF) Kulliyyah of Economics and Management Sciences > Department of Business Administration
Depositing User:	Dr Muhamad Abduh
Date Deposited:	09 Aug 2012 14:04
Last Modified:	09 Aug 2012 14:04
URI:	http://irep.iium.edu.my/id/eprint/25482

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