Title: Using data mining and neural networks techniques to propose a new hybrid customer behaviour analysis and credit scoring model in banking services based on a developed RFM analysis method

Authors: Mahmood Alborzi; Mohammad Khanbabaei

Addresses: Faculty of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran ' Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract: Nowadays, credit scoring is one of the major activities in banks and other financial institutions. Also, banks need to identify customers' behaviour to segment and classify valuable customers. Data mining techniques and RFM analysis method can help banks develop customer behaviour analysis and credit scoring systems. Many researchers have deployed credit scoring and RFM analysis method in their studies, separately. In this paper, a new hybrid model of behavioural scoring and credit scoring based on data mining and neural networks techniques is presented for the field of banking. In this hybrid model, a new enhanced WRFMLCs analysis method is developed using clustering and classification techniques. The results demonstrate that the proposed model can be deployed to effectively segment and classify valuable bank customers.

Keywords: data mining; neural networks; customer behaviour analysis; credit scoring; behavioural scoring; RFM analysis; recency; frequency; monetary value; banking industry; bank services; clustering; classification; valuable customers; bank customers

DOI: 10.1504/IJBIS.2016.078020

International Journal of Business Information Systems, 2016 Vol.23 No.1, pp.1 - 22

Received: 05 Jul 2014
Accepted: 31 Jan 2015
Published online: 31 Jul 2016 *

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