A hybrid model for customer credit scoring in stock brokerages using data mining approach Online publication date: Fri, 21-Jun-2019
by Rahmat Houshdar Mahjoub; Amir Afsar
International Journal of Business Information Systems (IJBIS), Vol. 31, No. 2, 2019
Abstract: Credit scoring has become a challenging issue for stock brokerages, as the credit industry has been facing high competition during the past decade. Many methods have been suggested to credit scoring in the literature. The purpose of this study is to set up a hybrid model for customer credit scoring in Iran's National Investment Brokerage. It also provides a way to pay appropriate facilities as tools for CRM. So, after the data pre-processing step, we convert refined dataset into RFM model. Customers were clustered using two clustering algorithms, self-organising map (SOM) and K-means. In both methods, the best optimum number of clusters was calculated at ten. Afterwards, the clusters ranked using TOPSIS and the top three clusters were considered as the target customers. Eventually, the credit score of the superior cluster members were calculated. Coefficient facilities granted to the top three clusters respectively are 0.271, 0.173 and 0.556.
Online publication date: Fri, 21-Jun-2019
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Information Systems (IJBIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com