A hybrid model for customer credit scoring in stock brokerages using data mining approach

Rahmat Houshdar Mahjoub and Amir Afsar

International Journal of Business Information Systems, 2019, vol. 31, issue 2, 195-214

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.

Keywords: credit scoring; customer relationship management; CRM; self-organising map; SOM; K-means; data mining; TOPSIS; stock brokerages. (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:31:y:2019:i:2:p:195-214

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