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Classification of Bank Customers by Data Mining: a Case Study of Mellat Bank branches in Shiraz

Author

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  • Dariush Farid
  • Hojjatollah Sadeghi
  • Elahe Hajigol
  • Nadiya Parirooy

Abstract

This research predicts through studying significant factors in customer relationship management and applying data mining in bank. Financial institutions and other firms in competitive market need to follow proper understanding of customer behavior. Customers’ data are analyzed to identify specific opportunities and investment, to classify and predict the behaviors; further, data are eventually used for decision-making. Therefore, data mining as knowledge exploring (discovery) approach plays a significant role through a variety of algorithms. This study classifies bank customers by using decision tree algorithm. Three decision tree models including ID3, C4.5, and CART were applied for classifying and finally for prediction. Results of simple sampling method and k-fold cross validation show that forecast accuracy of C4.5 decision tree using simple sampling was higher than other models. Thus, predicting customers’ behavior through C4.5 decision tree was considered the ideal prediction for bank.

Suggested Citation

  • Dariush Farid & Hojjatollah Sadeghi & Elahe Hajigol & Nadiya Parirooy, 2016. "Classification of Bank Customers by Data Mining: a Case Study of Mellat Bank branches in Shiraz," International Journal of Management, Accounting and Economics, International Journal of Management, Accounting and Economics, vol. 3(8), pages 534-543.
    • Handle: RePEc:air:journl:v:3:y:2016:i:8:p:534
    DOI: 10.5281/zenodo.17470972
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