eprintid: 16897
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/68/97
datestamp: 2023-12-19 03:23:23
lastmod: 2023-12-19 03:23:23
status_changed: 2023-12-19 03:07:05
type: article
metadata_visibility: show
creators_name: Muneer, A.
creators_name: Ali, R.F.
creators_name: Alghamdi, A.
creators_name: Taib, S.M.
creators_name: Almaghthawi, A.
creators_name: Abdullah Ghaleb, E.A.
title: Predicting customers churning in banking industry: A machine learning approach
ispublished: pub
note: cited By 7
abstract: In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. For this purpose, we develop a customer churn prediction approach with the three intelligent models random forest (RF), AdaBoost, and support vector machine (SVM). This approach achieves the best result when the synthetic minority oversampling technique (SMOTE) is applied to overcome the unbalanced dataset and the combination of undersampling and oversampling. The method on SMOTED data has produced excellent results with a 91.90 F1 score and overall accuracy of 88.7 using RF. Furthermore, the experimental results show that RF yielded good results for the full feature-selected datasets. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
date: 2022
publisher: Institute of Advanced Engineering and Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128708288&doi=10.11591%2fijeecs.v26.i1.pp539-549&partnerID=40&md5=0be0535c6df41298210e5507ca3e10cf
id_number: 10.11591/ijeecs.v26.i1.pp539-549
full_text_status: none
publication: Indonesian Journal of Electrical Engineering and Computer Science
volume: 26
number: 1
pagerange: 539-549
refereed: TRUE
issn: 25024752
citation:   Muneer, A. and Ali, R.F. and Alghamdi, A. and Taib, S.M. and Almaghthawi, A. and Abdullah Ghaleb, E.A.  (2022) Predicting customers churning in banking industry: A machine learning approach.  Indonesian Journal of Electrical Engineering and Computer Science, 26 (1).  pp. 539-549.  ISSN 25024752