Predicting customers churning in banking industry: A machine learning approach

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 whi...

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Main Authors: Muneer, A., Ali, R.F., Alghamdi, A., Taib, S.M., Almaghthawi, A., Abdullah Ghaleb, E.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.33155 /
Published: Institute of Advanced Engineering and Science 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128708288&doi=10.11591%2fijeecs.v26.i1.pp539-549&partnerID=40&md5=0be0535c6df41298210e5507ca3e10cf
http://eprints.utp.edu.my/33155/
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spelling utp-eprints.331552022-06-09T08:23:12Z Predicting customers churning in banking industry: A machine learning approach Muneer, A. Ali, R.F. Alghamdi, A. Taib, S.M. Almaghthawi, A. Abdullah Ghaleb, E.A. 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. Institute of Advanced Engineering and Science 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128708288&doi=10.11591%2fijeecs.v26.i1.pp539-549&partnerID=40&md5=0be0535c6df41298210e5507ca3e10cf 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. http://eprints.utp.edu.my/33155/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Article
author Muneer, A.
Ali, R.F.
Alghamdi, A.
Taib, S.M.
Almaghthawi, A.
Abdullah Ghaleb, E.A.
spellingShingle Muneer, A.
Ali, R.F.
Alghamdi, A.
Taib, S.M.
Almaghthawi, A.
Abdullah Ghaleb, E.A.
Predicting customers churning in banking industry: A machine learning approach
author_sort Muneer, A.
title Predicting customers churning in banking industry: A machine learning approach
title_short Predicting customers churning in banking industry: A machine learning approach
title_full Predicting customers churning in banking industry: A machine learning approach
title_fullStr Predicting customers churning in banking industry: A machine learning approach
title_full_unstemmed Predicting customers churning in banking industry: A machine learning approach
title_sort predicting customers churning in banking industry: a machine learning approach
publisher Institute of Advanced Engineering and Science
publishDate 2022
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
http://eprints.utp.edu.my/33155/
_version_ 1741197811079184384
score 11.62408

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