International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 7 (July 2022), Pages: 139-149

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 Original Research Paper

 Building a proper churn prediction model for Vietnam's mobile banking service

 Author(s): Nguyen Thi Ha Thanh 1, Nguyen Thao Vy 2, *

 Affiliation(s):

 1Faculty of Banking and Finance, Foreign Trade University, Hanoi, Vietnam
 2MB Shinsei Finance Limited Liability Company, Hanoi, Vietnam

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-5007-8821

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.07.014

 Abstract:

This study aims to build a model predicting the churn rate of customers using mobile banking services in Vietnam by applying data mining techniques. Customer churn is an issue that any service provider must pay attention to because it is decisive to the development of the business. The competition between banks is getting tougher, hence customer churn prediction has become of great concern to banking service companies. It is necessary for banks to collect colossal data and establish a valued model for classifying types of customers. In this study, three supervised statistical learning methods which are KNN, Random Forest, and Gradient Boosting are applied to the churn prediction model using the data source of VIB’s customers. In addition to selecting models belonging to the group of weak single learners such as Neural Networks, Naïve Bayes Classifier, and K-nearest Neighbor..., this paper utilizes Random Forest and Gradient Boosting which are assessed as better models because they can combine weak learners for improving model efficiency and capable of classification. The results exhibited that Gradient Boosting is the best performance in the three above classifiers with a 79.71% of accuracy rate, and 86.23% of ROC (Receiver Operating Characteristic) curve graph. Moreover, the decision tree algorithm generates readable rules for churner and non-churner classification which are potentially helpful to managers. Finally, this study suggests a proper model that can be used to forecast churners of mobile banking services in Vietnam.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Customer churn, Prediction model, Mobile banking, Data mining, Machine learning

 Article History: Received 14 January 2022, Received in revised form 15 April 2022, Accepted 21 April 2022

 Acknowledgment 

No Acknowledgment.

 Funding

This paper is funded by Research Project coded NTCS2021-30 from Foreign Trade University, Vietnam.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Thanh NTH and Vy NT (2022). Building a proper churn prediction model for Vietnam's mobile banking service. International Journal of Advanced and Applied Sciences, 9(7): 139-149

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 

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