Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)

Customer Churn Prediction on Credit Card Services using Random Forest Method

Authors
Xinyu Miao1, *, , Haoran Wang2,
1Beijing University of Technology, Beijing, 100000, China.
2Hefei University of Technology, Hefei, 230000, China.

These authors contributed equally.

Corresponding Author
Xinyu Miao
Available Online 26 March 2022.
DOI
10.2991/aebmr.k.220307.104How to use a DOI?
Keywords
credit card; customer churn; random forest; machine learning
Abstract

With the continuous development of the Internet, more and more people are spending money using credit cards online, therefore, retaining customers in order to maintain profit margin becomes very important for many banks. This paper aims to make predictions on credit card customer churn through machine learning methods and to provide feasible solutions to deal with customer churn issue based on the results. Three models including Random Forest, Linear Regression and K-Nearest Neighbor (KNN) are applied to a dataset which contains more than 10000 pieces and 21 features. By tuning hyperparameters and evaluating models based on ROC & AUC and confusion matrix, it is concluded that Random Forest has the best performance with its accuracy reaching 96.25%. Total transaction amount in the last 12 months, total transaction count in the last 12 months and total revolving balance are the top three important features which have the significant impacts on the customer churn prediction. It shows that the more frequent customers use their credit cards, the less likely they are to leave, and by using this model, bank managers can proactively take actions to fight against customer churn.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
26 March 2022
ISBN
978-94-6239-554-1
ISSN
2352-5428
DOI
10.2991/aebmr.k.220307.104How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xinyu Miao
AU  - Haoran Wang
PY  - 2022
DA  - 2022/03/26
TI  - Customer Churn Prediction on Credit Card Services using Random Forest Method
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 649
EP  - 656
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.104
DO  - 10.2991/aebmr.k.220307.104
ID  - Miao2022
ER  -