Customer Churn Prediction on Credit Card Services using Random Forest Method

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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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  -