Machine Learning to Develop Credit Card Customer Churn Prediction
The credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
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| Online Access: | https://www.mdpi.com/0718-1876/17/4/77 |
| _version_ | 1797456797844373504 |
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| author | Dana AL-Najjar Nadia Al-Rousan Hazem AL-Najjar |
| author_facet | Dana AL-Najjar Nadia Al-Rousan Hazem AL-Najjar |
| author_sort | Dana AL-Najjar |
| collection | DOAJ |
| description | The credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. To select the independent variables, three models were used, including selection of all independent variables, two-step clustering and k-nearest neighbor, and feature selection. In addition, five machine learning prediction models were selected, including the Bayesian network, the C5 tree, the chi-square automatic interaction detection (CHAID) tree, the classification and regression (CR) tree, and a neural network. The analysis showed that all the machine learning models could predict the credit card customer churn model. In addition, the results showed that the C5 tree machine learning model performed the best in comparison with the three developed models. The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the credit card, and the change in the transaction count. Finally, the results revealed that merging the multi-categorical variables into one variable improved the performance of the prediction models. |
| first_indexed | 2024-03-09T16:13:01Z |
| format | Article |
| id | doaj.art-8cf8ed298b4643e183dc6b82cd664c46 |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-03-09T16:13:01Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-8cf8ed298b4643e183dc6b82cd664c462023-11-24T16:04:26ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762022-11-011741529154210.3390/jtaer17040077Machine Learning to Develop Credit Card Customer Churn PredictionDana AL-Najjar0Nadia Al-Rousan1Hazem AL-Najjar2Department of Finance and Banking Sciences, Faculty of Business, Applied Science Private University, Amman 11931, JordanMIS Department, Faculty of Business, Sohar University, Sohar 311, OmanDepartment of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, 34310 Istanbul, TurkeyThe credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. To select the independent variables, three models were used, including selection of all independent variables, two-step clustering and k-nearest neighbor, and feature selection. In addition, five machine learning prediction models were selected, including the Bayesian network, the C5 tree, the chi-square automatic interaction detection (CHAID) tree, the classification and regression (CR) tree, and a neural network. The analysis showed that all the machine learning models could predict the credit card customer churn model. In addition, the results showed that the C5 tree machine learning model performed the best in comparison with the three developed models. The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the credit card, and the change in the transaction count. Finally, the results revealed that merging the multi-categorical variables into one variable improved the performance of the prediction models.https://www.mdpi.com/0718-1876/17/4/77customer churnmachine learningfeature selectiontwo-step clusteringprediction model |
| spellingShingle | Dana AL-Najjar Nadia Al-Rousan Hazem AL-Najjar Machine Learning to Develop Credit Card Customer Churn Prediction Journal of Theoretical and Applied Electronic Commerce Research customer churn machine learning feature selection two-step clustering prediction model |
| title | Machine Learning to Develop Credit Card Customer Churn Prediction |
| title_full | Machine Learning to Develop Credit Card Customer Churn Prediction |
| title_fullStr | Machine Learning to Develop Credit Card Customer Churn Prediction |
| title_full_unstemmed | Machine Learning to Develop Credit Card Customer Churn Prediction |
| title_short | Machine Learning to Develop Credit Card Customer Churn Prediction |
| title_sort | machine learning to develop credit card customer churn prediction |
| topic | customer churn machine learning feature selection two-step clustering prediction model |
| url | https://www.mdpi.com/0718-1876/17/4/77 |
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