22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Machine Learning to Develop Credit Card Customer Churn Prediction

      , ,
      Journal of Theoretical and Applied Electronic Commerce Research
      MDPI AG

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: not found
          • Article: not found

          Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea.

            Coronavirus COVID-19 further transmitted to several countries globally. The status of the infected cases can be determined basing on the treatment process along with several other factors. This research aims to build a classifier prediction model to predict the status of recovered and death coronavirus CovID-19 patients in South Korea.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

              Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.
                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Journal of Theoretical and Applied Electronic Commerce Research
                JTAER
                MDPI AG
                0718-1876
                December 2022
                November 16 2022
                : 17
                : 4
                : 1529-1542
                Article
                10.3390/jtaer17040077
                3e0495b1-7213-4d5c-940b-d205a127339d
                © 2022

                https://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article