Predicting credit card customer churn in banks using data mining
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- Manolis Maragoudakis & Dimitrios Serpanos, 2016. "Exploiting Financial News and Social Media Opinions for Stock Market Analysis using MCMC Bayesian Inference," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 589-622, April.
- Rasha Ashraf, 2024. "Bank Customer Churn Prediction Using Machine Learning Framework," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(4), pages 1-5.
- Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
- Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
- Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
- Vera Miguéis & Dirk Poel & Ana Camanho & João Falcão e Cunha, 2012.
"Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences,"
Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 337-353, December.
- V. L. Miguéis & D. Van Den Poel & A.S. Camanho & Joao Falcao E Cunha, 2012. "Predicting Partial Customer Churn Using Markov for Discrimination for Modeling First Purchase Sequences," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/806, Ghent University, Faculty of Economics and Business Administration.
- De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
- Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
- Owen P. Hall Jr. & Darrol J. Stanley, 2012. "A comparative modelling analysis of firm performance," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 43-56.
- Seungwook Kim & Daeyoung Choi & Eunjung Lee & Wonjong Rhee, 2017. "Churn prediction of mobile and online casual games using play log data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
- Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
- Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
- De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
- Abbas Keramati & Hajar Ghaneei & Seyed Mohammad Mirmohammadi, 2016. "Developing a prediction model for customer churn from electronic banking services using data mining," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-13, December.
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Keywords
credit cards; credit card churn; data mining; churn prediction; multilayer perceptron; MLP; logistic regression; decision tree; random forest; radial basis function; RBF neural networks; support vector machine; SVM; synthetic minority oversampling technique; SMOTE; undersampling; oversampling; expert systems.;All these keywords.
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