{"title":"深入研究银行业的客户流失预测:超参数选择和不平衡学习的挑战","authors":"Vasileios Gkonis, Ioannis Tsakalos","doi":"10.1002/for.3194","DOIUrl":null,"url":null,"abstract":"Forecasting customer churn has long been a major issue in the banking sector because the early identification of customer exit is crucial for the sustainability of banks. However, modeling customer churn is hampered by imbalanced data between classification classes, where the churn class is typically significantly smaller than the no‐churn class. In this study, we examine the performance of deep neural networks for predicting customer churn in the banking sector, while incorporating various resampling techniques to overcome the challenges posed by imbalanced datasets. In this work we propose the utilization of the APTx activation function to enhance our model’s forecasting ability. In addition, we compare the effectiveness of different combinations of activation functions, optimizers, and resampling techniques to identify configurations that yield promising results for predicting customer churn. Our results offer dual insights, enriching the existing literature in the field of hyperparameter selection, imbalanced learning, and churn prediction, while also revealing that APTx can be a promising component in the field of neural networks.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning\",\"authors\":\"Vasileios Gkonis, Ioannis Tsakalos\",\"doi\":\"10.1002/for.3194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Forecasting customer churn has long been a major issue in the banking sector because the early identification of customer exit is crucial for the sustainability of banks. However, modeling customer churn is hampered by imbalanced data between classification classes, where the churn class is typically significantly smaller than the no‐churn class. In this study, we examine the performance of deep neural networks for predicting customer churn in the banking sector, while incorporating various resampling techniques to overcome the challenges posed by imbalanced datasets. In this work we propose the utilization of the APTx activation function to enhance our model’s forecasting ability. In addition, we compare the effectiveness of different combinations of activation functions, optimizers, and resampling techniques to identify configurations that yield promising results for predicting customer churn. Our results offer dual insights, enriching the existing literature in the field of hyperparameter selection, imbalanced learning, and churn prediction, while also revealing that APTx can be a promising component in the field of neural networks.\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1002/for.3194\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1002/for.3194","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning
Forecasting customer churn has long been a major issue in the banking sector because the early identification of customer exit is crucial for the sustainability of banks. However, modeling customer churn is hampered by imbalanced data between classification classes, where the churn class is typically significantly smaller than the no‐churn class. In this study, we examine the performance of deep neural networks for predicting customer churn in the banking sector, while incorporating various resampling techniques to overcome the challenges posed by imbalanced datasets. In this work we propose the utilization of the APTx activation function to enhance our model’s forecasting ability. In addition, we compare the effectiveness of different combinations of activation functions, optimizers, and resampling techniques to identify configurations that yield promising results for predicting customer churn. Our results offer dual insights, enriching the existing literature in the field of hyperparameter selection, imbalanced learning, and churn prediction, while also revealing that APTx can be a promising component in the field of neural networks.
期刊介绍:
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.