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

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-09-05 DOI:10.1002/for.3194
Vasileios Gkonis, Ioannis Tsakalos
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引用次数: 0

摘要

长期以来,客户流失预测一直是银行业的一个重要问题,因为及早识别客户流失对银行的可持续发展至关重要。然而,客户流失建模受到分类类别之间不平衡数据的阻碍,其中流失类别通常明显小于无流失类别。在本研究中,我们检验了深度神经网络在预测银行业客户流失方面的性能,同时结合了各种重采样技术,以克服不平衡数据集带来的挑战。在这项工作中,我们建议使用 APTx 激活函数来增强模型的预测能力。此外,我们还比较了激活函数、优化器和重采样技术的不同组合的有效性,以确定在预测客户流失方面能产生良好结果的配置。我们的研究结果提供了双重见解,丰富了超参数选择、不平衡学习和客户流失预测领域的现有文献,同时也揭示了 APTx 可以成为神经网络领域的一个有前途的组件。
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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.
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
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.
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