信用卡损耗:机器学习和深度学习技术概述

Sihao Wang, Bolin Chen
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引用次数: 0

摘要

信用卡客户流失(即客户关闭信用卡账户)是银行和其他金融机构面临的一个主要问题。如果能准确预测客户流失率,公司就能采取积极措施留住有价值的客户。在本综述中,我们将探讨如何将机器学习和深度学习技术应用于预测信用卡流失率。我们首先介绍了信用卡流失的背景,并解释了这一重要问题的原因。接下来,我们讨论了用于预测客户流失的常见机器学习算法,包括逻辑回归、随机森林和梯度提升树。然后,我们将解释神经网络和序列模型等深度学习方法如何从客户数据中捕捉更复杂的模式。我们还详细介绍了流失模型的可用输入特征。我们根据以往的研究比较了不同建模技术的性能。最后,我们讨论了使用机器学习和深度学习进行预测性客户流失建模所面临的挑战和未来发展方向。我们的综述对该领域的关键研究进行了总结,并强调了推动最新技术发展的机遇。更稳健的客户流失预测可以帮助企业采取有针对性的行动来提高客户保留率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Credit card attrition: an overview of machine learning and deep learning techniques
Credit card churn, where customers close their credit card accounts, is a major problem for banks and other financial institutions. Being able to accurately predict churn can allow companies to take proactive steps to retain valuable customers. In this review, we examine how machine learning and deep learning techniques can be applied to forecast credit card churn. We first provide background on credit card churn and explain why it is an important problem. Next, we discuss common machine learning algorithms that have been used for churn forecasting, including logistic regression, random forests, and gradient boosted trees. We then explain how deep learning methods like neural networks and sequence models can capture more complex patterns from customer data. The available input features for churn models are also reviewed in detail. We compare the performance of different modeling techniques based on past research. Finally, we discuss open challenges and future directions for predictive churn modeling using machine learning and deep learning. Our review synthesizes key research in this domain and highlights opportunities for advancing the state-of-the-art. More robust churn forecasting can enable companies to take targeted action to improve customer retention.
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