Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking
Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking
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DOI：
10.1007/978-3-319-93034-3_30
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发表时间：
2018-06
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影响因子：
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通讯作者：
Yuzhou Chen;Y. Gel;V. Lyubchich;Todd Winship
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作者：
Yuzhou Chen;Y. Gel;V. Lyubchich;Todd Winship
现代客户分析为零售商提供了各种前所未有的机会，通过跟踪个人客户及其同行并研究客户行为模式来增强客户智能解决方案。虽然电信提供商多年来一直积极利用同行网络数据来改进其客户分析，但对零售银行业务中的同行效应的了解仍然非常有限。我们引入现代深度学习概念来量化社交网络变量对银行客户流失的影响。此外，我们提出了一种新的深度集成分类器，通过使用卷积神经网络有效地堆叠多个预测，系统地集成了元级模型中各个分类器的预测能力。我们评估我们的方法在应用程序中的客户保留在加拿大的零售金融机构。
Modern customer analytics offers retailers a variety of unprecedented opportunities to enhance customer intelligence solutions by tracking individual clients and their peers and studying clientele behavioral patterns. While telecommunication providers have been actively utilizing peer network data to improve their customer analytics for a number of years, there yet exists a very limited knowledge on the peer effects in retail banking. We introduce modern deep learning concepts to quantify the impact of social network variables on bank customer attrition. Furthermore, we propose a novel deep ensemble classifier that systematically integrates predictive capabilities of individual classifiers in a meta-level model, by efficiently stacking multiple predictions using convolutional neural networks. We evaluate our methodology in application to customer retention in a retail financial institution in Canada.