基于统计和机器学习方法的寿险保单客户流失建模--重要特征分析
Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important features
摘要：
人寿保险公司通常拥有涵盖多个系统和数据库的大量数据。这些数据通常用于分析过去和描述现在。考虑到过去，对未来的预测大多是通过传统的统计方法。到目前为止，仅进行了一些尝试来通过机器学习方法进行估计。在这项工作中，在两个部分股票的客户的个人合同取消行为建模的各种分类方法的援助。部分股票的私人养老金和养老金政策被认为是。我们描述了用于建模的数据，它们的结构以及它们的清洗方式。所使用的模型进行校准的基础上，广泛的调整过程，然后以图形方式评估其拟合优度和一个变量的相关性概念的帮助下，我们调查哪些功能显着影响个人合同取消行为。
英文摘要：
Life assurance companies typically possess a wealth of data covering multiple systems and databases. These data are often used for analyzing the past and for describing the present. Taking account of the past, the future is mostly forecasted by traditional statistical methods. So far, only a few attempts were undertaken to perform estimations by means of machine learning approaches. In this work, the individual contract cancellation behavior of customers within two partial stocks is modeled by the aid of various classification methods. Partial stocks of private pension and endowment policy are considered. We describe the data used for the modeling, their structured and in which way they are cleansed. The utilized models are calibrated on the basis of an extensive tuning process, then graphically evaluated regarding their goodness-of-fit and with the help of a variable relevance concept, we investigate which features notably affect the individual contract cancellation behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
