论文标题
通过统计和机器学习方法对人寿保险政策进行搅拌建模 - 对重要特征的分析
Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important features
论文作者
论文摘要
终身保证公司通常拥有大量涵盖多个系统和数据库的数据。这些数据通常用于分析过去和描述现在。考虑到过去，未来主要是通过传统统计方法预测的。到目前为止，只有几次尝试通过机器学习方法进行估算。在这项工作中，通过各种分类方法的帮助，两部分股票中客户的单个合同取消行为是建模的。考虑了私人退休金和捐赠政策的部分股票。我们描述用于建模的数据，结构化以及以何种方式清洁它们。使用的模型是根据广泛的调整过程进行校准的，然后对其拟合优度进行了图形评估，并在可变相关性概念的帮助下进行了图形评估，我们研究哪些特征特别影响单个合同取消行为。
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.
