A spatial machine learning model for analysing customers’ lapse behaviour in life insurance

IF 1.5 Q3 BUSINESS, FINANCE
Sen Hu, A. O'Hagan, James Sweeney, Mohammadhossein Ghahramani
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引用次数: 12

Abstract

Abstract Spatial analysis ranges from simple univariate descriptive statistics to complex multivariate analyses and is typically used to investigate spatial patterns or to identify spatially linked consumer behaviours in insurance. This paper investigates if the incorporation of publicly available spatially linked demographic census data at population level is useful in modelling customers’ lapse behaviour (i.e. stopping payment of premiums) in life insurance policies, based on data provided by an insurance company in Ireland. From the insurance company’s perspective, identifying and assessing such lapsing risks in advance permit engagement to prevent such incidents, saving money by re-evaluating customer acquisition channels and improving capital reserve calculation and preparation. Incorporating spatial analysis in lapse modelling is expected to improve lapse prediction. Therefore, a hybrid approach to lapse prediction is proposed – spatial clustering using census data is used to reveal the underlying spatial structure of customers of the Irish life insurer, in conjunction with traditional statistical models for lapse prediction based on the company data. The primary contribution of this work is to consider the spatial characteristics of customers for life insurance lapse behaviour, via the integration of reliable government provided census demographics, which has not been considered previously in actuarial literature. Company decision-makers can use the insights gleaned from this analysis to identify customer subsets to target with personalized promotions to reduce lapse rates, and to reduce overall company risk.
用于分析寿险客户失效行为的空间机器学习模型
摘要空间分析从简单的单变量描述性统计到复杂的多变量分析,通常用于调查空间模式或识别保险中空间关联的消费者行为。本文根据爱尔兰一家保险公司提供的数据,调查了在人口层面纳入公开的空间关联人口普查数据是否有助于在人寿保险单中模拟客户的失误行为(即停止支付保费)。从保险公司的角度来看,提前识别和评估此类失误风险可以防止此类事件的发生,通过重新评估客户获取渠道和改进资本准备金的计算和准备来节省资金。将空间分析纳入时差建模有望改善时差预测。因此,提出了一种失效预测的混合方法——使用人口普查数据的空间聚类来揭示爱尔兰人寿保险公司客户的潜在空间结构,并结合基于公司数据的失效预测的传统统计模型。这项工作的主要贡献是通过整合可靠的政府提供的人口普查人口统计数据,考虑人寿保险失效行为的客户空间特征,这在精算文献中以前没有考虑过。公司决策者可以利用从该分析中收集的见解来确定个性化促销的目标客户子集,以降低失误率,并降低公司的整体风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
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