保险中的失误风险建模:一种贝叶斯混合方法

IF 1.5 Q3 BUSINESS, FINANCE
Viviana G. R. Lobo, Thaís C. O. Fonseca, M. Alves
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引用次数: 0

摘要

本文主要研究人寿保险背景下投保人的退保时间模型。在这种设置中,通常在合同的前几个月观察到较大的失效率,几个月后该失效率会下降。取消时间的建模必须考虑到这种特定的行为。另一个程式化的事实是,在研究期间没有取消的政策被视为审查。为了考虑审查率和异质失效率,这项工作假设了一个混合回归的贝叶斯生存模型。该推断基于数据扩充,即使对于数百万客户端的数据集也可以进行快速计算。此外,还提出了基于期望-最大化算法的频率点估计。一个示例模拟了人寿保险合同的典型行为,并对所提出的模型的性质进行了模拟研究。通过一个案例研究,说明了我们提出的模型的灵活性,允许不同规格的混合物成分。特别是,在保险背景下观察到的审查可能高达数据的50%,这对于流行病学等其他领域的生存模型来说是非常不寻常的。在我们的模拟研究中利用了这一方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lapse risk modeling in insurance: a Bayesian mixture approach
This paper focuses on modeling surrender time for policyholders in the context of life insurance. In this setup, a large lapse rate at the first months of a contract is often observed, with a decrease in this rate after some months. The modeling of the time to cancelation must account for this specific behavior. Another stylized fact is that policies which are not canceled in the study period are considered censored. To account for both censoring and heterogeneous lapse rates, this work assumes a Bayesian survival model with a mixture of regressions. The inference is based on data augmentation allowing for fast computations even for datasets of over millions of clients. Moreover, frequentist point estimation based on Expectation–Maximization algorithm is also presented. An illustrative example emulates a typical behavior for life insurance contracts, and a simulated study investigates the properties of the proposed model. A case study is considered and illustrates the flexibility of our proposed model allowing different specifications of mixture components. In particular, the observed censoring in the insurance context might be up to $50\%$ of the data, which is very unusual for survival models in other fields such as epidemiology. This aspect is exploited in our simulated study.
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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
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