基于机器学习的医疗保险费率制定方法

IF 2.4 4区 管理学 Q3 BUSINESS
Amal Ben Hamida, Manel Kacem, Christian de Peretti, Lotfi Belkacem
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引用次数: 0

摘要

在保险业,根据被保险人的风险状况提出准确的保费,可以让公司更好地管理其投资组合,提高竞争力。最近,机器学习方法已被用于保险费率制定方面的各种改进,尤其是在汽车行业。这些模型专门用于挖掘潜在的数据信息,并利用解释变量建立相关变量的预测模型。在本文中,我们旨在利用机器学习算法为个人医疗保险合同的费率制定提供一种定价方法,并将其应用于突尼斯的医疗保险组合。我们从简单的分类树和回归树入手,然后采用随机森林、极端梯度提升、支持向量回归和人工神经网络回归模型等更先进的方法。我们将这些非参数方法的预测性能与标准的广义线性模型进行了比较。我们的研究结果表明,机器学习适用于医疗保险市场,XGBoost 算法的预测能力优于经典的广义线性模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning based methods for ratemaking health care insurance
In insurance, proposing an accurate premium that is adjusted to the insured risk profile allows companies to better manage their portfolios and to be more competitive. Machine learning methods have recently been adopted for various improvements in insurance ratemaking, especially in the automobile industry. These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using explanatory variables. In this paper, we aim to provide a pricing method for ratemaking individual healthcare insurance contracts using machine learning algorithms that are applied to a Tunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced methods that are Random Forest, Extreme gradient boosting, Support Vector Regression, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear model. Our results showed the applicability of machine learning in the healthcare insurance market and that the XGBoost algorithm outperforms the predictive capacity of the classical generalized linear model.
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来源期刊
CiteScore
6.00
自引率
6.70%
发文量
38
期刊介绍: The International Journal of Market Research is the essential professional aid for users and providers of market research. IJMR will help you to: KEEP abreast of cutting-edge developments APPLY new research approaches to your business UNDERSTAND new tools and techniques LEARN from the world’s leading research thinkers STAY at the forefront of your profession
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