基于机器学习的车险定价因子重要性测度比较研究

A Comparative Study of Machine Learning Based Importance Measures for Car Insurance Pricing Factors

随着机器学习技术的快速发展,越来越多的保险公司开始应用机器学习方法来改进车险定价策略。车险定价因素的重要性测度对于保险公司和车主来说具有重要意义,它可以揭示不同因素对保险费的影响程度,帮助制定更准确和个性化的保险策略。本研究旨在比较不同机器学习方法在车险定价因素重要性测度方面的表现,重点关注广义线性模型(GLM)、随机森林、XGBoost等常用方法,并基于2组真实的车险数据集进行实证研究。通过实验和数据分析,我们发现不同算法模型在车险定价因素重要性测度方面存在一致性和差异性。某些因素在不同模型中的重要性测度结果一致,例如奖惩系数和厂商指导价。然而,也存在部分因素在不同模型中的重要性测度结果不一致的情况,这可能是由于模型算法和数据特征的不同所导致的。这些测度结果为保险公司提供了重要的参考,并为进一步改进车险定价模型和方法提供了指导。

With the rapid development of machine-learning technology,more and more insurers are applying machine-learning methods to improve their car insurance pricing strategies.Measuring car insurance pricing factors is of great importance to insurers and car owners,as it can reveal the extent to which different factors affect insurance premiums and help develop more accurate and personalized insurance strategies.This study aims to compare the performance of different machine learning methods in measuring the importance of car insurance pricing factors,focusing on standard methods such as generalized linear models(GLM),random forests,and XGBoost,and to conduct an empirical study based on two real car insurance datasets.Through experiments and data analysis,we find consistency and variability in the essential measures of car insurance pricing factors across different algorithmic models.Some factors have consistent importance measures across the models,such as the reward and penalty coefficients and the manufacturer's guide price.However,there were also instances where some factors were inconsistent across models,which may be due to differences in model algorithms and data characteristics.These measures provide an essential reference for insurers and guide further improvements to car insurance pricing models and methods.