双参数Tweedie机器学习模型及其精算应用
Double Tweedie Machine Learning Models and Actuarial Applications
高雅倩 1孟生旺2
作者信息
- 1. 中国人民大学统计学院
- 2. 中国人民大学应用统计科学研究中心;中国人民大学统计学院
- 折叠
摘要
Tweedie回归是保险损失预测和风险定价的主要工具之一.为充分利用大数据、物联网、机器学习等技术促进保险业的数字化转型,实现更加精准的风险识别和风险定价,本文将传统的Tweedie广义线性模型推广到双参数形式,并结合机器学习算法,提出双参数Tweedie梯度提升树模型和双参数Tweedie组合神经网络模型.基于我国一家保险公司的车联网大数据,提取了新的驾驶行为风险因子.通过实证研究检验了双参数Tweedie梯度提升树和双参数Tweedie组合神经网络在风险识别以及风险定价中的有效性,为促进我国保险业数字化转型提供了一种新的模型和方法.
Abstract
Tweedie regression is one of the most widely used models for loss prediction and risk pricing in the insurance industry.In order to make full use of big data,Internet of Things,machine learning,and other technologies to promote the digital transformation of the insurance industry and achieve more accurate risk identification and risk pricing.This parper extend the traditional Tweedie generalized linear model to the double-parameter form.Combined with machine learning algorithm,the double Tweedie gradient boosting tree and the double Tweedie combined neural network model are proposed.In addition,we get the telematics data from a Chinese insurance company and extract new driving behavior factors for risk pricing.The empirical study shows that using the new driving behavior factors,the double Tweedie gradient boosting tree and the double Tweedie combined neural network model can effectively improve the risk identification and risk pricing.The new models may be used to promote digital transformation of the insurance industry.
关键词
Tweedie回归/双参数梯度提升树/双参数组合神经网络/驾驶行为因子Key words
Tweedie Regression/Double Gradient Boosting Tree/Double Combined Neural Network/Driving Behavior Factor引用本文复制引用
基金项目
国家社会科学基金重点项目(22ATJ005)
教育部人文社会科学重点研究基地重大项目(22JJD910003)
出版年
2024
NETL
NSTL
万方数据