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分析影响风险回避的因素:韩国人寿保险数据案例
Sehyun Lim1, Taeyeon Oh2, Guy Ngayo3
1Seoul Business School, aSSIST University, 6 Ewhayeodae 2-gil, Fintower, Sinchon-ro, Seodaemun-gu, Seoul, South Korea, 03767.
Heliyon
|October 13, 2023
概括
机器学习识别了风险回避个人的关键特征,预测了未来的保险购买. 这促进了保险需求理论的发展,详细描述了那些可能购买更多保险的人的特征.
科学领域:
- 行为经济学是一种行为经济学.
- 数据科学数据科学数据科学
- 保险研究 保险研究
背景情况:
- 保险需求理论表明,增加的风险厌恶与更高的保险需求相关.
- 以前的研究通常依赖于定性方法,如问卷来评估风险厌恶.
- 在量化识别与保险环境中的风险厌恶相关的特定特征方面存在差距.
研究的目的:
- 用机器学习量化划分风险回避个体的特征.
- 开发一个预测模型来识别可能购买额外保单的潜在保险消费者.
- 通过将个体特征与保险收购行为联系起来,来测试保险需求理论.
主要方法:
- 机器学习分析应用于94,306名有保险索赔的个人数据集.
- 使用19个人口和社会经济因素开发预测模型.
- 确定影响额外保险收购的重要独立变量.
主要成果:
- 该研究确定了10个关键因素,这些因素显著影响了额外的保险购买.
- 这些因素包括农村居住地,女性性别,年龄较大,资产较高,蓝领职业,教育较低,婚姻状况 (已婚/离婚/分居),癌症病史和现有政策细节.
- 成功构建了一个预测模型,预测可能购买补充保险的消费者.
结论:
- 机器学习为风险回避特征提供了具体的定量洞察力,克服了先前定性研究的局限性.
- 韩国保险公司可以利用这些发现进行有针对性的营销和客户保留策略.
- 结果为理解商业,心理学,社会学和营销领域的风险偏好和行为提供了跨学科的价值.
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