Insurance risk assessment in the face of climate change: Integrating data science and statistics
Insurance risk assessment in the face of climate change: Integrating data science and statistics
复制标题
DOI：
10.1002/wics.1462
复制
发表时间：
2019-04
期刊：
影响因子：
--
通讯作者：
V. Lyubchich;N. Newlands;A. Ghahari;Tahir Mahdi;Y. Gel
中科院分区：
文献类型：
--
作者：
V. Lyubchich;N. Newlands;A. Ghahari;Tahir Mahdi;Y. Gel
局部极端天气事件造成的保险损失总体上高于大型自然灾害。证据是通过对天气和保险记录的长期观察提供的，这些记录也是大多数涵盖天气相关损失的保险产品的基础。然而，世界各地的保险公司担心，过去用于评估和定价风险的记录低估了风险，并在近年来发生了损失。日益增长的保险风险主要归因于气候变化，气候变化给人类生活和福利的各个方面带来越来越多的变化和永久性影响。从洪水到冰雹再到强风，不利的大气事件深刻地提醒我们，我们的社会在极端环境造成的广泛威胁中是多么脆弱。事实上，随着气候变化的影响变得更加明显，我们面临着一个新的风险时代，与天气有关的损害和损失越来越多。这反过来，再加上大量气候数据的挑战，需要开发创新的分析方法，超越传统的学科界限的统计，精算和环境科学。然而，气候风险评估的多学科性质及其对保险的影响往往被忽视和忽视。我们强调了最新的发展和跨学科的观点，对不同的统计和机器学习方法进行建模和评估农业和家庭保险中的气候风险，特别关注非灾难性事件。
Local extreme weather events cause more insurance losses overall than large natural disasters. The evidence is provided by long‐term observations of weather and insurance records that are also a foundation for the majority of insurance products covering weather related damages. The insurers around the world are concerned, however, that the past records used to assess and price the risks underestimate the risk and incurred losses in recent years. The growing insurance risks are largely attributed to climate change that brings increasingly more alterations and permanent impact on all aspects of human life and welfare. From floods to hail to excessive wind, adverse atmospheric events are a poignant reminder of how vulnerable our society is across a broad range of threats posed by environmental extremes. Indeed, as climate change effects become more pronounced, we face a new era of risk with increasing weather related damages and losses. This in turn, coupled with challenges of massive climatic data, requires developing innovative analytic approaches that transcend traditional disciplinary boundaries of statistical, actuarial and environmental sciences. Nevertheless, the multidisciplinary nature of climate risk assessment and its impact on insurance is often overlooked and neglected. We highlight the most recent developments and interdisciplinary perspectives on diverse statistical and machine learning methodology for modeling and assessing climate risk in agricultural and home insurances, with a particular focus on noncatastrophic events.