文献类型：期刊文献

中文题名：基于定量风险评估的建筑火灾保险费率

英文题名：Building fire insurance premium rate based on quantitative risk assessment

作者：胡俊[1,2,3];疏学明[3];解学才[3];颜峻[4];张雷[3]

机构：[1]北京师范大学国家安全与应急管理学院,珠海519087;[2]北京师范大学应急管理部-教育部减灾与应急管理研究院,北京100875;[3]清华大学工程物理系,公共安全研究院,北京100084;[4]中国劳动关系学院安全工程学院,北京100048

年份：2023

卷号：63

期号：5

起止页码：775

中文期刊名：清华大学学报:自然科学版

外文期刊名：Journal of Tsinghua University(Science and Technology)

收录：CSTPCD、、EI(收录号：20231814037732)、SCOPUS、CSCD2023_2024、BDHX、CSCD、BDHX2020

基金：国家“十三五”重点研发计划(2020YFC0833402);应急管理部消防救援局科技计划项目(2020XFZD17)。

语种：中文

中文关键词：火灾保险;风险评估;费率计算;Bayes网络;拉丁超立方采样

外文关键词：fire insurance;risk assessment;premium rate;Bayesian network;Latin hypercube sampling

中文摘要：火灾保险是有效应对火灾风险的重要手段,根据建筑的火灾风险准确厘定建筑的火灾保险费率是保险行业关心的问题。当前的建筑火灾保险费率主要基于保险市场的统计数据得到火灾的发生频率和期望损失,然后再根据建筑的风险评估结果进行浮动调整。这种方法难以精确反映建筑的火灾风险,得到的保险费率也较为粗略。该文构建了建筑火灾费率的定量厘定模型,采用Bayes网络的方法计算建筑起火概率,并采用拉丁超立方采样的方法,对建筑火灾不同阶段的烧损率进行了分层采样,得到火灾损失的复合概率分布估计,从而对具体建筑的火灾风险进行定量评估,再根据风险评估的结果对保险费率进行了厘定。选取了15户家庭,根据风险评估的结果厘定了保险费率,并将结果与保险市场火灾纯保险费率进行了比较。结果表明,该模型更能反映火灾风险的差异化水平,得到的保险费率更具有公平性。

外文摘要：Fire is a serious threat to public life and property safety.Insurance is an effective means to deal with fire risk,and accurately determining the premium rate of buildings according to the fire risk is a concern of the insurance industry.Currently,the premium rate is mainly based on the fire frequency and loss expectation from the insurance statistics,and adjustments are based on building risk assessment results.The adjustment scheme can be divided into two types.One is the rate floating model,which gives the floating range of the premium rate based on the risk level,but the floating proportion is fairly subjective.The other is the rate calculation model,which establishes the quantitative risk assessment method to calculate the specific premium rate.However,comprehensively reflecting the hazardous in the buildings as well as the uncertainty of losses with the current risk assessment method is difficult.Thus,the premium rate is relatively rough.A quantitative model for building fire insurance premium rates is constructed in this paper.First,the Bayesian network method is used to calculate the building fire probability considering the influences of various risk sources.The specific factors affecting ignition were comprehensively analyzed from the aspects of humans,things,and environments.Therefore,14 factors were selected to construct the Bayesian network of building ignition,based on which the probability of building fire can be calculated rather quantitatively and objectively.Second,the Latin hypercube sampling(LHS)is used to stratify the burn rate in different fire stages from ignition,growth,and development to spread with certain distributions to reflect the staging and random characteristics of fire losses.Thus,the final loss distribution,including the expected value,standard deviation,probability density function,and cumulative probability density function,can be acquired accurately.Therefore,the quantitative and dynamic risk assessment of building fire is realized,and the rate calculation model is used to compute the rate based on the result.Fifteen households were selected to calculate their premium rates based on the quantitative assessment of building fire risk,including ignition probability and loss distribution,and the premium rates are compared with the rate in the insurance market.Results show that the proposed premium rate determination model can effectively reflect the differentiated level of fire risk and ensure the fairness of insurance.The premise of the building fire insurance premium rate model in this paper is that the insurance company covers all the fire risks of the building and disregards the case of deductible due to the retainment of fire risk by the insured.In addition,the foreign statistics were adopted,and the normal loss distribution at each stage after the ignition was assumed due to the lack of domestic data.Deductibles can be considered in further research to construct premium rate models,and accurate data can be acquired to obtain results consistent with the building fire risk level in China.