Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk

Vulnerability refers to the degree of an individual subject to the damage arising from a catastrophic disaster. It is affected by multiple indicators that include hazard intensity, environment, and individual characteristics. The traditional area aggregate approach does not differentiate the individuals exposed to the disaster. In this article, we propose a new solution of modeling vulnerability. Our strategy is to use spatial analysis and Bayesian network (BN) to model vulnerability and make insurance pricing in a spatially explicit manner. Spatial analysis is employed to preprocess the data, for example kernel density analysis (KDA) is employed to quantify the influence of geo-features on catastrophic risk and relate such influence to spatial distance. BN provides a consistent platform to integrate a variety of indicators including those extracted by spatial analysis techniques to model uncertainty of vulnerability. Our approach can differentiate attributes of different individuals at a finer scale, integrate quantitative indicators from multiple-sources, and evaluate the vulnerability even with missing data. In the pilot study case of seismic risk, our approach obtains a spatially located result of vulnerability and makes an insurance price at a finer scale for the insured buildings. The result obtained with our method is informative for decision-makers to make a spatially located planning of buildings and allocation of resources before, during, and after the disasters.     

What ails fisheries insurance in India? An assessment of issues,challenges and future potential

This paper undertakes a critical assessment of the present state of fisheries insurance in India. Various factors that contribute to the poor performance of fisheries insurance in the country were identified and ranked based on the responses of fishermen and fish farmers in major maritime states. In the light of the findings, measures such as leveraging the potential of technological interventions, developing innovative institutional mechanisms, bridging the trust deficit amongst the stakeholders and bringing about attitudinal changes through awareness drives are suggested as way forward towards harnessing the potential of insurance as a safety net tool in fisheries sector of India.     

汶川地震后我国再保险体系在巨灾风险管理中的定位设想

汶川地震表明,我国再保险体系在巨灾保险补偿中发挥的作用十分有限,这激发我们思考这样的问题:应怎样建立有效的巨灾风险分散机制来支撑和促进再保险体系的发展。为此,提出这样的研究设想,从汶川地震为出发点,结合我国巨灾保险的实践经验,整合保险及相关行业的技术优势,从管理制度、技术和风险管理三个层面深入研究我国再保险体系在巨灾风险管理中的作用,以期得出新的建议或改善措施。(1)制度层面:对比国外和我国在地震、洪水、干旱、台风等巨灾再保险管理方面的法律规定,结合政治制度的差别,总结出具有中国特色的巨灾风险管理体系的政策依据。(2)技术层面:以地震风险为突破口,兼顾洪水、干旱、台风风险,探讨如何建立拥有自主知识产权的巨灾风险分析模型及数据库。(3)管理层面:分析巨灾保险在巨灾条件下失灵的深层次原因,论证再保险体系在巨灾风险分散机制中的重要作用,提出我国巨灾风险管理体系的基本框架。     

建筑物地震损失风险与保险费用的确定方法

本文结合我国地震危险性和建筑物抗震设防标准提出了地震损失风险评估概率模型,并给出了建筑物地震保险金额和保险费率的计算方法。用该方法可以计算各类建筑物的保险费用,计算结果与国际地震保险业的经验数据较一致。同时,本文还利用GIS技术展示了与保险金额和保险费率的空间分布状态相关的属性数据,为保险当事人提供了实施地震保险的科学依据。     

保险电子气象证明服务系统设计与实现

针对传统气象证明服务效率与现有保险业务发展需求不匹配的问题,提出一种气象异构数据同步处理的在线保险电子气象证明服务系统的设计与实现方案.通过提取保险业务的气象证明服务需求,采用Springboot框架和特定接口协议技术,将雷达回波数据、自动站气象六要素数据、闪电定位数据等多元异构气象数据进行抽取、转换、展示,形成行业气象服务客户需要的公共气象服务产品,即保险电子气象证明,并通过设计特定接口,将证明材料进行规范化输出,完成保险电子气象证明服务系统的设计与实现,对提高保险电子气象证明服务效率具有较强的促进作用.     

基于结构类型和使用功能的建筑分类及其应用

基于结构类型的建筑分类是抗震设计和地震灾害分布调查的基础,也是开展建筑结构地震易损性研究所依据的分类形式,而在社会管理中则更注重基于使用功能的建筑分类。上述2种建筑分类方法难以给出恰当的经济损失评估结果,因此在面向地震巨灾保险研究与应用时存在一些不足。首先,传统的建筑分类将建筑的使用功能与结构类型平行处理,未考虑建筑结构类型和建筑使用功能的耦合影响;其次,随着经济社会的发展,建筑结构形式和使用功能均不断地变化,需要发展适用性强大的非固定类型分类方法。本文针对地震巨灾保险业务中建筑分类这一核心问题,结合中国建筑形式和使用功能,从建筑结构分类出发,进一步考虑建筑的使用功能,形成了基于结构类型-使用功能多因素耦合的建筑分类模型,并以北京市典型区域为例,对所提出的建筑分类模型开展应用示范。同时,本文提出的建筑分类模型结合网络信息技术,可解决传统分类应用于具体目标区域时分类指标体系不具备可变性的问题,满足了地震巨灾保险的数据需求。     

Record-High Losses for Weather Disasters in the United States During the 1990s: How Excessive and Why?

During 1990–1996 the United States experienced record-setting insured property losses due to numerous weather catastrophes, each event causing $100 million or more in losses (1991 dollars). The total loss in this 7-year period, after adjustment to inflation and other factors, was $39.65 billion with $15 billion coming from one event, Hurricane Andrew. In the 1990s, 72 catastrophes occurred, half of the total number in the 40 preceding years, 1950–1989. Although the total loss and the number of catastrophes were exceptionally high in the 1990s, the average loss per event was $551 million, only slightly more than the $467 million average for catastrophes during 1950–1989. Furthermore, storm intensities in the 1990s were slightly less than those during the preceding 40 years, revealing the excess losses of the 1990s to be a result of an extremely large number of damaging storms causing losses exceeding $100 million. Examination of historical values of most weather extremes including hurricanes, floods, and tornadoes, did not show an increase during the 1990s, revealing that weather changes were not the principal cause of more catastrophes. Examination of recent demographic shifts in the U.S. reveals two changes, each based on major re-locations to higher-valued property concentrated in areas either with a high frequency of damaging storms (Gulf and East Coast), or to where even a small but intense storm can cause huge losses (urban areas and West Coast). These shifts, plus the continuing growth of population in other storm-prone areas have greatly increased society's vulnerability to storm damage. An in-depth analysis of many conditions was required to establish that the high losses and numerous catastrophes of the 1990s were largely the result of societal changes and not major weather changes. This revised version was published online in June 2006 with corrections to the Cover Date.     

Damage Potential and Losses Resulting from Snow Avalanches in Settlements of the Canton of Grisons,Switzerland

In the field of natural hazards, risk is usually expressed as a function of probability of occurrence and damage potential. A key factor in the development of avalanche risk is a change in damage potential. In this paper, the development of damage potential and losses is analysed for settlements in the canton of Grisons, Switzerland for the period between 1950 and 2000. The spatial development patterns of the residential population and the insured value of buildings on a municipal level are described. These patterns are overlain by an accentuated occurrence of avalanches that caused damage to buildings. Extensive results from areas threatened by avalanches in the municipality of Davos are presented. Although the natural avalanche activity remained constant during the last decades, the total amount of damage decreased due to mitigation measures. The example of the municipality of Davos shows that mitigation measures are able to compensate an increase of tangible assets which leads to a reduced avalanche risk compared to earlier decades.     

Work sharing in Kerala's fisheries

Earlier studies suggest that once population growth and market integration reach a certain critical level, traditional practices of work sharing tend to degenerate or disappear altogether. Work sharing has, however, survived to date in small-scale fisheries in Kerala, India. Artisanal fishermen strongly favor the system, for ethical reasons as well as for their personal benefit. Besides, boat owners appear not to be hindered by the system. Contrary to observations in crop production, work sharing has not inhibited the introduction of modern technologies like motorization.     

Paleoclimate histories improve access and sustainability in index insurance programs

Proxy-based climate reconstructions can extend instrumental records by hundreds of years, providing a wealth of climate information at high temporal resolution. To date, however, their usefulness for informing climate risk and variability in policy and social applications has been understudied. Here, we apply tree-ring based reconstructions of drought for the last 700 years in a climate index insurance framework to show that additional information from long climate reconstructions significantly improves our understanding of the underlying climate distributions and variability. We further show that this added information can be used to better characterize risk to insurance providers, in many cases providing meaningful reductions in long-term contract costs to farmers in stand-alone policies. The impact of uncertainty on insurance premiums can also be reduced when insurers diversify portfolios, and the availability of long-term climate information from tree rings across a broad geographic range provides an opportunity to characterize spatial correlation in climate risk across geographic regions. Our results are robust to the range of climate variability experienced over the last 400 years and in model simulations of the twenty-first century, even within the context of changing baselines due to low frequency variability and secular climate trends. These results demonstrate the utility of longer-term climate histories in index insurance applications. Furthermore, they make the case from a climate-variability perspective for the continued importance of such approaches to improving the instrumental climate record, even into a non-stationary climate future.