Improving the understanding and consideration of uncertainty in the (re)insurance industry

摘要保险公司提供针对各种威胁的保险，如自然灾害、核事故和恐怖主义。为了监控风险和支持投资决策，数学模型被用来设定他们向客户收取的保费，以便在发生重大罕见事件时，他们的公司陷入财务困境的风险微乎其微。虽然这些模型是提高保险公司风险状况透明度的重要工具，但它们的开发成本高昂，其决策价值因缺乏严格和可信的模型验证过程而受到损害。保险业面临越来越多的监管，这要求它们测试资本模型，以充分捕捉不确定性，并制定评估风险及其缓解的计划。建立和测试金融模型对保险公司来说是一项高成本的工作，是一项耗时的活动，与高日常工作量相冲突。本项目旨在将学术界开发并由NERC项目资助的方法和工具(即全球敏感度分析和SAFE软件工具箱)转移到保险行业，并以这种方式进行定制，以促进其在保险行业的应用。这将为他们配备工具，以更好地捕获模型中嵌入的风险和不确定性，并使用更结构化的方法来验证他们的模型。因此，这将增加他们的财务决策的稳健性。在我的奖学金项目的第一阶段，我将在与(再)保险公司XL Catlin的现有合作的基础上，审查如何在他们的公司内开发、验证和使用数值模型，包括灾难模型和资本模型。我还将开发试点应用程序、这些工具的定制版本和如何使用它们的详细指南，这将成为在更广泛的(再)保险行业传播最佳实践的基础。这将通过与OASIS财团合作(包括在其开放获取平台中作为严格模型验证的标准方法的SAFE的定制版本)和为更广泛的(再)保险业举办研讨会来实现。对保险建模过程中的不确定性的更多理解和考虑只会促进更连续和更有意识地使用模型预测来支持金融决策。这将由对可能的模型缺陷导致的风险和脆弱性进行充分量化来推动，从而导致更知情和更稳健的业务决策。这反过来将加强英国在该地区的领先地位。最终，这将增加保险公司风险状况的透明度，有助于降低和管理财务风险，降低资本要求和稳定收益。

SummaryInsurance companies provide insurance against a wide range of threats, such as natural catastrophes, nuclear incidents and terrorism. To monitor risk and support investment decisions, mathematical models are used to set the premiums which they charge to their clients such that there is little risk of their company finding itself in financial trouble, should large rare events occur. While these models are essential tools for improving the transparency of an insurer's risk profile, their development is costly and their value for decision-making is undermined by a lack of rigorous and trusted processes for model validation. The insurance sector faces increasing regulation which requires them to test their capital models in such a way that uncertainties are adequately captured and that plans are in place to assess the risks and their mitigation. The building and testing of financial models constitutes a high cost for insurance companies, and is a time intensive activity, which conflicts with the high day-to-day workload.This project aims to transfer methods and tools (i.e. global sensitivity analysis and the SAFE software toolbox) developed in academia and funded by NERC projects to the insurance industry and to tailor them in such a way to facilitate their uptake in the insurance industry. This will equip them with tools to better capture the risks and the uncertainties embedded in their models, with more structured approaches to validate their models. Therefore, this will increase the robustness of their financial decisions. In the first stage of my fellowship project, I will build on the existing collaboration with the (re)insurance company XL Catlin to review how numerical models, both catastrophe and capital models, are developed, validated and used within their company. I will also develop pilot applications, a tailored version of these tools and detailed guidelines on how to use them, which will form the basis for disseminating best practices across the wider (re)insurance industry. This will be achieved both by collaborating with the OASIS consortium - including a tailored version of SAFE in their open access platform as the standard methodology for rigorous model validation - and by holding workshops for the wider (re)insurance industry.An increased understanding and consideration of uncertainty in the insurance modelling process can only promote a more continuous and aware use of model predictions to support financial decision-making. This will be driven by the adequate quantification of risks and vulnerability due to possible models flaws, therefore leading to better-informed and more robust business decisions. This in turn will strengthen the leading position of the UK in the area. Ultimately this will increase the transparency of an insurer's risk profile, contribute to the reduction and management of financial risk, reduce capital requirements and stabilise earning.