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

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。为了监控风险并支持投资决策，他们使用数学模型来设定向客户收取的保费，以便在发生重大罕见事件时，他们的公司发现自己陷入财务困境的风险很小。虽然这些模型是提高保险公司风险概况透明度的重要工具，但它们的开发成本很高，而且由于缺乏严格和可信的模型验证过程，它们对决策的价值被削弱了。保险部门面临着越来越多的监管，这些监管要求它们以这样一种方式测试其资本模型，即充分捕捉不确定性，并制定评估风险和减轻风险的计划。金融模型的构建和测试对保险公司来说成本很高，而且是一项时间密集的活动，这与日常的高工作量相冲突。该项目旨在将学术界开发并由NERC项目资助的方法和工具（即全球敏感性分析和SAFE软件工具箱）转移到保险业，并以这种方式对其进行调整，以促进保险业的吸收。这将为他们提供工具，以更好地捕获嵌入模型中的风险和不确定性，并使用更结构化的方法来验证他们的模型。因此，这将增加他们财务决策的稳健性。在我的奖学金项目的第一阶段，我将以与（再）保险公司XL Catlin的现有合作为基础，审查数字模型，包括巨灾和资本模型，是如何在他们的公司内开发、验证和使用的。我还将开发试点应用程序，这是这些工具的定制版本，以及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联盟合作（包括在其开放获取平台中提供一个外管局的定制版本，作为严格模型验证的标准方法）以及为更广泛的（再）保险业举办研讨会来实现。增加对保险建模过程中不确定性的理解和考虑，只能促进更持续和有意识地使用模型预测来支持财务决策。这将受到由于可能的模型缺陷而导致的风险和脆弱性的充分量化的驱动，从而导致更明智和更健壮的业务决策。这反过来将加强英国在该地区的领先地位。最终，这将提高保险公司风险状况的透明度，有助于减少和管理财务风险，降低资本要求并稳定收益。

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.