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

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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.