Climate-related disasters have occurred more frequently in recent years. Such losses mean that over the past several years, there have been massive increases in property insurance prices. But cost rises cannot continue unabated, or insurance will become unaffordable. Following the Task Force on Climate-Related Financial Disclosure (TFCD) Guidelines, it is clear the materiality that the climate risk (i.e., transition risk and physical risk) can manifest on the financial statements of private organizations, in addition to the opportunities that it can introduce. (i.e., resources efficiency and market resilience). It, therefore, becomes essential for organizations to equip themselves with an analytical tool of scenario analysis, highly performing and adaptive to the multiple interrogative needs, which can guide the strategic and financial planning, allowing them to foresee how the different possible climate futures will affect their businesses. As challenging and daunting as this task might sound, it is a critical component of understanding and preparing for the impact of climate change. A scenario describes a path of development leading to a particular outcome. Scenarios are not intended to represent a full description of the future, but rather to highlight central elements of a possible future and to draw attention to the key factors that will drive future developments.
Goals and Objectives
Competitive insurance companies will increasingly turn to Big Data–type modeling solutions capable of predicting risks that take weather conditions into consideration. Insurers need to equip themselves with scenario-based weather and climate risk simulation engines to explore the impact of climate change (physical and transition risk) for strategic decision making, integrating a rich portfolio of risk analytics techniques (VaR and excess climate risk). These tools will provide visual-driven configuration paths that might simplify and foster the climate and market hypotheses setting and the study of how those prior ideas will ultimately turn into financial results. The solution shall combine business rules as well as stochastic projections and integrate with third-party data to provide the latest global thinking on climate change effects on financial markets.
Forecasting and scenario analysis technology
High-performing simulation engine
Geospatial and IoT technology
Use Case Summary
Many companies are looking at risk mitigation and adaptation, for their risk assessment processes, including climate change scenarios such as the risk to infrastructures, sites, supply chains, etc. related to several environmental and social aspects (i.e., natural disasters, mass migrations, health and safety, reputation issues, etc.). As a result, insurance companies will have to adapt their processes to incorporate this scenario planning into their traditional assessments. The ability to incorporate AI and ML int the risk analysis function will provide a competitive differentiation, especially if those models incorporate third-party scenario planning on them. Given the importance of forward-looking assessments of climate-related risk, the TCFD believes that scenario analysis is an important and useful tool for an organization to use both for assessing potential business implications of climate-related risks and opportunities and for informing stakeholders about how the organization is positioning itself in light of these risks and opportunities. The data and quantification aspect is the short term immediate challenge most companies will have, but specifically insurers. With the various regulations around the corner (TCFD has already been introduced in the U.K., EU CSRD, German Supply Chain Act, etc.) an increasing number of companies will have to report on a number of sustainability-related KPIs, insurers included. Functionalities include scenario configurability including orderly transition scenarios, disorderly ones, and failed climate policy variations, attribution modeling between transition and physical risk alongside an exploration of different possible timings of market impacts that a scenario may cause.