EAA Seminar: Climate Days 6.0: Climate Change and the European Insurance Industry – Evolving Approaches to Risk Quantification

Announcement from the EAA organiser:

The need for novel approaches to measure physical and transitional climate risks is becoming more and more evident as the discussion surrounding climate change and its effects on the European insurance sector develops. In order to handle the complex issues brought on by a fast changing environment, the sector must now adopt cutting-edge models as traditional ways are no longer sufficient.

The Role of Machine Learning in Life Insurance

While the application of machine learning (ML) techniques in the life insurance sector remains in its early stages, this technological advancement holds great promise. Historically, the slow adoption of ML in life insurance has been attributed to several factors, including the limited availability of robust datasets and, to some extent, the lack of formal training for actuaries. In the past, modelling climate data with actuarial data using ML methods was particularly challenging due to both the scarcity and inconsistency of data. For instance, death counts were only available by age bands and gender on a weekly basis in the UK, but not by administrative region, while historical weather series data was available on an hourly basis for ca. 200 weather stations in England and Wales.

That said, the main obstacle is no longer the availability of data, which was once a significant barrier. The emergence of big data and enhanced data-gathering techniques have provided insurance companies with access to more comprehensive datasets. The current challenge lies in effectively integrating ML techniques into the industry's existing frameworks.

Understanding Climate Risks in Insurance

For the insurance industry, climate change poses a diverse array of risks that impact both the asset and liability sides of the balance sheet. These risks are multifaceted and demand a more sophisticated approach to quantification and mitigation.

While the actuarial community develops new ways to measure the economic impact of the risk posed by climate change, it is exceedingly important for actuaries to gain an understanding of how to use climate data, how to bridge the gap between climate models and actuarial projection models and how to produce relevant KPIs. Additionally, how actuaries can leverage new technologies to make more sense of available data in insurance to develop strategies to adapt to the risks posed by climate change.

During our Climate Days 6.0, we will explore the following key topics in depth:

The Impact of Climate Change on Low-Income Populations
What is the disproportionate impact of climate change on low-income populations, and what actions should insurers take to mitigate these risks? Vulnerable communities are often the hardest hit by climate events, and insurers must consider how to provide coverage that addresses this inequality.

Quantifying the Relationship Between Climate Change and Mortality
How does climate change influence mortality rates, and how can the insurance industry capture this relationship effectively? Rising temperatures, shifting humidity patterns, and extreme weather events all have a potential impact on mortality rates, but capturing the ‘signal’ between weather variables and death count or mortality rates remains a challenge.

Machine Learning for Climate Data Modelling
How can machine learning models be used to process and analyse granular climate data? Moreover, how can these models project the future impact of climate change under different emissions scenarios? Accurate forecasting is vital for insurers to understand long-term risks.

Measuring Climate Impact on Asset Portfolios
How can the effect of climate change on an asset portfolio be encapsulated into a single, actionable KPI (Key Performance Indicator)? Insurers need to integrate climate risk into their investment strategies to protect portfolio value in a rapidly changing environment.

Leveraging Large Language Models (LLMs) for ESG Reporting and Climate Stress Testing
How can LLMs, such as GPT, be deployed to summarize critical insights from ESG reports and assist in conducting climate stress tests? These models can quickly synthesize vast amounts of unstructured information, offering insurers a powerful tool for risk management.

Natural Language Processing (NLP) for Climate Risk Sentiment Analysis
How can NLP strategies be applied to develop a sentiment index tracker that monitors climate risk based on news feeds, ESG reports, and other available data sources? This could help insurers remain agile and responsive to changing risk landscapes.

The Impact of Shifting Ecological Conditions on Disease Transmission
How are shifting ecological conditions, driven by climate change, altering the transmission dynamics of diseases? What potential does this have for larger outbreaks and the emergence of new disease hotspots, and how can governments, and also insurers prepare for this emerging risk?

Click here to register. Your early-bird registration fee is € 1,193.10 (€ 970.00 net plus 25% VAT, if applicable) for bookings by 19 March 2025. After this date, the fee will be € 1,574.40 (€ 1,280.00 net plus 25% VAT, if applicable).