Organised by the EAA - European Actuarial Academy GmbH in cooperation with the Hrvatsko Aktuarsko Drustvo.

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
• Quantifying the Relationship Between Climate Change and Mortality
• Machine Learning for Climate Data Modelling
• Measuring Climate Impact on Asset Portfolios
• Leveraging Large Language Models (LLMs) for ESG Reporting and Climate Stress Testing
• Natural Language Processing (NLP) for Climate Risk Sentiment Analysis
• The Impact of Shifting Ecological Conditions on Disease Transmission