Programme Risk Day 2024
Friday, 13 September 2024
ETH Zurich, main building, Rämistrasse 101, auditorium HG E7
Abstracts
Knut Norheim Kjær
Can Investors Impact on the Risk of Global Warming?
Taking the global net emissions of greenhouse gases down to zero by 2050 implies a gigantic shift in technology, production processes and capital structure. Huge amounts of new investments are required. Most of it will come from the private sector.
Knut will in this lecture present required changes in the capital structure and discuss mechanism that give investors incentives to contribute. That includes also investors potential self-interest in protecting their portfolios from climate risk. To what extent may their actions impact on the risks related to global warming?
Anastasia Kartasheva
The Supply of Cyber Risk Insurance
Cyber risk losses are large and growing, yet the cyber insurance market is small. What constraints the insurance industry from providing larger capacity for cyber risk? We argue that cyber risk is special in that it combines heavy tails, uncertain loss distribution, and asymmetric information. We model the implications of these risk features for risk financing and then test them empirically in the context of the US cyber insurance market. Using an exogenous shock of the non-US affiliated reinsurance tax treatment in 2017, we establish the causal inference that insurers primarily rely on the internal capital market to supply cyber risk insurance. Then, we test which of the features of cyber risk contribute to the cost of external capital and confirm that all of them play a significant role.
Joint work with Martin Eling and Dingchen Ning.
Christiane Hoppe-Oehl
A View on AI: Opportunities and Risks from a Supervisory Perspective
How is AI being used in the Swiss Financial Market and what are the risks? Christiane will provide insights from surveys and international discussions around AI, how supervisors approach the topic and what is expected from companies to ensure AI is implemented in a responsible manner.
Christian Westermann
Artificial Intelligence - Driving Re-Use and Mitigating Risks at Global Scale
AI and GenAI are with no doubts a real game-changer in the insurance industry. To meet current and future needs of our customers, we believe three things need to come together: high-impact use cases, a mechanism to scale AI capabilities to any place where our customers are, and an effective AI governance that ensures proper risk mitigation. In his presentation, Christian will highlight where Zurich Insurance is on its journey and what the learnings have been so far.
Torsten Hoefler
AI for Climate Data Generation, Assimilation, and Modeling
This presentation explores the convergence of Artificial Intelligence (AI) and climate modeling for high-resolution (km-scale) climate prediction. We will discuss the use of accelerated km-scale simulations to generate synthetic climate data, enabling the training of even more sophisticated climate models. We'll delve into the integration of observational data from diverse sources – weather stations, satellites, and airborne platforms – through diffusion models, a technique commonly used for image generation. To address the exascale challenge posed by climate data volume, we will present an AI-based compression method utilizing deep neural networks for efficient data representation. This approach allows for near-lossless reconstruction of the original data for subsequent analysis. We posit that this synergistic approach, combining high-fidelity data generation with advanced data assimilation techniques, has the potential to significantly improve the accuracy of climate predictions. This, in turn, can inform policy decisions and guide societal responses to climate change.
Download Slides (PDF, 33.4 MB)
Fabian Uffer
Internal Solvency Models in (Re-)Insurance: Thoughts from a CRO
Internal models have been developed over the past 20 years and are widely used to assess the solvency of insurance companies. This presentation will provide an overview of the evolution of these models, highlight current challenges, and touch on a few classical risk modeling topics.