Description: There is value in a framework that allows a smooth transition from a qualitative assessment to a quantitative analysis as entities mature in their understanding of climate risk. In this report, a case study on climate change impact on home prices is used to illustrate a Bayesian network iterative approach to building a model for scenario analysis.

Description: Come discuss modeling costs and its governance, as well as introduce some platforms for more granular asset modeling.

Description: Book review of Extreme Risk Management: Revolutionary Approaches to Evaluating and Measuring Risk by author Christina Ray.

Description: The document is a research paper titled "Risk Analysis of the Causes of Food Deserts" authored by Lillian Wang, Audrey Hong, Jaden Chen, and Aaroh Tak for the Modeling the Future Challenge 2023-2024. It analyzes food desert risk factors, forecasts food desert rates, and proposes risk mitigation strategies based on statistical modeling and data analysis.

Description: This paper evaluates the current practice of operational risk management in the insurance sector. Operational risk is nothing new in insurance, but, because of regulatory requirements, companies have initiated computation of risk capital for their operational losses. The current effort to manage operational risk is not a naturally evolving phenomenon, and operational risk, in the Basel Committee on Banking Supervision’s Basel II definition, is unlikely to be a significant cause of insurers’ failure. In addition, the current Basel II definition of operational risk is not suitable for the insurance sector. Consequently, the invention of models and tools based on the definition is incomplete and illusionary. My findings are based on the analysis of dozens of interviews with insurance industry professionals. I demonstrate the way operational risk is quantified in practice, show that the result obtained from this computation is of littl e use in managerial decision making and propose a set of policy recommendations illustrating the characteristics of operational risk in insurance. This study can be used as a platform for launching dialogues to initiate fresh thinking about operational risk in insurance beyond the current artificial and narrow boundaries.

Description: The Casualty Actuarial Society, the Society of Actuaries Research Institute, the Society of Actuaries Joint Risk Management, Reinsurance and Financial Reporting Sections are pleased to make available key findings from the 2022 Emerging Risks Survey, the sixteenth in the series. Conducted by Max J. Rudolph of Rudolph Financial Consulting LLC, the survey incorporated a set of Emerging Risks defined by the World Economic Forum as the basis for several of the questions. The survey also included questions related to current risk management topics.

Description: Gather with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, pension, investment, product strategy, and more.

Description: A comprehensive webcast designed to bring together industry practitioners to discuss the current state of Model Risk Management (MRM) frameworks. This event will delve into the advancements in MRM, the challenges faced by professionals in the field, and emerging trends such as machine learning model validation that are set to shape the future of MRM. Engage with experts, share knowledge, and explore innovative solutions to enhance the effectiveness of your MRM framework.

Description: This study presents a comprehensive approach of real option analysis to climate adaptation policies aiming to mitigate flood risk. The framework focuses on pricing catastrophic risk in coastal regions, allowing for economic growth and uncertainty in the mean sea level rise.

Description: The United States Geological Surv ey USGS released the latest version of its National Seismic Hazard Maps NSHM in April 2008. The maps, which were last updated in 2002, define the latest scientific view of earthquake hazard at varying probability levels across the United States.