Description: Enterprise risk management (ERM) has grown fast in the 21st century, thankful due to a variety of extreme yet unfortunate risk events and regulatory changes. However, challenges continue with many other initiatives competing with ERM for resources within an organization. In this month’s newsletter, we revisit this old yet still important topic and bring a few relevant reports to your attention.

Description: Risk management techniques do not include system science. System science requires that the uncertainty of the world is incorporated into the results. The articles explains the difference between risk and uncertainty. Learn how risk practices create an overoptimistic view of future outcomes.

Description: The 2024 Dangerous Risks survey shows respondents react to recent increases in "old favorite" risks as technology evolves and the environment moves away from the pandemic.

Description: Feature articles discussing the risks created by climate change.

Description: This article introduces the JRMS library, how one can access it and what books are added this year.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

Description: Joint Risk Management Section chairperson's corner, June 2023

Description: The Federal funds rate increased by 4.25% in 2022 with further increases in 2023, which have already resulted in a liquidity crisis for a few banks. As the rapid increase of the interest rates pushed the new money rates above the yields of many life insurers’ investment portfolios, insurance companies are facing a dilemma between compressed margins from peer pressure and the concern with liquidity shortfalls and realized capital losses should mass surrenders happen. This paper discussed main emerging risks under rising interest rate environment, and several ways to mitigate these risks.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: The mismatch between how interest rate impacts the value of assets and liabilities could have material negative impact to companies’ earnings and surplus. To quantify and mitigate the impact of such mismatches, companies have used matching a combination of duration and convexity. I will review these measurement techniques and their limitations.