Description: This article will examine four approaches under two different methods (eight in total) used to combat the non-additivity of key rates and will compare them under a number of rate environments.

Description: Climate risk modeling is taking center stage. With businesses increasingly expected to show how they assess, adapt to, and mitigate the risks of climate change, companies are asking: What is the right climate risk modeling approach for our business?

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

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

Description: Results of a Survey of JRMS members re claims changes due to COVID

Description: The problem and largest challenge with systemic risk identification and management is that high-risk activities and high-risk companies are often identified only with hindsight. This article identifies risk factors and proposes a framework for systemic risk management that can be applied in advance by regulators, company management, and other stakeholders

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: it discusses the framework and modeling of the market consistent valuation of fixed indexed annuity, against the backdrop of the convergence of accounting standards and internal risk management.

Description: In this article, we summarize inflationary factors that may result in a sharp increase in interest rates and implications for life insurer convexity risk exposure, most notably to dynamic policyholder lapsation. We further provide insights into potential ALM strategies to mitigate this convexity risk. Due to the prolonged low-interest rate environment and low minimum interest rate guarantees offered to policyholders, liability convexity is higher than prior historical periods, which results in life insurers’ ALM positions being extremely vulnerable to short-term interest rate spikes. Convexity mismatches cause issues for life insurers under both rising and declining interest rate scenarios. This means that life insurers are not compensated for taking convexity risk and, therefore, convexity matching should always be considered in the asset optimization process. There are mixed signals from policymakers and financial markets regarding potential inflationary and sharply rising interest rate scenarios. The key takeaway is that life insurers are not compensated for taking convexity risk and it is the right time for insurers to review convexity exposure. There are different approaches available to mitigate convexity exposure through rebalancing of asset positions and hedges within each life insurer’s risk limits.

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.