Description: This is the second of a two-part article introducing and elaborating an “Adaptor” strategy for ERM. Such a strategy is intended to fulfil two purposes: first, switching among four styles of risk-coping strategies in order to track qualitative changes in a business’s risk environment; and, second, to support the business in navigating with resilience through all the seasons of a business-economic cycle, i.e., of boom, bust, moderate, uncertain. This latter requires careful study of the nature of resilience, the familiar idea of bounce back, and the less familiar idea of bounce forward. Part I of our article is underpinned therefore by a supplementary document (available from https://riskviews.wordpress.com/adaptor-exceptionalism/), which addresses the issue of structural change in a business or economy. It draws heavily on ideas taken from the discipline of control engineering, as well as some from ecology, social anthropology, and economics. The entire two-part article is thus an exercise in cross-disciplinary Systems Thinking.

Description: This is the first of a two-part article introducing and elaborating an “Adaptor” strategy for ERM. Such a strategy is intended to fulfil two purposes: first, switching among four styles of risk-coping strategies in order to track qualitative changes in a business’s risk environment; and, second, to support the business in navigating with resilience through all the seasons of a business-economic cycle, i.e., of boom, bust, moderate, uncertain. This latter requires careful study of the nature of resilience, the familiar idea of bounce back, and the less familiar idea of bounce forward. Part I of our article is underpinned therefore by a supplementary document (available from https://riskviews.wordpress.com/adaptor-exceptionalism/), which addresses the issue of structural change in a business or economy. It draws heavily on ideas taken from the discipline of control engineering, as well as some from ecology, social anthropology, and economics. The entire two-part article is thus an exercise in cross-disciplinary Systems Thinking.

Description: Discussion of how to mature value-based contracts between health insurers and provider organizations to improve population health and financial sustainability. Centers around contract parameter consistency, revenue predictability and risk assessment. Intended audience are health plan and ACO actuaries and leaders. Authors are writing from the provider perspective in value-based contracts.

Description: An update to the risk management readers on what the JRMS is planning to do over 2023 to serve its membership.

Description: The article introduces a cashflow matching strategic asset allocation (SAA) framework for life insurers. Given the recent shift to higher interest rates after two decades of persistent low interest rates, insurers have an opportunity to enhance their asset liability management by rebalancing asset portfolios to narrow duration gaps and attain higher risk-adjusted yields. The framework aims to align investment objectives with economic, regulatory, and rating agency requirements. It prioritizes cashflow matching as the primary optimization goal and incorporates security-level asset information to assist portfolio managers in executing SAA seamlessly. The article highlights the benefits of the cashflow matching SAA framework, including greater attention to cashflow matching, unbiased results, readily executable strategies, and stable economic reserves. A case study is included to demonstrate the effectiveness of the framework in improving risk-adjusted return and cashflow matching for a representative liability block.

Description: COVID19 highlights the need for greater complexity expertise in risk management. Crisis like the current COVID19 pandemic and 2008 Financial meltdown and others help us to turn into focus inwards for introspection on how we can better manage our resources and provide a deeper understanding of what our objectives should be and how we can achieve them through applying complexity science.

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: The SOA is interested in understanding how companies are performing scenario analysis, thinking through how various stakeholders (regulators, general public, competitors) could be reacting to a large-scale large-impact event over a multi-year period.

Description: The introduction and effective application of US GAAP LDTI have increased the scrutiny of those reviewing the updated financial results. That is because LDTI has resulted in significant changes in actuarial reserving and deferred acquisition cost (DAC) models for long-duration contracts, including significant changes to data, assumptions and required disclosures. In light of the changes, greater reliance has been put on companies’ model risk management (MRM) teams and model validations to assess the conceptual soundness and accuracy of their companies’ actuarial models and to assess that the underlying data and process utilized are suitable and appropriate. This article covers a brief background on model validation, walk through a sample LDTI-focused model validation framework/test plan, then provide insights on common LDTI-related validation observations based on the authors' prior validation experience.

Description: This is report summarizes the results of a Joint Risk Management Section sponsored study on negative interest rates and the insurance industry.