Description: Reunite with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, IFRS 17, product strategy, cross-border communication and discussion, and more. Advance your career through learning and networking opportunities, and share your experiences in the finance and insurance industries, as well as other fields related to actuarial science.

Description: "Hear AM Best rating analysts discuss the rating process and the best capital adequacy ratio (BCAR), a quantitative foundation for AM Best’s assessment of carriers’ financial strength. The benchmarking of BCAR scores for various company types (national MCOs, BCBS plans, smaller regional plans) will be discussed. Look at how changes in investment and underwriting environment impacted the results over the past several years. Further, AM Best’s view of the quality of capital and various factors impacting the overall assessment of the balance sheet strength will be discussed. By attending this session, you will: Gain an understanding of AM Best’s building block approach to ratings and how the BCAR results fit into the process. Learn the differences between RBC and BCAR models. Learn how various factors—e.g. liquidity, financial flexibility, financial leverage, the org structure—impact the final AM Best assessment of health companies' capital."

Description: The management of the investments of an insurance enterprise has undergone a considerable evolution since the pioneering work of Redington in the mid 20th century. Best practices have evolved to incorporate ever more complex insurance liabilities, expanding investment asset classes and global market opportunities. Strategic asset allocation (SAA), liability driven investing, and other robust optimization approaches form the core of the professional management of insurance company investments.

Description: The article provides a summary of the Drake Risk & Opportunity Forum held on October 21, 2022. The forum was a hybrid conference that attracted more than 200 attendees from 17 states, two countries, 48 employers, and five universities. Following an introduction by the Zimpleman College of Business Dean, the forum held six sessions. First was an insurance company CRO panel followed by a panel of non-insurance company CROs. Gary Zhu shared his outlook for interest rates and capital markets. The fourth panel discussed insurance capital models. The fifth panel discussed environmental, social, and governance (ESG) criteria when building insurance company investment portfolios. The final session of the day was a discussion about the NAIC, how they operate, and their priorities for the coming year.

Description: This research report summarizes methodologies used for yield curve extrapolation to value liability cash flows that extend beyond the maximum observavble portion of the yield curve.

Description: This article argues that using Economic Capital as a metric does not capture all of the risks. The author suggests an alternative that better meets a corporation's overall risk management needs. This is value-based ERM, which is an approach that makes the quantification of enterprise value central to all aspects of the ERM process. It is a combination of two techniques—enterprise risk management and value-based management, and it captures all of the risks in a single metric.

Description: This report aims to help actuaries and other interested parties to define the relative differences in managing risks between the insurance and banking industries. This report guides actuaries and others about change risk management practices to prevent future financial failures. The paper seeks to examine what factors account for the differences in the United States, compared with Canada about the underlying causes and effects of the most recent crisis with financial institutions.

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: A central problem for regulators and risk managers concerns the risk assessment of an aggregate portfolio defined as the sum of d individual dependent risks Xi. This problem is mainly a numerical issue once the joint distribution of (X1,X2, . . . ,Xd) is fully specified. Unfortunately, while the marginal distributions of the risks Xi are often known, their interaction (dependence) is usually either unknown or only partially known, implying that any computed risk measure of the portfolio is subject to model uncertainty. Previous academic research has focused on the maximum and minimum possible values of a given risk measure of the portfolio, in the case in which only the marginal distributions are known. This approach leads to wide bounds, as all information on the dependence is ignored. In this paper, we integrate in a natural way available information on the multivariate dependence and provide easy-to-compute bounds for the risk measure at hand. We observe that incorporating the information of a well-fitted multivariate model may, or may not, lead to much tighter bounds, a feature that also depends on the risk measure used. We illustrate this point by showing that the Value-at-Risk at a very high confidence level (as used in Basel II) is typically prone to very high model risk, even if one knows the multivariate distribution almost completely. Our results make it possible to determine which risk measures can benefit from adding dependence information (i.e., leading to narrower bounds when used to assess portfolio risk), and, hence, to identify those models for which it would be meaningful to develop accurate multivariate models.

Description: In session 119 CS of the Washington Annual Meeting panelists discuss techniques for analyzing and developing investment strategies based on the mathematical approach of linear programming are presented. From the Record of the Society of Actuaries, Vol. 23, No. 3.