Description: A critical step in valuing a company for the purpose of financial reporting is the computation of the market value of economic surplus (MVES). This article provides a novel approach to the valuation of MVES that is stable and reasonably immune to “market noise.” Current approaches to the calculation of economic surplus of an insurance company generally define such surplus as the market value of the assets (MVA) supporting the liabilities less the market value of liabilities (MVL). While MVA is observable in the market, MVL is typically computed directly, without regard to the underlying supporting assets. Subtracting MVL from any value of assets that changes with market movements results in an unstable surplus. Consequently, a surplus that is not fully reflective of market sentiment has been rejected by many. This same approach is further used in the calculation of market consistent embedded value (MCEV) when a balance sheet approach is used .

Description: Explore various aspects of the Asian reinsurance market in this webcast. Learn about the landscape within the market as speakers cover key topics like product perspectives, structured solutions and emerging trends and issues. Take an in-depth look at the post COVID-19 impact, as well as regulatory considerations that could affect the Asian reinsurance industry. Secure your spot in this opportunity and dive into the direction of this marketspace.

Description: To discuss the principles to be followed in the determination of dividend distribution scheme, how to ensure the fairness and sustainability of dividend distribution, and the specific issues to be considered in the practice of dividend distribution.

Description: This article discusses the attributes of a unified Operational Risk Management framework and how those attributes can underpin a seven-factor economic capital model.

Description: This article discusses the impact of rising interest rates on annuity writers and life insurers and the risk management challenges it presents. Falling interest rates were addressed as well.

Description: Artificial intelligence is rapidly transforming the investment landscape. From natural language processing and large language models to physical AI applications like robotics, autonomous vehicles, and drones, the evolution of AI is reshaping both how we analyze data and where we find opportunity. We’ll begin by exploring the evolution of AI, which is already driving disruption across industries such as coding, customer service, and marketing. However, the future of AI is physical AI (drones, AVs, robotics). With valuations already stretched, and growing concerns about the “circular economy” and bubble risks, investors should focus on high-quality tech companies that generate free cash flow and deliver strong returns on invested capital. We'll explore examples of interesting companies across the globe: Meta, Alphabet, and Uber in the US; ASML in Europe; Tokyo Electron in Japan; TSMC in Taiwan; and BYD in China. The session will also examine how natural language processing and large language models are reshaping the way quantitative and fundamental managers extract insights from raw data. We’ll highlight how these tools are being deployed to enhance understanding of risk and return, and how they’re increasingly supporting collaboration between quant and fundamental teams.

Description: This report explores how artificial intelligence can be applied to investment and retirement planning, highlighting real-world applications, risks, and recommendations.

Description: The article offers a more nuanced approach to caregiving as the population ages, suggesting our current focus on planning leaves out most people. Instead find solutions for non-planners!

Description: Attend this webcast to learn more on how to navigate managing ESG principles in the new normal of a post-COVID world. Enhance your ESG insights in a post-pandemic landscape.

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.