Description: Risk adjustment is needed under IFRS 17 to reflect the compensation that a company requires for bearing the uncertainty about the amount and timing of the cash flows that arises from non-financial risk. Companies are also required to disclose the method and confidence level used for the calculation of the RA. However, IFRS 17 doesn’t specify a method and a confidence level, nor does it provide a list of specific risks that are considered to be non-financial risk. Companies need to define them based on their own preferences or existing practices. This article will introduce available approaches and discuss the confidence level approach with potential consideration stemming from industry preferences and illustrative examples.

Description: This is an update, as of July 2020, on projects in process and those recently completed sponsored by the Financial Reporting Section.

Description: Given the limits of classical actuarial modeling and forecasting techniques, this newsletter contains articles discussing several ways to supplement your toolset.

Description: Today's actuarial teams dedicate substantial time and resources to maintain strong frameworks for risk management and control. Current practices typically endure this as a sunken cost, yielding minimal value beyond SOX/MAR/COSO compliance and basic error prevention. With a small up-front time investment, and a willingness to approach these activities with an open mind, the functionality of existing compliance activities can be completely transformed. Compliance activities, once a sunken time cost, can be elevated to deliver more insightful analysis to stakeholders.

Description: The CAS has sponsored research which explores potential exposure bases for rating cyber liability. This short article gives some highlights from that research.

Description: One of the most fundamental concepts in actuarial practice is the time value of money. For any work in which future cash flows are allowed for, such as reserving or pricing, it is natural to discount to present values so that an appropriate amount of money can be set aside today, allowing for future investment returns. This article discusses yield curve extrapolation methods along with the benefits and drawbacks of these methods.

Description: Economic risk analysis has two dimensions: time and frequency. Asset return varies by time because of economic cycles and economic structural changes. Risk is higher during economic recessions and smaller during economic expansions. In addition, different economic structures may exist at different time scales. Risk measures calculated based on daily, weekly, monthly and yearly historical data can be very different. The appropriate frequency depends on the time horizon of the analysis. Therefore, it is important to measure economic risk at both the time level and the frequency level. However, time series analysis and statistical analysis that have been widely adopted in economic risk analysis focus on only one of the two dimensions. In this paper, wavelet models are used to enhance both time‐invariant analysis and time‐variant analysis. Wavelet models can systematically analyze risk by time and frequency and provide richer information than time series models. Using wavelet models, risk measurement can easily be adjusted based on time horizon in a consistent way. Equity VaR estimation is selected to demonstrate the application of wavelet analysis in risk management.

Description: One of the most fundamental concepts in actuarial practice is the time value of money. For any work in which future cash flows are allowed for, such as reserving or pricing, it is natural to discount to present values so that an appropriate amount of money can be set aside today, allowing for future investment returns. This article discusses yield curve extrapolation methods along with the benefits and drawbacks of these methods.

Description: The article provides an update on the 2019 ERM Symposium with regard to date, location and content (General Sessions and Concurrent Sessions).

Description: The second of three articles exploring the new technology offered by Santa Fe, New Mexico-based Assuratech, Inc. Describes the development of the applied complex adaptive systems technology behind Assuratech’s highly sophisticated program that uses simulation and data mining techniques for accurate, reliable versatile risk modeling.