Description: Learn about the industrial practice for constructing the mortality table from 0 to 1 through journeys in Hong Kong and Singapore, including key takeaways. Hear speakers share their biggest challenges during their process, how they resolved them, and discover the most common technical issues faced in conducting the mortality tables.

Description: Actuarial models and modeling processes continue to grow in complexity, accelerated by the changes in U.S. Statutory, U.S. GAAP and IFRS reporting requirements in the past few years. Modeling and valuation teams are expanding their toolkits beyond the traditional vendor-maintained actuarial software and the MS Office suite of tools. Companies have started to use a variety of tools to improve model dev and valuation processes, including: - Data management - Automation and workflow - Reporting and analytics

Description: This paper uses a mortality surge model to estimate the total financial impact on the U.S. insurance industry of a flu pandemic. It does not attempt to determine the probability of such a pandemic.

Description: This paper shows the mortality element can be partially isolated allowing an exact annuity value to be readily obtained at any interest rate. From the Actuarial Research Clearing House 1979, Vol.2.

Description: The United States Geological Surv ey USGS released the latest version of its National Seismic Hazard Maps NSHM in April 2008. The maps, which were last updated in 2002, define the latest scientific view of earthquake hazard at varying probability levels across the United States.

Description: This paper analyzes the U. S. Treasury monthly interest rates from 1953 to 1992, and the daily rates from 1981 to 1992 for various forms of chaotic behavior. The primary analysis was in determining the Burst Exponent by the use of Rescaled Range Analysis. This paper also discusses other possible uses of Fractional Brownian Motion in Finance Theory, Macroeconomics, and Actuarial Science. From Actuarial Research Clearing House 1994 VOL. 1.

Description: Historically different approaches have been used by actuaries in modeling mortality improvement. The SOA has recently released a framework that practitioners can use to project mortality improvement, providing methodological consistency across a range of applications.

Description: The life annuity mortality risk is the variation in actual financial experience of a group of annuitants from that expected, attributable to the mortality experience of the group. This paper investigated the mortality risk present in a single life annuity and determine the effect of age, sex, assumed interest rate, mortality table, and form of annuity on this risk. Then, it investigated the mortality risk in various portfolios of annuities. Followed by several discussion papers submitted

Description: It has been a little over a year since the release of the Society of Actuaries (SOA) Research Institute's Mortality Improvement Model, MIM-2021. Since then, waves of COVID-19 have continued to occur in the U.S. The uncertainty around the COVID-19 pandemic and the slowing of U.S. general population mortality improvement prior to the pandemic are just a few of the reasons that make developing MI projection scales difficult. Uncoverthe results from intercompany surveys on mortality improvement, including one that addresses COVID-19, discover insights from a mortality expert panel on COVID-19 and future mortality, and discuss enhancements to MIM-2021. By attending the session, you will: - Understand MIM-2021 and 2022 enhancements. - Understand SOA Research Institute activities around COVID-19 and impact on mortality improvement. - Apply research results and insights presented to daily work.

Description: From a session at the 2000 Valuation Actuary Symposium held in Washington DC, September 14-15, 2000 In this session the panelists discussed the issues related to the actuarial aspects associated with the demutualization process. The issues discussed included: • Developing historical and prospective assumptions • Aligning the models used in the company’s philosophy regarding dividend determination and pricing • Creating experience assumptions when less-than-perfect data exist • Determining who gets what • Complying with actuarial and accounting standards