Description: In this report, we consider high-level results from provisional mortality data released from the U.S. Centers for Disease Control and Prevention (CDC) for 2021, including looking at changes by sex and age group.

Description: This paper compares two mortality projection models – the latest version of the CMI Mortality Projections Model and the latest version of MIM-2021.Both models were applied to data for England & Wales and the US, and the results were compared.

Description: A literature review was performed on mortality improvement, socioeconomic differences in mortality and their links to specific causes of death, and methods for analysis. It highlighted the fact that socioeconomic status (e.g., income and education) is closely linked to differences in mortality and that the longevity gap between socioeconomic groups is growing over time. It also informed the selection of key variables and the need to view mortality improvement as a complex interaction between specific causes of death.

Description: This report models and forecasts future mortality improvements in China, by geographical regions, as well as by causes of death. Deaths and population exposures data provided by the Chinese Center for Disease Control and Prevention was used for the period 2004–2019.

Description: Non-guaranteed elements are parameters of life insurance and annuity polices that are not fixed when the policy is issued but may be changed from time to time by the insurance company.

Description: The rapid advancement of technology today will revolutionize how humans interact with their vehicles in the near future. Vehicle manufacturer initiatives to incorporate new technology into their products should usher in societal benefits such as improved driving safety, enhanced fuel efficiency and potentially lower costs for insurance.

Description: This project pertained to the development of anew Actuarial Science Learning Community model, involving four types of students' engagement: academic coursework, residential life, research, and professional development. The model provided students an immersive exposure to Data Science from the Actuarial perspective, which will result in Actuarial students greatly expanding their skill set and future job opportunities, at the intersection of Actuarial Science and Data Science.