Description: This article summarizes five key lessons we have learned in implementing LDTI and shares our insights on practical ways to address them. This is not an exhaustive list of implementation challenges, but companies that can successfully tackle the issues we describe should have a smoother data and systems implementation transition.

Description: Discover an introduction to decomposing differences in life expectancy by age and cause of death. Take a look into demographic techniques that decompose life expectancies at birth and older ages into the portions contributed by later age groups and causes of death-. These have been in existence for some time and provide a convenient device for analyzing mortality change over time as well as differences between sexes, countries, and ethnic groups. The methods will be described and illustrated by spreadsheet examples.

Description: "Population Aging, Global Climate Change and Social Security – Sally Shen, Research Associate, Global Risk Institute, John A. Turner, Ph.D., Director, Pension PolicyClimate Change and Mortality Time for Actuaries to Pay Attention – Sam Gutterman. FSA, CERA, FCAS, FCA, HONFIA, MAAA"

Description: The Mortality Improvement Model, MIM-2021- V3. Different mortality projection methodologies are utilized by actuaries across applications and practice areas. As a result, the MIM Advisory Group (“Advisory Group”) developed a single framework to serve as a consistent base for practitioners in projecting mortality improvement.

Description: This report examines the adoption and challenges of predictive analytics in life and annuity reinsurance, including current practices, regulatory context, and advanced AI applications.

Description: Award-winning essays on addressing unwanted bias in life and annuity insurance processes.

Description: Description and welcome to the 2020 Canadian Reinsurance Conference including highlight of the key note speaker

Description: General Session III – The Ageing Process: From New Technology to New Medicine

Description: "On the decomposition of mortality models into causes of death – Andrés M Villegas Ph.D – UNSW Australia, Madhavi Bajekal, Ph.D.,HONFIA, University of London, UK, Steven Haberman, Ph.D. – Bayes Business School, University of London, UK Forecasting cause-specific mortality rates using the insights from the cointegration analysis, Séverine Arnold, PhD, Viktoriya Glushko, Department of Actuarial Science, Faculty of Business and Economics (HEC Lausanne), University of Lausanne, Switzerland"

Description: Some research implies that micro SDOH-related risk factors can influence a significant proportion of an individual's overall health outcome. Contained in SDOH are items such as mental health, ongoing social connection and interactions, transportation, financial confidence, and other items.