Description: The most recent demographic data published in Mexico allows us to analyse in detail the profound demographic transition that has been taking place in the country for more than four decades. It is possible to develop forecasts for the future on the basis of this historic experience. The purpose of this article is not only to illustrate the profound demographic change in Mexico – and what it is expected to experience over the coming decades – but to highlight the challenges for the insurance industry posed by the various demographic trends. Wherever challenges are found, however, opportunities also exist, provided the industry succeeds in adequately transforming its own processes and meeting the needs of a changing market.

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: 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: Join us for the It Takes a Village: Insurance Product Development in the Digital webcast. Focuses will be directed toward a variety of disciplines within the actuarial science industry. We will also take a deep dive into UW, Technology Product and Marketing and Distribution. Don't miss the opportunity to bring digital insurance products to life and discover what the process of product evolution.

Description: A study of disability experience examines cost burden and degree of disability, and how these measures vary across different subpopulations.

Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.

Description: This article delves into the transformative realm of Business Intelligence (BI) tools. We explore the fundamental functionality and structure of these tools, and through case studies illustrate how they offer valuable insights into carrier experience and underwriting dynamics. From real-time data visualization to in-depth data analysis, discover the essential features that empower businesses to make better informed decisions.

Description: An application of GLM trees to mortality modeling using the Individual Life Experience Committee 2009-2015 dataset. GLM trees are a hybrid of GLMs and decision trees which are analogous to the way actuaries have analyzed data. The major benefits include speeding up actuarial analytic workflows, improving data understanding, and improving data visualization.

Description: Retirement planning generally includes a focus on decisions needed at time of retirement, but it often fails to focus on changes during retirement and additional decisions needed as people age. The 2021 SOA Risk Survey and other SOA research documents the lack of focus on future change. It is common for people to lose cognitive ability and generally to become less able to make decisions, manage their finances, their medical care and their lives at high ages.This panel focuses on communication and decision issues. The SOA has recently issued a decision guide on High Age Decisions. The brief identifies four domains for high age decisions: ·Health, health care and staying healthy ·Managing finances ·Housing and transportation ·Support networks The panel will include a discussion of communications and how we change, problems of physical, hearing, sight or cognitive decline and the challenges created and the four domains in the SOA Decision Brief.

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"