Description: Getting people to think beyond retirement about future care needs and how they will be paid for is critical for a healthy aging plan. However, most people avoid the topic and make rushed decisions when finding long-term care services. Hear from experts in this advisory field explain what a successful plan looks like and what it takes to get people to plan ahead.

Description: 1993 SOA Spring Meeting, Boston. This session addressed the problems unique to mutual companies. Topics addressed included: 1. In search of capital. 2. Merger/acquisition considerations. 3. The demutualization alternative. 4. Profit crunch. 5. Joint ventures. 6. Lessons from other countries - Canada, Mexico, Britain.

Description: As the size of the life reinsurance market in Bermuda has grown to over half a trillion dollars at the end of 2021, more and more actuaries are getting involved with this 21 square mile North Atlantic island in one way or the other. Come explore a fundamental understanding of the Bermuda solvency framework and an overview of its upcoming changes.

Description: In session 7I of the Montreal Spring Meeting panelists discuss the growth of the cross-selling of financial products in the banking and insurance sectors. From the Record of the Society of Actuaries, Vol. 23, No. 2.

Description: Provides an overview of India's insurance market and the important role of the actuary.

Description: As technology continues to advance and consumers' preferences constantly change, the need for insurance to be sold at more competitive prices has become apparent, forcing the Broker channel to adjust its business model and also creating a new opportunity for the Direct-to-Consumer channel. The presenter will examine how various channels have co-existed in the past, and evaluate how they will evolve and co-exist in the future. The discussion will also include an analysis of a channel strategy to optimize results for the growth of a company. Presenters will include representative from the following distribution channels

Description: "Driving Longevity Through Educational Attainment – Robert L. Brown – Ph. D. Higher Age U.S. Mortality By Education and Cause of Death Trends, Inequality and Controllable Risk Factors – Andrew J. G. Cairns – MPA Ph.D., Maxwell Institute for Mathematical Sciences, and Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, Scotland, UK, Cristian Redondo Lour´es, Ph.D., Actuarial Solutions,"

Description: "Weighted compositional data analysis for modeling and forecasting life-table death counts – Han Lin Shang, Ph.D., Professor of Business Analytics and Director of Research at the Department of Actuarial Studies and Business Analytics at Macquarie Business School, Macquarie University, Sydney, Australia, Steven Haberman, PhD, Bayes Business School, City University of London, UK Compensation Effect of Mortality as a Challenge to Life Extension – Natalia S. Gavrilova, Ph.D., Leonid A. Gavrilov, Ph.D. Academic Research Centers, NORC at the University of Chicago"

Description: Mortality Model Selection and Combination in RAlternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base mortality forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation and other alternative out-of-sample measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. Such methods combine predictions from multiple mortality models to reduce model choice uncertainty and improve forecast accuracy. Explore techniques to select and combine mortality models using the freely available open source R package CoMoMo (https://github.com/kessysalvatory/CoMoMo), which extends the widely used R package StMoMo (https://CRAN.R-project.org/package=StMoMo). We will use data for the USA and other countries to discuss and demonstrate four different model combination approaches including simple model averaging, Bayesian model averaging, model confidence set, and stacked regression ensembles.

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.