Description: This annual survey of long-term lapsation prepared by the Life Insurance Marketing and Research Association examines the experience of ordinary insurance in Canada between 1987 and 1988 policy anniversaries.

Description: This article is seeking participation from companies focused on nontraditional products and/or nontraditional distribution for the SOA Inter-Company Expense Study.

Description: Excel file to be attached to cover sheet on this same 2005-2007 individual life persistency study. Contains the actual persistency experience data from the study.

Description: Provide users with a demonstration of the 2012 IDEC Claim Incidence and Termination Rate Tables and adjustments to these tables to reflect experience of key segments of the business. Compares active life and disabled life reserves based on the Base Tables and CIDA.

Description: Analysis of possible mathematical relationships between hospital quality and hospital billed or allowed charges or other measures, using quality data as defined by the U.S. Department of Health and Human Services along with the Hospital Quality Alliance for their Hospital Quality Initiative.

Description: The 1986-98 Credit Risk Loss Experience Study represents a continuation of the 1986-94 study analyzing credit loss experience by institutional investors on private placement bonds. The study measures incidence rates, loss severities and economic loss rates associated with credit risk events for privately placed debt during the years 1986-98.

Description: This is a study of Pension Benefit Guaranty Corporation (PBGC) mortality experience. The study consists of data furnished by the PBGC for the time period of September 30, 2009 through September 30, 2014. All of the data come from single-employer pension plans taken over by the PBGC in a distress termination. This is the SOA’s first study of PBGC experience and provides another comparison point of retirement plans mortality experience for work performed by pension actuaries.

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: "Living to 100 in the time of COVID-19: A Study of Late –Life Mortality Trajectories, Leonid A. Gavrilov, Ph.D., Natalia S. Gavrilova, Ph.D., Academic Research Centers, NORC at the University of Chicago Swedish Mortality Does Not Decelerate in Recent Cohorts, or Does it? – Roland Rau, Ph. D., Professor, University of Rostock & Max Planck Institute for Demographic Research, Rostock, Germany; Marcus Ebeling, Ph.D., Karin Modig, Ph.D., Associate Professor, Anders Ahlbom, Ph.D., Professor Emeritus, Karolinska Institute, Stockholm Sweden; Kai Kaufhold, Aktuar DAV, Ad Res Advanced Reinsurance Services GmbH, Cologne, Germany; Annika Ziegler, Lukas Stake, Hochschule Koblenz & Ad Res, Germany"

Description: The session will highlight trends in recent industry mortality and lapse studies that have been conducted, including areas where individual companies typically have limited data. The sources of data feature SOA studies as well as other industry studies conducted by consultants and/or reinsurers. Gain an understanding of current drivers of mortality and lapse experience for life insurance evidenced in industry studies, and an understanding of available data to supplement experience in areas where you may have limited internal experience.