Description: This paper, a study of Canadian life insurance company expenses, is regarded by the author, Arthur Pedoe, as a continuation of his earlier study presented to the Society of Actuaries in 1952 TSA IV, 485. In examining various expense trends the author emphasizes what he feels to be useful, namely an overall expense ratio defined as actual to expected total company expenses. The paper and ensuing discussion explore how expected expenses should be determined, as well as examining trends in group business expenses, among other topics. From 1961 Transactions Vol. 13, No. 35 AB.

Description: The first annual report on the study of the morbidity experience of Group Supplementary Major Medical Expense insurance. From the Transactions of Society of Actuaries, 1967 Reports.

Description: The article highlights the key components of the PBR resource manual and analyze its implications for companies' assumption management practice.

Description: Data Request from individual life carriers who have policies that have a waiver of premium provision.

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: 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: 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: "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: With the mandated health plan transparency files, health plans and providers are now able to substantially enhance their provider contracting efforts. Although the files are large and cumbersome they are market disruptors and provide new insights for provider contracting efforts. Explore a demonstration on the type of information now available and how it can be used to enhance this from both a health plan and health care provider perspective.

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.