Description: Data used for HAT at 2020 PA 4.0 event.

Description: Describes the type of information sought on the Preferred Class Structure Questionnaire for the 2005-2007 individual life experience study and explains how to fill out the questionnaire

Description: The FAQ page sent to potential participants for the 2007-09 Individual Life Mortality Experience Study.

Description: Submission components, format and considerations.

Description: This report contains estimates of incidence and continuance rates from the general population using data available from the National Long–Term Care Surveys. This report was done by a subcommittee of the Society's Long–Term Care Experience Committee and reflects work done primarily at the Duke University Center for Demographic Studies. This is a ZIP file containing main report, all appendices, tables, and figures from the study and model.

Description: This zip file contains the following Excel workbooks in support of the 1996 Group Long-Term Disability Experience Study: 1. SOATAB~1.XLS, 2. SOATAB~2.XLS, and TABLE95A.XLS.

Description: This note presents a review of calculator financial functions for the Texas Instruments BA II PLUS calculator. This note, including a number of the examples used as illustrations, is reprinted with permission from the 3rd edition of the book Mathematics of Investment and Credit, by S. Broverman. Also, several examples from SOA/CAS math of finance exams old Course 2 will be presented illustrating the use of the calculator.

Description: How might retirement income strategies be designed, and their suitability assessed, when retirees may differ substantially in their objectives, preferences and personal circumstances? This webcast provides a wide-ranging overview of the key elements involved in formulating retirement strategies, drawing on the “Primer on Retirement Income Strategy Design and Evaluation” that was recently released by the SOA. It will cover three types of income objective – income floor, income target and income optimization – and how they imply different retirement strategies along with a different set of metrics to evaluate outcomes. It will also touch on: key member attributes to account for; the nature of income risk; approaches to investing and drawing down on available assets to generate income; and how stochastic modelling can support the analysis. This webcast should deliver something of interest for all attendees, regardless of prior familiarity with strategies for retirement.

Description: 2023 Generally Recognized Expense Table (GRET) recommendations.

Description: In this session, we will delve into the powerful capabilities of two essential algorithms in the field of data science and machine learning: Generalized Additive Models (GAM) and CatBoost. These algorithms can play a crucial role in conducting experience studies for insurance.