Description: This report provides actuaries and other finance professionals with an example of running the Medicaid Unwinding model with results at the national level on projected changes in both acuity and enrollment for Medicaid and Children’s Health Insurance Program.

Description: This report explores the behavioral factors influencing insurance purchase among Gen Z and Millennials for forward-looking purposes, leveraging a rich dataset spanning 22 markets across six continents.

Description: The Mortality Improvement subgroup of the American Academy of Actuaries’ Work Group and the Society of Actuaries’ Mortality and Longevity Oversight Advisory Council released a recommendation for a set of improvement factors that vary by gender and attained age to be used in conjunction with the 2008 Valuation Basic table or the 2015 Valuation Basic table for AG-38 purposes. This recommendation has been adopted by the NAIC’s Life Actuarial Task Force.

Description: Come learn from a panel of LTC industry professionals as they discuss setting goals for LTC wellness, measuring success of the programs, and integrating them with other LTC process (e.g. assumption setting) in an effort to create sustainable programs.

Description: Recent and potential near future extreme experiences are leading us to a new normal rather than back to the prior environment. Learn more about several examples of past Regime Changes along with a detailed discussion of how recent disruptions lead toward a persistent change in the business, social and political environment. Come develop an understanding of these drivers of change as well as the likely impacts on the Financial Services industry.

Description: This abstract describes a paper that introduces an approach to sensitivity analysis for quantitative risk models such as those used in solvency calculations.

Description: Results of 2019 survey to their subgroup members.

Description: This report is a technical paper on a Predictive Analytics Framework and Reusable Workflow for Experience Analysis in actuarial studies. It covers data analysis, machine learning models, methodologies, and model interpretation techniques.

Description: Even in published scientific research, we find fraudulent data. What is an actuary to do? Let's consider our actuarial principles and standards of practice to question our data quality and truthfulness. In specific, the article addresses ASOP 56 on Modeling and considerations of model risk as well as ASOP 23 on Data Quality. Recent examples are given from a Harvard research fraud controversy as well as questionable sumo statistics.