Description: According to the Actuarial Standards Board’s Actuarial Standard of Practice No. 49 (ASOP 49), actuarially sound Medicaid managed care capitation rates will include a provision for underwriting gain in order to provide compensation for the risks assumed by an MCO. However, there is no generally accepted method for quantifying underwriting gain in managed Medicaid capitation rates. This article discusses a new model developed to address this void and calculate underwriting gain. The model was developed by researching existing financial literature and performing a statistical analysis of industry data to build out a framework that can be leveraged to estimate underwriting gain.

Description: Article presents issues for postretirement medical on models, data and the frail elderly. From Health Section News, December 2000, Issue No. 39.

Description: This report provides a toolkit for actuaries and other interested parties to model and forecast mortality by cause and socioeconomic factors.

Description: The Product Development Section, the Financial Reporting Section, the Modeling Section and the Committee on Life Insurance Research are pleased to present new research on modeling and forecasting cause-of-death mortality. Authored by a Milliman team led by Alexandre Boumezoued, the report develops a framework for forecasting cause specific death rates. Included also is a Cause of Deaths Forecasting Tool and a user guide for the tool.

Description: Summary of the modeling-related sessions from some of the major 2018 Society of Actuaries (SOA) meetings: Life & Annuity Symposium, Health Meeting and the Valuation Actuary Symposium.

Description: This paper reviews the applications of operations research methods in the insurance industry. From Transactions of Society of Actuaries 1995, Vol. 47.

Description: Presenters at session 9I of the Maui II Spring Meeting discuss credibility and why it should be important to the actuary. From the Record of the Society of Actuaries, Vol. 24, No. 2.

Description: This paper gives a brief review of advanced time series models, the state space model, using Monte Carlo techniques and subsequently illustrates their usefulness to practicing actuaries. The models employed allow for the possibilities of nonnormal errors and nonlinear functionals in the state equation, the observational equation or both. The approach is illustrated with several examples and data compiled by the National Center for Health Statistics.