Description: An excerpt from a presentation at the 2014 Living to 100 Symposium, this article describes an open-source model for use by actuaries attempting to estimate future health benefit costs, discusses its strengths and limitations, and projects that spending for those age over 65 will take more than $13 trillion by 2055, about 50 percent of total medical expenditures.

Description: Recognizing an opportunity to help serve its members in this age of increased financial oversight, the Health Section of the Society of Actuaries commissioned a series of guides on the use of statistical techniques specifically geared for the work of health actuaries. The first guide in the series titled, 'Statistical Methods for Health Actuaries - IBNR Estimates: An Introduction,' is about the calculation of incurred but not reported [IBNR] health claims reserves. In particular, the guide focuses on the development of confidence intervals around IBNR estimates. This is an Excel workbook created to demonstrate the techniques discussed in the guide.

Description: A discussion of alternative meanings to “Best” in best estimate. When one is trying to estimate a result that is normally distributed, the mean, median and mode will all equal one another. However if the results are not normally distributed, different variance metrics will result in different “best” estimates. When different parts of a financial statement use different criteria for measuring best estimates and different processes for handling variances, the results can be volatile, unpredictable and perhaps unreasonable.

Description: This abstract describes a paper that develops a method to estimate the two parameters of the discrete stable distribution.

Description: An approach to quantify medical intervention savings when medical claims data is not available by leveraging overlapping measurement techniques.

Description: This article discusses the use of parametric distributions to model health care cost.

Description: This study note focuses on actuarial applications and loss distributions for insurance products.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

Description: Private LTC insurers are paving the way for value-based care through working to implement care management programs that improve the lives of their policyholders before and during a claim. However, measuring the effectiveness of these new programs is a challenging task. In this article, we will explore powerful tools that can enhance traditional effectiveness studies and address some of their existing weaknesses.