Description: The following case study walks through the steps of a predictive modeling exercise to illustrate one way analytics can streamline the underwriting paradigm.

Description: This report describes a simple way to construct a bivariate copula with specified marginals and partially specified dependence. For example, the method allows one to model tail behavior scenarios while holding the core of the joint distribution fixed. An Excel workbook named CopulaExample.xls is provided to illustrate the method, including its use in Monte Carlo simulation.

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

Description: After more than a decade of a prolonged low interest rate environment, long-term Treasury rates have started to increase with higher volatility. While it is generally agreed that a modest increase in interest rates and inflation is beneficial to the life insurance industry, interest rate risk remains a significant challenge for life insurance industry to manage its long duration liabilities, especially under volatile interest rate environments. Learn how to proactively manage interest rate risk and improve capital efficiency through the cash flow based strategic asset allocation (SAA) framework. Discover practical examples of how to combine asset allocation with derivatives to manage tail risk and liability convexity.

Description: This session is a primer on how to apply Gestalt psychology to display quantitative information to improve neural receptiveness. While much of brain functioning is still a mystery to psychologists and neuroscientists, we do understand a great deal about which parts of the brain engage or disengage given visual cues. Actuaries are often tasked with presenting very technical information and are prone to focus more on the substance of numerical data rather than a pictorial presentation based on neural preferences. This presentation will discuss how to display quantitative information more effectively tailored to these preferences.

Description: "Explore a general introduction to the opportunity of applying a relatively newer area in machine learning called uplift modeling, which has been successfully used in retail (Wayfair etc). The concepts are parallel to the intervention programs in health care, where we are interested in measuring the treatment effects and ultimately directing the right members to the most effective programs, driving the highest value for the programs both from a financial and health outcomes perspective. Uplift modeling can also be viewed in context of causal modeling where we are interested in assigning cause to a treatment relative to a member not receiving the treatment. Check out an introduction on the concept, seehow it relates and builds on existing work, and use a demo to showcase the potential of integrating this ML approach to current work."

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.

Description: The purpose of this article is to provide a high level summary of the paper “Variance of the CTE Estimator” by B. John Manistre and Geoffrey H. Hancock that appeared in the North American Actuarial Journal in 2005. The authors also expanded on some of the results.

Description: A study of disability experience examines cost burden and degree of disability, and how these measures vary across different subpopulations.

Description: This paper presents a method that uses three-dimensional graphs to solve stationary population problems. From Transactions of Society of Actuaries 1981, Vol. 33.