Description: The U.S. health care industry has gone through a dramatic change during the past several years. The business model has moved from a risk selection model to a risk management model. People can shop online for their health care coverage. Everyone is welcome! Annual and lifetime dollar limits, underwriting and the pre-existing conditions exclusion are all in the history book now. Facing the challenges of the post-reform era, many health insurance carriers began using big data analytics on all aspects of their decision-making processes. The goal is to be able to compete on analytics in this quickly changing environment. However, becoming an analytics competitor is not easy. It requires the belief from the leadership and a group of dedicated analytical professionals to solve emergent business problems that existing strategies cannot address in an evolving market. These problems include marketing for acquisition and retention, coding accuracy, revenue growth, health care disparity, payment integrity, quality of care, health care cost reduction and, ultimately, personalized care management. In this section, audience will learn a basic understanding of the role of predictive analytics in US Healthcare marketplace.

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: 2003 Valuation Actuary Symposium, San Diego, CA. In this Teaching Session the instructor discussed the importance of making sure the correct inferences were drawn from stochastic testing exercises. Topics included: 1. Applying the assumption of a normal distribution. 2. Drawing inferences about, and creating confidence intervals around, a population mean. 3. Drawing inferences about, and creating confidence intervals around, tail percentiles. 4. Certifying anticipated mortality under XXX in a mathematically concise way.