Description: What is a "super" model? How do we develop, maintain, and visualize them? What is the difference between models and assumptions? Most importantly, how can we use all of this to quantifiably improve risk management?

Description: The Triple Aim is a vision to improve the health of populations, create a better quality of life, and reduce per capita spending for the benefit of communities.(http://www.ihi.org/Topics/TripleAim/Pages/default.aspx) While much public attention on wellness has focused on topics such as obesity and smoking, it would be beneficial if greater emphasis was placed on raising the bar of oral health. The mouth plays an important role in overall health and wellness. Routine oral care helps prevent dental problems and appears to positively affect chronic conditions before they develop into complex, more expensive ones. This purpose of this session is to increase awareness of the importance of oral health, the consequences of poor oral health, and the link with socioeconomic characteristics. This session will explore and highlight studies completed in this space by both researchers and payers. Actuaries interested in learning more about the impact of oral health and overall health, relevant interventions that have been undertaken, areas for potential financial savings, and issues relating to public policy applicable to oral health would benefit from attending this session.

Description: The presenters will explore policyholder experience data for long-term products, risks of over-fitting data in developing behavioral models, and ways to mitigate these risks and develop a sustainable and coherent framework to translate complex policyholder behavior experience data to behavioral models using company- and industry-level data. At the conclusion of the session, attendees will be able to: - Identify key drivers of policyholder behavior for long-term product - Understand the risks of over-fitting data and a false sense of precision in developing behavioral mode - Understand techniques that can be used to mitigate these risk

Description: The Excel spreadsheet contains the supporting source lapse rates for figures within the U.S. Individual Life Insurance Persistency report. The data for this report, and the Excel spreadsheet, represent experience years 2009-13.

Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.

Description: This report presents the results of individual life insurance lapse experience in the United States between observation years 2009 and 2013. Lapse experience is analyzed for whole life, term life, universal life, and variable universal life plans issued between 1918 and 2012. Results for most key policy and product factors are examined.

Description: CMS provides a significant, and growing amount of information that can be leveraged by actuaries. Between the public use and research files CMS makes publicly available large amounts of information that can be assimilated to begin to understand the who, what, when, where, and how of Medicare. This session will focus on the depth and breadth of data available, summarize any restrictions on the use of the data, and identify areas where access to the new data can provide more meaningful insights than CMS's historical data sets.