Description: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.

Description: Changes introduced by various Medicare Alternative Payment Models and the Medicare Access and CHIP Reauthorization Act (MACRA) have accelerated an existing market trend of shifting some of what has traditionally been considered “insurance risk” to providers. The Provider Risk Sharing Boot Camp will give attendees deeper insights into methods, current thinking, and alternative solutions that actuaries can use to help providers understand and manage the financial and actuarial risks that they are taking, many for the first time.

Description: The presentation provides an overview of the motor insurance pricing. It will highlight some good practices and important skills desired by the actuaries.

Description: LON-CAPA is a full-featured course management, learning content management, and assessment system. It was initially developed by Michigan State University under several National Science Foundation grants. You can run LON-CAPA for free on your own dedicated server. Purdue University has used LON-CAPA to create a robust online homework system for the Financial Mathematics material of the FM Exam. This homework system is in the public domain and may be used by any university running LON-CAPA. This session will provide additional information and include a demonstration of the system.

Description: in the insurance industry, machine learning models are increasingly used to make decisions that affect people's lives. In order to ensure that models are fair, they should be audited for bias. There are no hard and fast rules for bias audit; metrics that can be used to evaluate fairness vary depending on the context in which the model is deployed. The presenters in this session present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process.

Description: Predictive modeling continues to gain traction in the life insurance industry with the rapid growth of available data and maturity of open source tools. New methods and technology in predictive analytics have enabled the evaluation of extremely large data more quickly and allow us to incorporate features previously not considered. The presenters will provide background on predictive modeling tools and algorithms utilized by actuaries and data scientists for life insurance, highlighting applications from marketing to underwriting.

Description: The presenters will discuss predictive analytics as applied to recent group life and disability experience studies and other applications.

Description: This is the informal transcript from Session 1A: Data Sources and Analysis from the January 4-6, 2017 Living to 100 Symposium in Orlando.

Description: Informal discussion 2017 LT100 transcript for Session 1C: Impact of Aging: Biggest Current Policy Challenges from the U.K./U.S./Canada as a Result of Aging?

Description: This is the informal discussion transcript from Session 3B: Comparing Mortality of Different Groups from the 2017 Living to 100 Symposium in Orlando.