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: Industry briefings and SOA news

Description: The Society of Actuaries (SOA) is pleased to bring you information about the actuarial career path in life insurance and predictive analytics. We’ll continue to share more about actuaries who are tackling some of the industry's biggest challenges and top employers in life insurance.

Description: Interview with Milliman consultant Sarah Konrad Hinchey

Description: Meet Christine Hofbeck. Christine is pushing the boundaries of traditional life and general insurance by finding problems and fixing them with new technology, modeling methods and sources of data. Most recently, she built a predictive analytics team at one of the world's largest financial services institutions to support the pricing team for a $5 billion portfolio covering life, disability, critical illness and more.

Description: Interview with John Houston, Advanced Analytics and Predictive Modeling practice leader at Deloitte.

Description: Read the April 2017 issue of CompAct published by the Technology Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Interview with Ian Duncan about his actuarial background and role in predictive analytics.

Description: Industry briefings and SOA news

Description: The purpose of this paper is to define a predictive model, based on known system attributes, for assessing risk associated with information systems.