Description: This event is intended to provide an opportunity for members of the Actuary of the Future and Predictive Analytics and Futurism sections to network with other members, as well as a format to provide an opportunity to discuss hot topics in either/both sections. It is intended for actuaries registered in either section, as well as those interested in either section.

Description: A panel of practicing actuaries and policy analysts will discuss the 'big issues' that could affect the actuarial profession in the future, and suggest ideas on how the actuarial profession could adapt to these challenges. This will be an interactive session where attendees can expect to have an opportunity to weigh in through polling questions and discussion. Topics touched will include: socialized medicine, artificial intelligence, the competition from data scientists, data commoditization, disruption from new entrants into the healthcare insurance space, and more.

Description: Feature article discussing the usefulness of Lorenz and Lift curves.

Description: A concise overview of Generative Adversarial Networks.

Description: A description of the random generalized linear model (RGLM) and how to use it.

Description: Predictive analytics is impacting various actuarial disciplines. During this session, we will discuss how predictive analytics could impact and enhance retirement and pensions, along with how pension/retirement actuaries can utilize various enhanced analytical techniques. <em>This breakfast is open to all meeting attendees free of charge. If you plan to attend, you must register in advance.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.

Description: Overview of regularization and least angle regression approaches.

Description: This session will cover clustering techniques generally (k-means, hierarchical), and how k-means have been used to segment policyholders using third party features such as credit scores to assess customer profitability.

Description: This article gives a brief overview of ways to extend clustering analysis beyond basic methods such a k-means