Description: Report on the activities of the Academy’s health practice council and its work groups.

Description: The time has arrived for ASU 2018-12, otherwise known as Long-Duration Targeted Improvements (LDTI). For health insurance products, the ASU will require considerable efforts to make the necessary changes to valuation systems, procedures, assumptions, and/or methodologies in order to be in compliance. This session is designed to cover LDTI implications specific to carriers of long-term care and supplemental health products, including changes to how attained age rated products will be approached, allocating and discounting the claim liability (IBNR), and how the treatment of Profits Followed by Losses (PFBL) will evolve under LDTI. By attending this session, you will be able to: • Understand LDTI’s key impacts on long-duration health insurance products. • Recognize the treatment of attained age rated products under LDTI. • Comprehend the requirements of allocating the claim liability to cohorts. • Grasp the requirements pertaining to the discounting of the claim liability. • Understand how PFBL will evolve under the new standard.

Description: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

Description: This webcast will walk through several statistical methods for imputing race and ethnicity from U.S. datasets, highlighting their strengths and weaknesses, talking about how to use them and evaluating their accuracy, and telling you where you can get code packages as well as a free dataset to try the methods out.