Description: CompAct May 2016

Description: Read the October 2016 issue of Expanding Horizons published by the Education and Research Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: The SOA announced the 2019—2020 James C. Hickman Scholars.

Description: The 2020 Dive In Festival was the biggest one yet, with more than 30,000 people attending the global celebration of diversity and inclusion in the insurance industry. The Festival featured more than 500 speakers presenting in 144 events in 35 countries across the globe on Sept. 22 – 24, 2020.

Description: The Society of Actuaries (SOA) is pleased to announce that The Actuary received recognition for excellence in two separate competitions.

Description: We stand for respect and equality. We are committed to doing all we can to ensure that our profession reflects the diverse perspectives, cultures and backgrounds of the members and candidates.

Description: The SOA launches a new newsletter powered by Artificial Intelligence

Description: Panelists will discuss ethical dilemmas for retirement actuaries in their day-to-day practices. This session will analyze whether ethical issues have arisen, how to avoid ethical dilemmas, and how to resolve the dilemmas if you find yourself in them. This session will also identify differences between the ethics rules of the Joint Board for the Enrollment of Actuaries and the Code of Conduct for members of the SOA and AAA. Audience members will be invited to participate in the discussion via chat.

Description: This webinar will cover the basics of structured assets, their uses in the insurance industry, and ALM and capital considerations. It will also include a section with an introduction to CLO Residual Tranches and recent capital discussions surrounding them.

Description: Over the last 10 years, Artificial Intelligence (AI) healthcare foundation models have made tremendous strides when it comes to delivering highly accurate predictive results when compared to traditional models. This is especially true for models developed and trained on the ‘language’ of healthcare. When trained on healthcare data sequences (claims, EHR, CPT, ICD, pharmacy, lab, etc.), these models deliver far more accurate results as to WHAT beneficiary-level health events will occur and even WHEN they will occur.