SOA Podcasts - Society of Actuaries Society of Actuaries (SOA)

Listen in as host Jackie Lee talks with Joan Barrett about the recent research paper, “Calculated Risk: Driving Decisions Using the 5/50 Research.” The discussion and report will help you answer key questions like “what are the chances we will lose more than $1million.”

Join us for another exciting YPAC podcast as host Shawn Stender, FSA, MAAA, discusses unique actuarial career journeys with Lana Marozava, ASA, CERA, and Joe Long, ASA, MAAA. This podcast will shed light on the numerous different paths that young professionals can take on their journey to become an actuary. Whether that means changing careers from a teacher, project manager, or data scientist or just taking the exams at your own pace—this podcast will help young actuaries understand that there is more than one way to be successful in actuarial work.  

Join Frank Yeager, FSA and Chris Smith, ASA for an insightful discussion about leadership, the future of actuarial consulting, and the value of strong relationships. Frank shares his advice for aspiring and early-career actuaries gleaned over the course of a two-decade long career at Mercer that has culminated in his latest role as US Health Actuarial and Financial Group Leader.

Listen as host Jackie Lee talks with SOA President and Chair, Tim Rozar. They discuss Tim’s actuarial and volunteer background and the SOA’s goals for the year to come. Be sure to check out Tim’s Presidential address here: https://www.youtube.com/watch?v=4BG0AhwX6Ic  

Podcast on the industry adoption of emerging data sets across the insurance value chain. The participants include recognized leaders from business, regulation, legal, and innovation. The discussion covers current adoption practices, business opportunity, regulatory  and legal considerations.

Building on the discussion of individual decision trees in the prior episode, Shea and Anders shift to one of today’s most popular ensemble models, the Random Forest. At first glance, the algorithm may seem like a brute force approach of simply running hundreds or thousands of decision trees, but it leverages the concept of “bagging” to avoid overfitting and attempt to learn as much as possible from the entire data sets, not just a few key features. We close by covering strengths and weaknesses of this model and providing some real-life examples.