Description: Explores the open vs. closed systems debate with regards to actuarial modeling and exposes the over-simplification. Valuable for anyone who uses models.

Description: Mindful data visualizations show the end users the end way in how to analyze and interpret your data. More importantly, they can then use this data they see to drive decisions and modeling going forward.

Description: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

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: In year’s past, we presented the Practical Predictive Analytics Seminar (PPAS) as an add-on to the Life and Annuity Symposium or attached to the Predictive Analytics Seminar, this year it will take place as a stand-alone virtual Seminar.

Description: Using Healthcare Claims Data to Create Actuarial Models.

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: Possible usage of tools that can automate time consuming processes, and lead to a different team structure and organization for predictive analytics team.