Description: Comprehensive actuarial guides.

Description: Discussion on modeling Medicare Supplement insurance and the considerations when choosing an Actuarial Modeling Software (AMS). Focuses on model risk management, key modeling assumptions and inputs, and the complex interactions between modeling assumptions and why its critical to have a model that can handle that complexity. This article also touches on the incorporation the of accounting and regulatory regimes into the model, including Statutory, GAAP (Legacy and LDTI), and Tax regimes, and the modeling considerations for sensitivity testing and capital planning. Lastly, the article highlights the capabilities to look for when choosing an AMS for Medicare Supplement.

Description: Listen in as Anders Larson, FSA, MAAA, interviews Shea Parkes, FSA, MAAA, and Michael Niemerg, FSA, MAAA, about artificial neural networks. Artificial neural networks have gone through a recent renaissance as part of deep learning. Artificial neural networks can now provide amazing accuracy, but they come with an equally amazing amount of complexity. This podcast provides a very high-level overview of everything from their history to modern implementations.

Description: Actuarial Process Optimization is a framework for using technology to support the actuary of the future. In this article, we discuss the capabilities of new technologies and explore examples where they can be used to aid with providing strategic business direction, optimizing skillsets and use of technology, and maintaining governance, control, and risk frameworks.

Description: Listen now as April Shen, FSA, CFA interviews Tom Peplow (Principal, Director of Product Development - LTS) on the interaction between actuarial modeling and big data. In this episode, Tom discusses the basic concept on data warehouse, tools and resources actuaries can use to learn more.

Description: This article explores some key challenges facing the modeling actuary related to LDTI implementation and shares associated compliance and modernization considerations.

Description: A review of opportunities for using machine learning and artificial intelligence in group health underwriting including cost predictors and predicting behavior and decisions.

Description: Best practices for a successful model conversion or transformation. It details the key stakeholders involved and the processes and tasks across various stages of the model conversion.

Description: This is a historical account of the predictive analytics Hack-A-Thon sponsored by the SOA.

Description: Irish actuary and professor explains his hypothesis that actuaries, as practitioners, are reluctant to adopt the theories of financial economists unless the models on which the theories are based fit the actual data the actuaries have.