Description: Climate risk modeling is taking center stage. With businesses increasingly expected to show how they assess, adapt to, and mitigate the risks of climate change, companies are asking: What is the right climate risk modeling approach for our business?

Description: This article provides an alternative method to the computation of the Reduction in Benefits Test under 7702A. Part 1 provided for reduction in death benefits. Part 2 provides for reduction in Qualified Additional Benefits.

Description: Actuarial concepts of credibility can be extended to encompass additional information provided by new machine learning estimates. New statistical ideas also indicate how to generalize to blend arbitrarily many estimators, while not double-counting common information. For example, historical claim costs and risk scores both leverage diagnosis code information. We introduce two new estimators, one based on partial correlation and the other based on the machine learning-derived Shapley statistic, to address these limitations.

Description: This article updates the reader on the Doctoral Stipend process.

Description: The five-year moving average of the Actuaries Climate Index (ACI) reached a new high for the sixth consecutive quarter, according to the latest data release that includes summer 2019.

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: The article introduces two previously published articles.

Description: The session will discuss key areas of statutory reserving that contain some ambiguity or areas where there are simplifications being used in the industry. The content will be based on current practices used in the industry and possibly accompanied by an industry survey to be completed prior to the session. The focus will be heavily on CRVM, AG38, and PBR statutory frameworks and will include a Q&A portion for audience participation. Some potential topics will be reinsurance modeling, DBO3/ROP treatment, segmentation, deficiency reserves, system differences and multi-tiered designs

Description: Model design is an important part of being an actuary, and the SOA highlights the importance of the actuarial control cycle of model design, evaluation and monitoring. What tools are at our disposal for these steps? When should we be concerned or excited about model performance? Can we, and should we, use machine learning tools to assist with model improvement or development?

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"