Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.

Description: Actuaries understand the technical benefits of converting their models to a modern actuarial system, such as new capabilities and upgraded technology. This article highlights additional, non-technical benefits that also come with model conversion. These benefits include advancements in attitudes and capacities among the team and across the company.

Description: How do you make model output media worthy? This article explores considerations that may contribute to the mass production of model results for public consumption.

Description: Read the April 2020 issue of The Modeling Platform, published by the Modeling Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.