Description: This abstract describes a paper that introduces spatio-temporal models and methods for analysis with specific emphasis on quantities of interest in actuarial science. Generalized additive mixed models are used for the analysis of geographic and temporal variability of mortality rates.

Description: A review of the Johns Hopkins Data Science Specialization courses with special emphasis on how they may be useful to actuaries.

Description: Techniques that have gained some traction in general (property/casualty) insurance will be highlighted with a particular focus on the linear mixed model. This model adds random effects to the more commonly seen fixed effects (for example, as used in GLMs).

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: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.

Description: Python is the leading programming language for data science. Python programming is one skill that actuaries should acquire. It facilitates analytic work, and the available visualization tools help to communicate ideas. In this article, I use a traditional actuarial problem, mortality modeling, as an example.

Description: Private LTC insurers are paving the way for value-based care through working to implement care management programs that improve the lives of their policyholders before and during a claim. However, measuring the effectiveness of these new programs is a challenging task. In this article, we will explore powerful tools that can enhance traditional effectiveness studies and address some of their existing weaknesses.

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.

Description: This chapter introduces a few probability distributions that play an important role in understanding and analyzing healthcare data. While theoretical in nature, these distributions model real life if appropriately chosen. Probability distributions are mathematical representations of how statistical data behave.