Description: "A crucial importance in forecasting is to be able to distinguish cases when temporary slowdowns in health care utilization are followed by a “rebound” period of higher utilization. Come explore an analysis of a new mathematical model describing the return-of-care process. By means of simulation and case studies using the R programming language, the relationship between the visibility of these effects and the endogenous variance of the utilization time series is explored. After attending this session, you will be able to: Describe the parameters and mathematical form of the model Apply the model to real-world utilization data"

Description: Increasing technology is available to create and disseminate educational resources worldwide. In response, this article advocates creation of open educational resources (OERs) that will benefit the actuarial profession with both recruitment and retention of members. Specifically, we describe OER contributions made at the University of Wisconsin-Madison and more recently through the ASTIN Academy, a working group of ASTIN, the non-life section of the International Actuarial Association. We advocate pushing boundaries and creating new ways of teaching effectively for the future. The innovative use of technology and open-source materials is a way to foster diversity, equity, and inclusion in the actuarial profession.

Description: A significant fraction of insurance risk is characterized by the tail of the claims distribution, where we have much less data to model probabilities accurately. Ignoring the tails can lead to underpricing of tail risk and mispricing. Estimation of the tails can be complex due to a bias-variance tradeoff--the further out we go into the tail the more accurately we can model the extreme tail (lower bias) but the less data we have (increased variance). The statistical tools of Extreme Value Theory can be leveraged to more accurately estimate both the incidence and the severity of extreme events given that they occur. using the Generalized Pareto Distribution.

Description: A description of Data Science best practice tools and how they can apply to general actuarial work.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: The famous 80/20 rule in data science states that only 20% of your time working with data is spent driving insightful business analytics. The rest of your time is spent trying to query, clean and organize your data. Even with this knowledge, the majority of actuarial training/focus is on the 20 percent. This session takes a different approach, focusing solely on speeding up your work on the other 80 percent. Practical examples will be part of a code walkthrough in R.

Description: Editorial for the May 2019 issue of Predictive Analytics & Futurism newsletter.

Description: Dive into an overview of potential customer value propositions, trends in consumer acceptance and adoption, solving the data portability issue, potential impacts of emerging partner ships with OEMs and more within the auto insurance industry during our latest webcast. Experience interactive dialogue that will allow another dimension of thinking as industry experts provide insights and expertise. Throughout this webcast, you can expect developments in the personal lines automotive telematics-based insurance landscape that will develop a further understanding of the present and future of this industry.

Description: Discover the impact of COVID-19 on member costs post diagnosis and the future of mortality in this Emerging Long Term COVID-19 Trends webcast. This webcast will be split up into Health and Life portions. During the Health portion of this webcasts, you will learn more about the impact on risk scores due to recent findings. Enjoy this discussion and further dive into the emerging COVID-19 trends that may affect how you conduct business practices. At the Life portion, the SOA Research Institute assembled a panel of mortality experts who will express opinions on the effects of the U.S. mortality rate for general, insured and pension populations in the short-term.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.