Description: Article describing content and key takeaways from experience completing the SOA PA Certificate

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: This is a 4-hour predictive analytics competition. It will be developed by the PAF and Modeling section. The competition will use publicly available data and ask participants to predict the outcome. The person with the best prediction will win the competition. The competition can be done on an individual for team basis. We don’t allow more than 4 people on a team.

Description: Health claims are not normally distributed. Claims Forecasting can be improved by understanding the entire distribution of plausible values of future claims, for example knowing there is an 80% chance future claims are between $340 and $375 PMPM. Confidence intervals around our estimates allow us to understand for each range around the best estimate (for example +/- 1%) what is the probability that future claims will fall in that range. Quantile Regression is a mathematical technique where you can estimate any percentile of the claims distribution you are looking for as a function of demographics, diagnosis indicators, prior cost variables, or other significant predictors. You can see which coefficients are important at different parts of the distribution. Individual and group distributions can be constructed. There are numerous applications in pricing and underwriting, stop loss, provider profiling, ACO target setting, allocation of group risk charges based on volatility, discounting strategy, and experience analysis.

Description: As actuaries in health care, we spend most of our time focused on reviewing claims, enrollment and clinical data. That information is captured while patients are in health care facilities. Yet most people spend only a small fraction of their time in these facilities. This article explores what types of data are available to actuaries the provide a more holistic view of the member so that a members physical and emotional needs, the social determinants of health can be taken into consideration along with their clinical needs. Two uses cases are presented so actuaries can see how these types of analyses can be leveraged to improve clinical, operational, and financial performance of risk bearing health entities. Understanding the customer (member) not just the patient enables actuaries to provide new insights and values to our stakeholders across a wide range of business and risk applications.

Description: Discover the key components of value-based care (VBC) contracts and their impact on provider credibility. Learn about benchmarks, measurement period results, and the unique challenges providers face in managing insurance risk. Gain insights into reducing costs and aligning financial incentives in the US healthcare market.

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.