Description: Yi-Ling Lin, FSA, MAAA, FCA, started the SOA’s Predictive Analytics Certificate Program to keep pace with the impact big data and predictive modeling is having on the health care industry

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

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

Description: Our firm has developed the first publicly available insured lives mortality table for Cambodia using data science methods. This essay provides a brief background, our approach and the impact of our work.

Description: An introduction to the methods and parameters for Bayesian Hyperparameter Optimization in a Life Insurance context.

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: Wright State University is a public research university in Dayton, Ohio, with approximately 11,000 students. With the lowest tuition fees in Ohio, the university has an access mission and plays a critical role in the region by offering high-quality, affordable education. I joined Wright State University in fall 2019 as the director of the Actuarial Science program. In spring 2020 we attained the Universities and Colleges with Actuarial Programs (UCAP)-AC designation from the Society of Actuaries (SOA), becoming one of only three universities in southwest Ohio at this recognition tier. In this article, I would like to tell you about the journey our program has taken—its successes and its challenges. The goal is to offer a resource to other universities seeking to upgrade their Actuarial Science programs.

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: Early in his career, Ehren Nagel, FSA, MAAA, scoured through podcasts and articles about predictive analytics, long suspecting this was a field with potential for him.

Description: Often actuarial practitioners are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes predictive analytic results are very fragile to the inclusion or omission of just a few observations in the sample. Data, particularly big data, are often "messy" and something must be done about it. What is the actuary to do about these very practical matters? These types of questions are what this presentation is intended to address. Concerns on missing data, outliers, nonlinearity, mixed-frequency, cross-correlations are illustrated using real datasets in the Asia-Pacific countries. Some possible solutions to the messy data problems will be discussed.