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: Description of methods for extracting and reformatting the existing mortality tables using R. Methods and formats for storage are discussed in the second article. Intent is to generate discussion regarding the format of our stored mortality tables. Value is in the demonstration of task automation in R and in the reduction of duplicate work across companies related to reformatting tables.

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: 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: A description of the operation of an R package for experience studies. Premium patterns, lapse rates, and mortality rates can be calculated.

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

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

Description: Principal component analysis is an unsupervised linear transformation mainly used for dimension reduction. Determine key numerical variables with maximum variances in a dataset, identify the correlation and removal of redundant variables are the key aspects of this exploratory data analysis.

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