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

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.

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

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

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