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

Description: An autobiographical journey from actuarial student to data scientist.

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: 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.

Description: "Discuss research conducted at the Cleveland Clinic to understand levels of anxiety and depression across Northeast Ohio. Specifically, learn how advanced analytical techniques were utilized with social media data from Twitter and Google to estimate the need for psychological support by geography and overtime. The panel will go further to demonstrate how natural language processing (NLP) and machine learning could be used to make operational decisions at a leading health care provider."

Description: The advent of machine learning algorithms has taken predictive analytics and risk scoring to a new level. From gradient boosting machines to k-nearest neighbors regression, the use of these techniques has gained significant traction in healthcare - including health systems like the Cleveland Clinic. This session will discuss the use of predictive analytics to go beyond just assessing the risk of a patient, and instead focus on prescribing a specific intervention of care. In practice, the recommendation of a care plan goes beyond just attributing a score to a patient. It provides physicians and care coordinators the most probabilistic options - e.g. referral to social worker, depression screening, weight-loss program - for positively impacting a patient's future healthcare outcome.

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: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

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

Description: Chairperson's column for the May 2019 issue of Predictive Analytics & Futurism.