Description: The COVID-19 pandemic has disrupted the global economy at a level never imagined. The economic fallout has been catastrophic. Record high unemployment, staggering revenue loss, bankruptcy and an uncertain future have businesses—and consumers—searching for a beacon of hope. In the quest for a lifeline, actuarial teams are quickly being thrust into the spotlight

Description: The winner of the “most unique use of technology” prize of the 14th Speculative (and Inspiring) Fiction Contest.

Description: Actuarial Process Optimization is a framework for using technology to support the actuary of the future. In this article, we discuss the capabilities of new technologies and explore examples where they can be used to aid with providing strategic business direction, optimizing skillsets and use of technology, and maintaining governance, control, and risk frameworks.

Description: An overview of the basic tooling and packages available for the Julia programming language, with a focus on why this is of interest to the actuarial profession. It covers some of the built-in language features as well parts of the Julia package ecosystem that make Julia ideal for modeling and data analysis. This is the second installment in a series of article aimed at actuaries. The first installment, Julia for Actuaries, was published in the October 2020 edition of Actuarial Technology Today, and covered the base language itself, its high-level, math-friendly syntax, and why the actuary of the future should consider its adoption.

Description: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.

Description: Feature article that contends in order for actuaries to thrive, technology needs to be central to company strategy and actuaries should view coding as a core competency—equal to or more important than other “traditional” skills.

Description: Mindful data visualizations show the end users the end way in how to analyze and interpret your data. More importantly, they can then use this data they see to drive decisions and modeling going forward.

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: Using Johns Hopkins data around the COVID-19 pandemic, this article explores the interaction between selecting a metric and the different ways of visualizing it.

Description: Feature article that supports the statement that actuaries who are competent coders will differentiate both themselves and the companies they work for. Coding ability will be useful no matter what tools are utilized every day and all of those tools and communities contribute to moving actuarial processes out of the “Spreadsheet Age.”