Description: This article discusses the publication of the “Encyclopedia of Quantitative Risk Assessment”

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: Feature article discussing image recognition and how it may affect actuaries in the future.

Description: The predictive analytics application growth in the Asia-Pacific region has been gaining momentum.

Description: The predictive analytics application growth in the Asia-Pacific region has been gaining momentum.

Description: Many executives travel now with only a single mobile device. These tools generally do not replace your desktop or even your laptop look at them as extending the reach of your physical office. Tips for using these mobile devises are given in this article.

Description: This article focuses on the recent surge of digital medicine data being seen in the healthcare industry. While an increase in data can generally be perceived as good, it is important to remember that current privacy practices need to be maintained, if not strengthened, to continually protect healthcare members. The COVID-19 pandemic helped fuel digital medicine data, but it also created new avenues for privacy to potentially be compromised (i.e. healthcare employees using personal devices while working from home). Digital medicine data is a very powerful asset, but it is also a massive responsibility. If mishandled, this data can cause more harm than good.

Description: The author notes the decline in the study of compound interest and the effect of computers on the subject.

Description: Read about The Bulletin Board Updates on important events related to the Technology Section.

Description: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.