Description: A first look, and actuarial review of an electronic book for actuaries who wish to learn Excel beyond the basics.

Description: This article describes in detail how Microsoft Excel spreadsheets can be used to develop and test relational database projects.

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: A description of the issue's contents.

Description: Importance of bringing the DevOps mechanism in order to reduce the operational risks and to create a stable audit compliance practice in Actuarial world.

Description: This presentation is from the Digest of Informal Discussion, Transactions of Society of Actuaries 1955 Volume 7, Number 17. This discussion on technology reviews recent [as of 1955] uses of electronic data processing equipment and major computer installations. The speakers also look at the prospects of joint use of large-scale installations by two or more companies, and discuss specific actuarial activities that can be accommodated by electronic data processing machinery, such as calculation of dividends, cash values, and policy reserves.

Description: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

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.