Description: Read the April 2017 issue of CompAct published by the Technology Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: This article explores the possibility of using machine learning to improve, or potentially rewrite, actuarial code within actuarial software.

Description: This article highlights business use cases where XTbML can define the tabled data in actuarial tables.

Description: The author, from the United Kingdom, as a member of the Institute’s working group that devised LAN, the Linear Actuarial Notation. The author notes this article is a supplement to The actuary’s ‘IAN, LAN & CAN’ article in the November 1980 issue. The article discusses this actuarial notation system, designed for use in the computer age.

Description: This article is about the emergence of the financial modeling process in the actuarial tool kit.

Description: This article contains reviews of the following books published in 1960: 1. 'Ensuring Medical Care for the Aged' by M. Spiegelman, 2. 'All Lines Insurance' edited by D.McGill, 3. 'Alternative Methods of Financing Old-Age, Survivors, and Disability Insurance' by J. J. Carroll, and 4. 'Installing Electronic Procedures-A Progress Report' by J. J. Finelli. Notices of other articles and publications are also included. From Transactions of Society of Actuaries 1960, Vol. 12, No. 34.

Description: The article discusses the use of R in prototyping automation procedures. It offers general pros and cons for R and walks through a specific use case, which involves scraping and summarizing data stored in Microsoft Excel.

Description: This article explains the importance of centralizing modeling departments. It goes further and explains the importance of making all aspects of modeling reusable. It explains the consequences of decentralization.

Description: This session from the 1984 SOA Toronto Meeting discusses microcomputers, uses in the insurance industry, programming languages, using micros as an actuarial and agency tool, using micros for preparing policy illustrations and other computer applications. From Record of the Society of Actuaries Vol. 10, No. 4B

Description: As any international traveler who has tried to plug in an electronic device knows, the human race is great at devising many different ways to accomplish the same task. Predictive modeling is no different. Data scientists can choose from a wide variety of languages to prepare data, fit models, and verify results. The Rosetta Stone, which showed writing composed in three languages side by side, was instrumental in translating unfamiliar Egyptian hieroglyphics into the more common Greek language. This presentation follows a similar approach by showing the same predictive model implemented in the following languages: R, Python, Spark, MatLab, and SAS. Participants will compare and contrast each of these languages and, by doing so, build a greater understanding of those languages less familiar to them.