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

Description: End-User Computing (EUC) applications (such as Microsoft Excel, Microsoft Access, and others) provide a great benefit by allowing users to directly manage, control, and manipulate data. Unlike SAP, Oracle, and other Enterprise Resource Planning (ERP) applications that facilitate the automated and integrated flow of transactions and data, EUCs are neither ponderous nor difficult to modify. In fact, EUCs allow businesses and users to quickly deploy solutions in response to shifting market and economic conditions, industry changes, or evolving regulations. They can also help plug functionality gaps for ERP systems. Alas, those same elements that make EUCs so appealing (and vital in providing timely information) can also make them challenging to manage and control effectively.

Description: Summarizing the session "How to Keep Your Spreadsheets Out of the Headlines", on preventing spreadsheet error, from the 2015 SOA Annual Meeting. Presenters were Alex Cires, Sandra To, and Mary Pat Campbell.

Description: Comparison of open code and closed code actuarial modeling systems, focusing on advantages and disadvantages of each group.

Description: This article explains how to create more maintainable models using abstractions by relating it to option, machine learning and machine intelligence models.

Description: The author has gathered a variety of resources, general theories, and specific practices concerning spreadsheet errors and their avoidance.

Description: Feature article discussing new approaches to actuarial modeling.

Description: Presented at September 1995 Valuation Actuary Symposium. This session talks about the tools and methods for asset modeling, particularly mortgages and asset-backed securities.