Description: Discussion of the selling of life insurance to the middle income market through banks and with the support of new technologies and underwriting processes.

Description: Discussion of the new features that have been added to the Society of Actuaries' web-site.

Description: Details of an actuary’s project to help a local nonprofit agency improve its data analysis and customer service issues.

Description: Insurance regulations are undergoing a paradigm shift in determining capital and reserves. With Solvency II in Europe and principle-based reserving in the United States, the old formula-based static approach is being replaced or supplemented by a principle-based dynamic method. This change requires enhanced modeling, stochastic simulation and sensitivity analysis, which pose challenges to insurers whose infrastructure has not been built for big computation. This paper introduces a method of building complicated models with small reusable modules and running them on a many-task computing platform to achieve high performance with simplicity. The paper presents an example of a term life insurance model built to take advantage of computer hardware for parallel computing at the task level.

Description: This article discusses the benefits of the recent advances in technology. The author cautions that while these advances create demands that call for yet more advances, we need to think longer term to avoid future side effects like Y2K.

Description: This article explains why distributed computing systems have grown in popularity as well as the benefits and drawbacks of implementing them.

Description: Describes the Excel OFFSET function and demonstrates how it can be used to efficiently manipulate arrays of data.

Description: Read the March 2018 issue of Small Talk published by Smaller Insurance Company Section. Practical information on trends that impact sections and activities.

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.