Description: Increasing technology is available to create and disseminate educational resources worldwide. In response, this article advocates creation of open educational resources (OERs) that will benefit the actuarial profession with both recruitment and retention of members. Specifically, we describe OER contributions made at the University of Wisconsin-Madison and more recently through the ASTIN Academy, a working group of ASTIN, the non-life section of the International Actuarial Association. We advocate pushing boundaries and creating new ways of teaching effectively for the future. The innovative use of technology and open-source materials is a way to foster diversity, equity, and inclusion in the actuarial profession.

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

Description: Life and annuity markets around the world have seen increasingly complex investment-combined products, including universal life, fixed indexed annuities, variable annuities with a wide variety of guaranteed benefits, indexed variable annuities, etc. Insurance companies are facing unprecedented exposure to financial risks in addition to traditional mortality and longevity risks. The risk assessment and modeling for such complex designs by conventional methods often focus heavily on their market competitiveness and profitability. However, there seems to be a lack of quantifiable and systematic approach to assess investment risks associated with investment combined products. The objective of the proposed work is to develop a framework for quantifying and analyzing various forms of contractual designs and showcase different techniques to assess the trade-off between competitive contractual designs and their practical implications with regard to the difficulty of risk management.

Description: This presentation proposes a solution to the student actuarial paradox “I mastered the syllabus but failed the exam,” by utilizing the two-dimensional syllabus vehicle which pedagogically focuses on the two dimensions of content and problem difficulty. This presentation advocates the PMR characterization of three levels of problems difficulty: Plug-ins (easy), Monkey-wrench (intermediate), and Research-type(hard) questions. PMR was developed by examining difficulty levels in SOA exams, and problems of various software vendors. The author's students routinely score high on the SOA FM exam; it is hoped that these methods will assist other instructors in their quest for good pedagogy. Very briefly: “Plug-ins,” refer to multi-part problems, for example, calculating the price of an annuity that is first increasing and then level; “Monkey-wrench” problems specifically refer to plug-in multi-part problems where one component is a monkey-wrench, an issue the candidate has not previously encountered. It might be a non-typical modeling or it might be a non-typical algebraic simplification or it might be an unexpected integration of distinct modules in a manner that the candidate has not yet seen. “Research-type questions” refer to problems requiring some type of experimentation for example, looking at initial cases, seeing a pattern and then applying methods

Description: This article studies several pedagogical uses of tables, particularly as they apply to actuarial teaching.

Description: This article discusses the work of the Accreditation Implementation Task Force in developing accreditation standards for university actuarial programs.

Description: An article from the Expanding Horizons newsletter, issue 26, published in April 2003 that previews the Spring Meeting E&R sessions.

Description: A special issue of the Annals of Actuarial Science, focusing on insurance data science, was published on July 1. As explained by the editors—Katrien Antonio (KU Leuven), Christophe Dutang (Université Paris Dauphine), and Andreas Tsanakas (City, University of London)—the theme of this issue grew out of the rapid and transformative developments in the field, with the increased use of computational statistics, machine learning and artificial intelligence models in insurance applications. While the special issue has been associated with the Second Insurance Data Science Conference, held in June 2019 at ETH Zurich, the published papers come from a wider and very distinguished pool of authors.