Description: 2022 Welcome and Strategic Themes for the Marketing and Distribution Section Council.

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: Zero-Knowledge Proofs (ZKPs) represent an innovative suite of cryptographic algorithms that empower the insurance community to validate the accuracy of sensitive information without revealing the actual information itself. Mutation with the use of non-traditional data entering the insurance processes of customer acquisition and in-force management is currently taking place. A shift toward using non-traditional data in insurance customer acquisition and in-force management processes is currently taking place. This evolution is captured in the Australian market through surveys and interviews. Come and join us to learn about these two new research studies that open up the possibilities for the insurance industry to be more efficient in delivering its value to the society.

Description: We will share the latest research from the Actuarial Innovation and Technology (AIT) Strategic Research Program, with a focus on research relevant to health actuaries. Attendees will learn about leading-edge technology that has impacts on actuarial functions, both broad and specific innovations and breakthrough technology, and the resulting impacts.

Description: En el seminario hablaremos sobre los nuevos flujos de tarificación, y nos enfocaremos en la inclusión del análisis geográfico en el proceso de modelado para optimizar la extracción del riesgo en modelos de Machine Learning.

Description: Hardly a day goes by without a feature story about the latest reverberation from the expanding use of Artificial Intelligence/Large Language Models (tools) in a variety of settings. The pace of use (and misuse) of these tools, such as ChatGPT and Bard, has been explosive and shows no sign of abating. Many experts would argue we are on the cusp of a permanent paradigm shift in teaching/academics and overall application of technology. With these issues in mind, the Society of Actuaries (SOA) Research Institute’s Aging and Retirement program is interested in an exploration of this topic from a variety of perspectives. The result of this effort is intended to provide a useful resource for readers to become well-grounded on the issues surrounding the impact of these tools in a retirement context and set the stage for future research.

Description: The document is a research call for proposals seeking researchers to produce a report on the uses of synthetic medical claims data for actuaries. It outlines the background, purpose, research objectives, proposal requirements, selection process, and conditions for submission.

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.