Description: Actuaries are leveraging predictive modeling and machine learning more and more as they strive to provide greater value to their stakeholders. Actuarial standards of practice provide guidance around modeling and data. The world has changed significantly over the last few years, so it is important these models be tested for fairness as well as any statistical biases. This article addresses some of the key issues actuaries need to be aware of as Corporations and society work through the responsible us of this technology and underlying data.

Description: In the spring of 2019, Dr. Cai started to serve as the Actuarial Science Program coordinator at Salisbury University, a medium-sized public teaching school in Maryland. At that time, the program was small, with only two faculty members who focused on both statistics and actuarial science, with approximately 10 students. During the next five years, the program did make some achievements in curriculum development, student exam preparation, job placement, and recognition, but Dr. Cai continued to have a dream to make the program unique. Dr. Cai believed that the skill sets students learned, even from a small program, could enable them to compete with anyone on a global basis. In spring 2024, Dr. Cai advised a group of exceptional actuarial students to participate in the 2024 Society of Actuaries (SOA) Student Research Case Study Challenge (SRCSC), and the team won third place – —behind two teams from the University of New South Wales, Australia. This article is the story of the journey.

Description: This article starts with the increasing attention that FP&A receives due to the implementation of new financial regimes and emphasizes the collaborative nature of FP&A. It then describes the roles that actuaries play in FP&A, the new demands for actuaries that we are observing, and suggests some best practices that we are seeing across the clients. This article targets to point out the new demands from FP&A on actuaries and provide some advice on how actuaries can do to meet these demands.

Description: The paper demonstrates that the most popular rationalization for evolving glide paths is flawed and should be discarded. The paper also demonstrates that glide paths should generally evolve for investors that endeavor to optimize outcomes and reevaluate their portfolios regularly.