Description: Segregated Fund is a special investment fund to provide capital appreciation with embedded insurance features. The traditional methodology to estimate the capital reserve and pricing of contracts goes to Monte-Carlo based stochastic models due to its complexity. Recent research has introduced a deep learning model, Long Short-Term Memory (LSTM), to help cash flow projection for a Segregated Fund in its whole lifetime horizon. In this paper three new deep learning models are presented: Long Short-Term Memory with attention (LSTM-ATTN) to estimate the liability reserve and pricing Segregated Fund contracts, Wasserstein Generative Adversarial Network (WGAN) for stock return forecasting and Temporal Convolutional Network on GAN (TCN-GAN) for interest rate time series generation. As an example, the cash flow projection and Economic Capital for Segregated Fund portfolio are used to compare deep learning models against traditional models in terms of accuracy and computation efficiency.

Description: In this episode, Joe Alaimo interviews Jeremy Levitt to explore the critical role of model validation in actuarial practice—what it is, why it matters, and how to do it effectively. From technical procedures to strategic impact, we break down the components of a strong validation framework and discuss how emerging technologies like AI are reshaping the landscape.

Description: In this episode, Joe Alaimo interviews Bob Leach to introduce us to the article he has written for the emerging topics community: Model Validation 101: Bring your Calculus Mindset!. Bob describes how the calculus concept of differentiation can be used to validate results from actuarial models.

Description: Al is reshaping the actuarial profession at an unprecedented pace. This article explores how emerging technologies—machine learning, generative AI, and automation—are transforming actuarial practice across pricing, reserving, and risk management. It highlights opportunities for actuaries to harness AI for deeper insights, enhanced predictive power, and more efficient workflows, while also addressing the challenges of transparency, bias, and ethical responsibility. The discussion situates AI within the broader context of professional standards and regulatory expectations, emphasizing the need for governance frameworks that safeguard trust and accountability. Drawing from real-world case studies and current research, the article examines how the actuary’s role is evolving from traditional modeling to strategic data leadership. It calls for continuous learning and interdisciplinary collaboration to ensure that actuaries remain central to the future of risk and finance. Ultimately, this article offers a balanced roadmap—one that recognizes AI as both a disruptive force and a catalyst for professional reinvention.

Description: For 20 years the Education and Research Section has given awards to the top graduate student speakers at the annual Actuarial Research Conference. This article provides the motivation, its evolution, lessons learned that can apply to all who give presentations, and why 2025 was the final year for the awards.

Description: This article introduces 'Online Quick Case Discussions,' a pedagogical model for integrating professional skills into Exam P and Exam FM courses.

Description: Discusses the market, demographic, and macroeconomic factors that affect actuaries' choices for the assumptions they use in the pricing and valuation of many insurance products.

Description: At the E&R section breakfast at the Annual Meeting on Monday, October 13, Tom Edwalds handed over the (notional) gavel and the keys to the E&R safety deposit box to me, as the new section chairperson. I am honored to be able to lead the section in the coming year.