Description: All things considered, caution needs to be applied to the use of so-called “standard” mortality tables. Sharpening up these blunt estimates by using individual assumptions reveals material differences in the structure of longevity, both between plans and within plans.

Description: A look at the COVID-19 crisis in 2020, and how it differs from both prior financial crises and pandemics. The COVID-19 pandemic gives actuaries an opportunity to assess different ways risk may interact, and thereby adjust the profession’s work for the future.

Description: This article takes a look at how defined benefit (DB) pension plan financials ended the third quarter of 2023 on a solid footing, improving the capacity to take meaningful de-risking actions; as DB plans look to de-risk at reasonable costs, they may find it beneficial to consider a broad range of investment options; and completing a core bond allocation with private credit and income-oriented alternative strategies can help achieve important savings over other frequently pursued de-risking approaches, without materially increasing the funded status volatility or downside risk

Description: A note to the section members from the outgoing and incoming section chairs.

Description: Discussion of the new scenario generator selected by the NAIC and its behavior at low interest rates.

Description: The article introduces a cashflow matching strategic asset allocation (SAA) framework for life insurers. Given the recent shift to higher interest rates after two decades of persistent low interest rates, insurers have an opportunity to enhance their asset liability management by rebalancing asset portfolios to narrow duration gaps and attain higher risk-adjusted yields. The framework aims to align investment objectives with economic, regulatory, and rating agency requirements. It prioritizes cashflow matching as the primary optimization goal and incorporates security-level asset information to assist portfolio managers in executing SAA seamlessly. The article highlights the benefits of the cashflow matching SAA framework, including greater attention to cashflow matching, unbiased results, readily executable strategies, and stable economic reserves. A case study is included to demonstrate the effectiveness of the framework in improving risk-adjusted return and cashflow matching for a representative liability block.

Description: “DeFi,” or decentralized finance, is an emerging financial system built on blockchains without central intermediaries such as banks. Defi has experienced explosive growth in the last couple of years, with billions of capital locked in numerous DeFi protocols on blockchains. However, DeFi is still relatively unknown outside of the cryptocurrency/blockchain sphere. Established actuarial practice areas such as insurance, employee benefits, banking, and investments all contain DeFi application opportunities for those interested in exploring them. This article aims to provide an introduction to DeFi concepts with a few practical tips.

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.

Description: While the major market crash subsequent to the widespread contagion of the COVID-19 virus was readily predictable, the rapid recovery and ongoing growth subsequent to it was not. Market dynamics around the recovery resist characterization by most traditional analytical approaches. In examining the circumstances around the emergence of a large retail class of investors, one sees that ingrained human tendencies, particularly optimism bias, have fueled much investor behavior responsible for the recovery. Understanding this makes it possible to predict potential future crashes and develop behavioral interventions for subsequent market recoveries.

Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).