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: 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).

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

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: 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: 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 many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

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