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 summary and introduction of an SOA research paper that explores valuable insights and recommendations for insurance companies considering entering the market of commercial long-term care insurance (LTCI) in the Asia-Pacific, based on individual health status.

Description: In this article, a numerical comparison of two commonly used FIA valuation methodologies is conducted to help insurers decide on the best methodology to use under GAAP LDTI and VM-22. We compared two modeling approaches to project the future index credits with the annual point-to-point crediting formula: Tthe stochastic equity crediting model and the deterministic option budget crediting model. The analysis is then extended to different levels of option budgets and the monthly average crediting formula. The advantages and disadvantages of the two equity return projecting approaches are also highlighted and will be discussed later in the report.

Description: This paper uses the ideal quasi-natural experiment of government-subsidized agricultural insurance program phased out in different regions of China since 2007 to study the effects and transmission mechanisms of the program on alleviating regional poverty using a multi-period Difference-in-Differences (DID) approach. Specifically, this paper identifies the causal effects on the incidence, depth and intensity of poverty among rural residents. The research has important policy implications. First, this paper uses the program as a case to provide a research paradigm to estimate effects of implementing policy instruments that combine markets and government mandates. Second, as common prosperity is now a government imperative, the research also provides insights into ways insurance can be used to hedge income risks, alleviate poverty and help achieve more equitable distribution. The paper has been published on WeChat account of Chinese Association of Actuaries on February 9, 2022. The full version has been published in CSSCI journal Insurance Research, issue 11, 2021.

Description: An overview of how the VM-31 requirements for documenting PBR approaches and assumptions can/should reflect the Actuarial Standard of Practice on Modeling, ASOP No. 56

Description: This article provides an overview of activities that should be considered by an actuary when validating a predictive model. It describes several components to consider when validating a predictive model and provides an examination of various methodologies that can be implemented in the validation process.

Description: The Society of Actuaries' Pension Section and Health Section Research teams originally commissioned Professor Thomas E. Getzen of Temple University to construct a resource model for the projection of long term healthcare cost trends. The Society of Actuaries continues to make annual updates to the model to ensure its usefulness and relevancy.

Description: This article discusses key considerations and differences in measurement requirements between IFRS 17 and LDTI, leveraging a term insurance example to illustrate potential impacts on liabilities and, ultimately, earnings emergence.

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: Nathan Pohle and Darryl Wagner believe actuaries can benefit from new technologies in predictive analytics and life insurance.