Description: An overview of bias and fairness in machine learning models, as they apply to use cases in life insurance.

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: 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: This article provides an update, as of October 2023, on Financial Reporting Section research projects in process and those recently completed.

Description: In the United States, there has been much industry discussion of the challenges associated with a sustained low interest rate environment, which impacts both the asset and liability sides of the balance sheet in unfavorable ways. The intent of this article is not to focus on these strategic issues. Rather, we attempt to shine a spotlight on some of the modeling challenges that practicing actuaries may experience when incorporating negative interest rates into their models.

Description: Overview of guest editorials and special issues in the Annals of Actuarial Science, dealing with great societal challenges that the actuarial profession is responding to.

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: Accelerated underwriting expert, Ryan Holt, FSA, gives a brief history of the confusion matrix and how its use came into the accelerated underwriting space. An in depth discussion of estimating mortality impact using underwriting audits follows. Three key assumptions that effect mortality impact are discussed. Readers will leave with an understanding of how to estimate mortality impacts using audit results from any new under writing program as well as the considerations that should be taken around key assumptions. Background articles are provided for readers that are less familiar with the subject matter.

Description: Milliman was engaged by the SOA to conduct two Accelerated Underwriting (AU) surveys, one of direct companies and another of reinsurers. The surveys explored actual results from 2019–2021 and expected results from 2022.

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).