Description: Summary of Administration's FY 2023 Tax Proposals of Interest to Life Insurers

Description: This article is an interview of Stuart Klugman, senior staff fellow for Education at the SOA, who explains the changes to the pathway to earn the ASA designation.

Description: This article summarizes the second Simon Conference for young researchers in Actuarial Science, Risk Management, and Insurance, which took place at Michigan State University last November. The intent is to publicize the event and promote future participation in similar conferences of young researchers. The article will contribute to expanding the horizons of actuarial research performed by the academic community. Participation in the conference also gives professional development opportunity for practicing actuaries.

Description: A discussion of the process and results report “A Study of the Use of the Delphi Method, A Futures Research Technique for Forecasting Selected U.S. Economic Variables and Determining Rationales for Judgments.”

Description: This article is an update on the educational and qualification 2005

Description: The article describes recently proposed regulations on the deduction of partially-worthless debts under section 166. It also provides background on the comments submitted by the insurance industry in response to the proposed regulations and additional guidance issued by Treasury and the IRS following the publication of the proposed regulations.

Description: Accounting Standards Update No. 2018-12, Financial Services – Insurance (Topic 944): Targeted Improvements to the Accounting for Long-Duration Contracts (LDTI) has been effective for most large, public business entities since 2023 and will be effective for other insurers in 2025. While the guidance clarifies the need for incorporating claim liabilities and premium accrual balances in the liability for future policy benefits (LFPB) under LDTI, companies may still have questions given there are limited details available around the techniques used in practice. In this article, we demonstrate the need for incorporating claim liabilities and premium accrual balances in the LFPB under LDTI. We also look at two potential approaches of incorporating these accrual balances.

Description: In recent years, vast amount of data from emerging experiences has helped insurers gain a deeper understanding of their policyholders and the factors driving their behaviors. However, due to limitations in traditional experience study methods, this understanding typically remains at the cohort level. This series of articles proposes a novel machine learning-based model that analyzes and predicts policyholder behaviors at the individual policy level. Policyholder embedding representations are learned using a Recurrent Neural Network (RNN) that takes into account the sequence of historical transactions associated with a policy. Additionally, a Neural Network with a multi-head architecture is trained to predict the policyholder's next action based on the policyholder embedding and other policy attributes. The resulting model offers several advantages over traditional experience studies and assumption-setting methods, including the ability to set policy-level assumptions that improve reserve accuracy, predict multiple behaviors simultaneously while capturing the interrelationships between them, and generate policyholder embedding representations that are useful for segmentation.