Description: Summarizes financial reporting regulatory developments from April - June.

Description: Chairperson’s column discussing the section’s accomplishments during 2010.

Description: Announcement of the winners of the awards for Financial Reporter OutStanding Treatises - FROSTies - and Financial Reporter Uniquely Memorable Papers - FRUMPies - and what they received.

Description: Update on IASB developments from January - March 2010.

Description: Feature article discussing accounting for embedded derivatives.

Description: The 2020 edition of the Valuation Manual permits mortality data aggregation thereby enhancing credibility but also has accompanying requirements and addresses calculation of company experience rates.

Description: ASU 2018-12 introduced a new concept called “market risk benefits” (MRBs). MRBs are a new accounting classification for benefits within deposit contracts, offering protection from “other than nominal market risk.” This applies to products with account values, including variable and indexed annuities with guaranteed living or death benefits attached (aka GMxBs). ASU 2018-12 Section 944-40-30-19D mentions both a non-option and an option-based valuation approach to account for MRBs. This article considers the option-based approach. The purpose is to highlight weaknesses in the current application of the option approach to MRBs, propose an alternative method, and consider the benefits of the new approach. The intent of this article is to persuade practictioners / actuaries / accountants that this is a better approach for applying the fair value option method to market risk benefits.

Description: This article was first published in 2010 when accounting standards codification (ASC) was new. An explanation of the codification system is given.

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.

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.