Description: Setting the X Factor Percentile— Commentary by G. Thomas Mitchell from The Financial Reporter, October 1999, Issue No. 40.

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

Description: An update on SOA research relevant to the Financial Reporting Section.

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: 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.

Description: Results of an SOA survey regarding the expectation for 2015 accruals for 3Rs and cost sharing reduction payments.

Description: The CERA pathway is an avenue for a select group of Society of Actuaries members who have demonstrated expertise and substantial experience in the field of enterprise risk management to obtain this new credential without completing the examination requirements.

Description: Chairperson’s column discussing the Section's 2009 focus on continuing education, research, PBA, and international issues.

Description: This article lays conceptual foundations associated with projecting investment-related discretionary elements of various life insurance and annuity products. It outlines potential implementation approaches that may be appropriate when calculations are performed in a risk-neutral setting. The article concludes that crediting rates in risk-neutral models can be projected as in real-world models, except that the modeled provision for expected credit losses should be modified to equal real-world expected losses initially, but over the projection reflect management’s evolving expectation, moving ultimately to risk neutral expected losses.