Description: Read the March 2019 issue of The Financial Reporter published by the Financial Reporting Section of the Society of Actuaries.

Description: Summary of the SOA research project on PBR Simplified Methods. Describes the multi-risk scenario generator, central estimate assumptions, assumption objectivity measures, and regulatory demonstrations per VM-20.

Description: An overview of the NAIC Variable Annuity Reforms, including a discussion of perceived positives and challenges associated with the reforms.

Description: Feature article discussing the volatility of reserves due to FASB changes.

Description: Feature article discussing variable annuity reform.

Description: Feature article discussing how the insurance industry can become more data driven.

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.

Description: Read the July 2020 issue of The Financial Reporter published by the Financial Reporting Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: This article provides a summary of the research paper and Excel-based tool that can be found on the SOA’s website at Regulatory Capital Adequacy for Life Insurance Companies: A Comparison of Four Jurisdictions | SOA.

Description: In this article, we introduce 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.