Description: Summary of industry practices on assumption governance, trend, challenges, and opportunities.

Description: report on the IAA

Description: This article discusses update in the last two years to Solvency II.

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: Feature article discussing variable annuity reform.

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: 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: With coinsurance of an existing block of long-duration insurance contracts, there may be existing reinsurance that remains in force. The new coinsurance is typically net of such “inuring reinsurance.” In this article, we look at approaches that the new reinsurer might apply to account for the effects of inuring reinsurance.

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.