Description: An introduction to the Product Development Section's new subgroup supporting interested in the practice of in force management.

Description: The ideal business situations for securitization would be those where a company faces significant risks or has significant growth opportunities and where conventional sources of risk hedging or financing are not available or are too expensive. Ultimately, securitization should be able to provide hedging and/or financing that is more effective and less expensive than the alternatives.

Description: This article discusses the pending book entitled 'Dynamic Risk Modeling Handbook.' The proposed scope and outline of the book is reviewed. This is a revision of the 'DFA Handbook' which was published in 1995.

Description: ongoing discussion of the proper interest rate to use in discounting liability cash flows to reflect credit risk of the insurer in a fair value accounting system

Description: Discusses the recent developments with RBC requirements. At the forefront of the RBC changes is the stochastic exclusion test.

Description: Read the September 2020 issue of Risks & Rewards published by the Investment Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Case Study for Fall 2009 Exam AFE.

Description: The COVID-19 pandemic infection has greatly affected the economy, resulting in volatile market conditions. Because market conditions greatly impact the value of insurance company’s assets and liabilities, the fourth survey in this series focused on best practices around asset and liability management (ALM). The survey was directed at actuaries, investment managers and risk managers directly involved in assessing the impact of recent events on ALM. Responses to the survey were collected between April 22-28, 2020. Thirty-two companies responded to the survey.

Description: ALM #2 Survey Questions

Description: The Society of Actuaries’ Committee on Finance Research is pleased to make available a research report that develops a framework for applying deep learning and reinforcement learning techniques to optimal dynamic strategic asset allocation for Liability Driven Investment (LDI).