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: This article discusses how insurers that focus on their core competencies and astutely outsource other functions will become more competitive and improve their performance.

Description: This is an interview with Prakash Shimpi who talks about enterprise risk management, research and development. From News Direct, September 2005, Issue No. 51.

Description: Highlights of Society of Actuaries research efforts in the financial services practice area.

Description: Many experts tout the benefits of index investing, but history tells us that when enough investors do the same thing it tends to end badly. Stay tuned!

Description: Investment Fallacies: The "Dollar Bill" Fallacy

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).