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).

Description: With the credit interest rate floor of a cash balance plan—a simple type of option identified by the Project Oversight Group—as a model, this research uses three valuation and risk analysis approaches to explore the existing techniques to value embedded options on an economic basis.

Description: This abstract describes a report that studies the social media response to three extreme tornado outbreaks in the Midwest United States.

Description: The essay describes an application of “Do Jumps Matter in the Long Term? A Tale of Two Horizons,” by Jean-François Bégin and Mathieu Boudreault, published in the North American Actuarial Journal online Jan 5, 2021

Description: Research material that developed a Liability-Driven Investment (LDI) benchmark framework.

Description: The report studies the impact of unemployment and underemployment on insurance business, introducing concepts, theoretical background, and patterns of both.

Description: Enterprise risk management (ERM) has grown fast in the 21st century, thankful due to a variety of extreme yet unfortunate risk events and regulatory changes. However, challenges continue with many other initiatives competing with ERM for resources within an organization. In this month’s newsletter, we revisit this old yet still important topic and bring a few relevant reports to your attention.

Description: This paper discusses some key challenges of embedding guarantees in DC plans, including setting an appropriate guarantee level, designing effective cost funding and risk management strategies, and fair treatment of plan participants with different investment strategies.

Description: The Society of Actuaries announces the release of a new report that will provide a resource for the insurance industry to learn about image recognition technology. Authored by Kailan Shang, the report explores the current status of image recognition technology, models for image recognition, and various possible applications of image recognition to insurance.

Description: This paper explores areas where fuzzy logic models may be applied to improve risk assessment and risk decision-making. It discusses the methodology, framework and process of using fuzzy logic systems for risk management. With the help of practical examples, it is hoped that it will encourage wise application of fuzzy logic models to risk modeling.