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 research report studies the social media response to three extreme tornado outbreaks: May 22, 2011 in Joplin, MO, November 17, 2013 in Washington, IL and April 9, 2015 in Rochelle, IL. It uses Twitter data with a 14‐day period for each tornado event with a focus on a 24‐hour period surrounding the event. It starts with an overview of the data followed by analysis of geolocation, insurance related user influence, social network, and sentiment.

Description: This report studies the impact of unemployment and underemployment on insurance business. It introduces the concepts, theoretical background, and patterns of unemployment and underemployment. It also touches on economic policies that have been used to improve labor market conditions and maintain the market’s stability.

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: 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.

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

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).