Description: This is the key findings research report for the sixteenth Emerging Risk Survey.

Description: Because of the growing significance of Enterprise Risk Management, it is critical to consider how ERM responds and relates to circumstances and conditions. In particular, one megatrend currently impacting our world and our understanding of the economy is the emergence of a behavioral economics perspective. This paper describes the nature of behavioral economics, and considers the potential impact of such a perspective on ERM. From Actuarial Research Clearing House, 2012, Vol. 1.

Description: The interactive visuals provided a compelling demonstration for Allstate's leadership team to see the effects of changes to Allstate's risk profile.

Description: Summary of April 2020 Investment Section Town Hall and come additional commentary.

Description: 2011 Enterprise Risk Management Symposium, Chicago. This paper explores cognitive and behavioral theories that can be applied to explain why emerging risk is often overlooked, downplayed or ignored. It also proposes an innovative framework for managing emerging risk within an overall enterprise risk management program.

Description: This paper provides a summary of the Dec. 2012 summit of the Natural Resource Sustainability Group and invited experts.

Description: The CIA, SOA and Joint Risk Management Section of the CAS, CIA, and the SOA sponsored a whitepaper on the application of ERM at a federal government level.

Description: Pricing is a central activity of actuaries to assure solvency and to manage risk. Life insurance is also a social good, not just a product, and the US population is massively underinsured. Pricing must strike a balance. Unlike a simple 4x4 matrix to produce pricing, today's technology offers the ability to evaluate literally thousands of variables, enabling a kind of precision pricing. It also heightens the possibility of introducing bias and discrimination through the use of unregulated third-party data.

Description: Insurers are increasingly using advanced analytics to model expected outcomes. Two well-known examples in the life insurance industry are models for predicting policyholder behavior and to manage risk selection. The presenters will explore the various types of advanced analytics being considered in models and how the associated risks of these models can be incorporated into an enterprise's risk framework.