Description: The article provides an update on the 2019 ERM Symposium with regard to date, location and content (General Sessions and Concurrent Sessions).

Description: The article provides an update on the 2019 ERM Symposium with regard to date, location and content (General Sessions and Concurrent Sessions).

Description: Back in the day, when data was scarce and computers were slow, actuaries were forced to use simple methods to account for risk. By far, the most common method was to add a margin, usually 5% to a “best estimate” calculation. Remarkedly, this method still works quite well in the aggregate. The problem is that, in many cases, decisions today require a more in-depth analysis. For example, how big a risk is a health plan really taking if it lowers its premiums by 10% to attract more members? This session will discuss techniques for measuring risk in today’s environment. Speakers will also share the results from the 5/50 research project, which forms the foundation for the techniques.

Description: This article is part of a 2 part series on real options analysis (ROA). Before you can understand the limitations of ROA, you must first understand the difference between risk and uncertainty. This article goes into detail explaining the vast differences between the two phenomena.

Description: Risk management techniques do not include system science. System science requires that the uncertainty of the world is incorporated into the results. The articles explains the difference between risk and uncertainty. Learn how risk practices create an overoptimistic view of future outcomes.

Description: Model Risk Management continues to be an evolving area for insurers to be considering. This has grown from both internal and external forces and may require a different format to previous model risk frameworks held by insurers. Furthermore, there are a number of trends emerging that we believe will drive an increased focus on insurer’s model risk management frameworks such as technology automation, ever increasing data sources, and the use of predictive models. Risk management and the development of models is an area where insurance companies have performed well in the past. It is expected that frameworks currently exist within companies; the governance, documentation, validation and control of model risk are likely to be well established practices. The challenge that insurers must consider is how well these have been adopted throughout the company for all models used to make business decisions and if the same level of rigor has consistently been applied. Regulation in a high-velocity actuarial environment will to be a challenge that actuaries must spearhead and must be proactive in guiding rule making to ensure continued innovation while putting data security and risk management first.

Description: Duration matching is a risk management strategy, commonly used in the insurance industry, where the duration (price sensitivity to a yield curve movement) of an asset portfolio is adjusted to be made equal to the duration of the liability. However, this approach has flaws. In this essay, I note some key failures of duration matching and offer a potential alternative.

Description: Risk management techniques do not include system science. System science requires that the uncertainty of the world is incorporated into the results. The articles explains the difference between risk and uncertainty. Learn how risk practices create an overoptimistic view of future outcomes.

Description: Duration matching is a risk management strategy, commonly used in the insurance industry, where the duration (price sensitivity to a yield curve movement) of an asset portfolio is adjusted to be made equal to the duration of the liability. However, this approach has flaws. In this essay, I note some key failures of duration matching and offer a potential alternative.

Description: This is the second part of a 2 part series on Real options analysis (ROA). This article looks into the limitations of using financial option theory when doing real options analysis with projects that are long-dated and uncertain, such as: 1. First-degree homogeneity, 2. Uncertainty must derive from Brownian motion, and 3. Risk neutrality.