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: In this paper, the author uses simple risk portfolios to discuss the abilities and shortcomings of the current methodologies for Value-at Risk [VaR], and suggests methodologies which can be used to facilitate the aggregation of VaR measures.

Description: This paper suggests that risk is too complex to quantify with a single number, and offers a two-dimensional risk measure while introducing the concept of 'iso-risk' curves. From 2006 Enterprise Risk Management Symposium, M-AS06-1.

Description: This risk bulletin, published by the Society of Actuaries, provides insights and expertise on critical business issues from industry leading enterprise risk management actuaries.

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: This abstract describes a paper that studies the optimal reinsurance problem when risk is measured by a general risk measure. It is demonstrated that there is a “stable optimal retention” that will show no sensitivity, insofar as it will solve the optimal reinsurance problem for many risk measures, thus providing a very robust reinsurance plan.

Description: The National Association of Insurance Commissioners (NAIC) is moving forward to implement a new regulatory requirement that requires U.S. insurers to perform an Own Risk and Solvency Assessment (ORSA). Before developing a response to the ORSA requirement, insurers will want to understand its genesis and the underlying rationale for it, as well as its implications. This essay provides an overview of the evolution and rationale for ORSA, as well as practical implications for insurers as they begin to design an ORSA process. Part of the essay collection "Risk Metrics for Decision Making and ORSA"

Description: The direct and indirect effects of the pandemic have impacted many actuaries’ work since the spring of 2020. This article addresses three considerations actuaries should account for when using risk evaluation models which are focused on projecting remote, highly unlikely losses: model effectiveness, asset risk, and insurance (mortality and mortality) risk.

Description: Presented at June 1996 Spring Meeting. Inability to quantify the volatility in individual disability income insurance is one of the key reasons for loss experiences in the industry. Discussion covers: volatility measures for reserving, application of reserve adequacy criteria under various plan designs, application of volatility measures to pricing, and a test of the effectiveness of volatility measures.