Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

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: Economic risk analysis has two dimensions: time and frequency. Asset return varies by time because of economic cycles and economic structural changes. Risk is higher during economic recessions and smaller during economic expansions. In addition, different economic structures may exist at different time scales. Risk measures calculated based on daily, weekly, monthly and yearly historical data can be very different. The appropriate frequency depends on the time horizon of the analysis. Therefore, it is important to measure economic risk at both the time level and the frequency level. However, time series analysis and statistical analysis that have been widely adopted in economic risk analysis focus on only one of the two dimensions. In this paper, wavelet models are used to enhance both time‐invariant analysis and time‐variant analysis. Wavelet models can systematically analyze risk by time and frequency and provide richer information than time series models. Using wavelet models, risk measurement can easily be adjusted based on time horizon in a consistent way. Equity VaR estimation is selected to demonstrate the application of wavelet analysis in risk management.

Description: This article departs from the usual GMWB pricing approach by assuming that the policyholder is always smart enough to act in his/her own best interest.

Description: 2011 SOA Enterprise Risk Management Symposium, Chicago. This is an abstract of the referenced article.

Description: How insurers can include a liquidity score and liquidity stress scenarios as part of their asset liability management (ALM) protocol.

Description: This article provides an introduction to group supervision and group capital and its importance in today's increasingly global insurance market. Additionally, the article examines the various methods used in practice for calculating group capital and the factors that companies should consider as they develop their group capital management strategy.

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: Enterprise risk management (ERM) is an orderly or guided conduct of an enterprise to deal with risks. For insurers, ERM requires full support and commitment of the senior management. ERM in the U.S. and Canada has evolved over the years into a complex ecosystem.