Description: The presentation will outline the different possibilities insurance companies have to hedge their equity risk for the various products.

Description: This chapter introduces a few probability distributions that play an important role in understanding and analyzing healthcare data. While theoretical in nature, these distributions model real life if appropriately chosen. Probability distributions are mathematical representations of how statistical data behave.

Description: There will be no easy resolution to the current trade friction between the U.S. and China. If anything, the motivations for increasing protectionism are set to intensify. What are these motivations? Where is this trade war headed? And what impact could this have on economies and financial markets?

Description: Retirees will face the societal issue of inadequate savings with likely insufficient time to rectify the situation. They need immediate growth, but also the disciplines of risk management. They typically do not know how to engage with these two aspects in a harmonious manner on their own. Insurance companies are in the business of providing the solution but regulators have been clamping down in favor of simpler and more transparent products. In addition, the market reality over the last decade has been that insurers have stock betas at a multiple to the general markets with PE ratios well below. A panel of senior professionals from writers of retirement product business will present their vision of how the insurance industry can address the above dilemma, including the risks underlying the writing of the business, impact of financial markets, and regulations in the development of the business.

Description: Regulatory change and increased focus on internal risk management are driving a renewed interest in model efficiency. In this session, we will review several new and existing tools, that show significant promise for advanced modeling of equity linked products. These include: • Clustering • Use of unique--and potentially a different number of--scenarios for each policy • Semi Monte Carlo (SMC) – this is a proprietary technique that is similar to a combination of Monte Carlo and numeric integration • Using a neural network approach to optimize some of the more complex and manual aspects of clustering and improve the fit of the clustered model • Combining SMC with the use of unique scenarios per policy

Description: This paper looks at the problem of valuing and managing the ALM risks associated with insurance liabilities that are too long to be matched by available investments. Two options were explored and the paper concludes by arguing that the Static Control Model leads to more useful ALM strategies for long liabilities.

Description: 2011 Enterprise Risk Management Symposium, Chicago. This presentation discusses how real world Grey Swan scenarios are excellent choices for stress and resiliency testing. In addition, an example is provided of a set of Grey Swan scenarios which correspond to the RBC C-3, Phase II.

Description: 2011 SOA Enterprise Risk Management Symposium, Chicago. The stated purpose of this paper is to consider whether a more comprehensive and timely measurement framework for risk exposure is now needed and to examine one possible approach. This paper proposes a new approach to risk measurement that, along with capital measurement and more rigorous oversight, will allow banks an ability to manage risk.

Description: Producing a crystal-clear balance roll-forward on time, obtaining a fresh model output the first time or providing model results for your business partners with a tight turnaround all depend on one thing: how fast the model engine runs. Authors explored various reasons behind significant computational power consumption, and discussed different ways to speed the computational process such as in-force population compression, model use case consolidation, code simplification and streamlining, and infrastructure automation, etc.

Description: According to the Actuarial Standards Board’s Actuarial Standard of Practice No. 49 (ASOP 49), actuarially sound Medicaid managed care capitation rates will include a provision for underwriting gain in order to provide compensation for the risks assumed by an MCO. However, there is no generally accepted method for quantifying underwriting gain in managed Medicaid capitation rates. This article discusses a new model developed to address this void and calculate underwriting gain. The model was developed by researching existing financial literature and performing a statistical analysis of industry data to build out a framework that can be leveraged to estimate underwriting gain.