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: A critical step in valuing a company for the purpose of financial reporting is the computation of the market value of economic surplus (MVES). This article provides a novel approach to the valuation of MVES that is stable and reasonably immune to “market noise.” Current approaches to the calculation of economic surplus of an insurance company generally define such surplus as the market value of the assets (MVA) supporting the liabilities less the market value of liabilities (MVL). While MVA is observable in the market, MVL is typically computed directly, without regard to the underlying supporting assets. Subtracting MVL from any value of assets that changes with market movements results in an unstable surplus. Consequently, a surplus that is not fully reflective of market sentiment has been rejected by many. This same approach is further used in the calculation of market consistent embedded value (MCEV) when a balance sheet approach is used .

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: The speaker will discuss the training of neural networks to compute future Greeks of liabilities (or options) without the use of nested stochastics.

Description: Tomorrow’s risk landscape is shaped by macro trends and their interconnections, and those new, changing but not-yet envisioned risks will shape tomorrow's insurance needs. Karen would like to share Swiss Re's insights on new emerging risk themes and trends, climate change and its effects on the L&H business. Hopefully the session can provoke new ideas and enable us to build resilience together.

Description: A critical step in valuing a company for the purpose of financial reporting is the computation of the market value of economic surplus (MVES). This article provides a novel approach to the valuation of MVES that is stable and reasonably immune to “market noise.” Current approaches to the calculation of economic surplus of an insurance company generally define such surplus as the market value of the assets (MVA) supporting the liabilities less the market value of liabilities (MVL). While MVA is observable in the market, MVL is typically computed directly, without regard to the underlying supporting assets. Subtracting MVL from any value of assets that changes with market movements results in an unstable surplus. Consequently, a surplus that is not fully reflective of market sentiment has been rejected by many. This same approach is further used in the calculation of market consistent embedded value (MCEV) when a balance sheet approach is used .