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: Managed strategies as underlying investments in VA/FIA products have seen spectacular growth since the financial crisis. The speakers of these sessions will share their views on the development of this market and what is expected to unfold over the next 12 months in regard to the next generation of products and fund-based strategies.

Description: In this session, a company will share its experience on managing risks underlying the equity-linked insurance guarantees and what lessons were learned over these years as it adapted its strategies to suit the current market environment.

Description: This session will provide an overview of the evolution of equity-based guarantees (e.g. VA, FIA, etc.) over the last few years and discuss the current lay of the land as it relates to the regulatory, issues, capital markets (e.g. Department of Labor, Dodd-Frank) environment in which these products are offered.

Description: Starting with a review of recent VA and FIA industry policyholder behavior experience, this session will explore several critical elements in developing a sustainable and coherent framework to translate this complex experience data to assumption models, particularly for guarantee features such as GLWBs. The discussion will include the use of industry- and company-level data in building customized predictive models, and integrating models across behaviors such as income utilization and surrenders.

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: 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