Description: We will determine the role of risk function in evaluating operational risks associated with the implementation of LDTI and any system conversions. Are all models validated by the risk function before they are productionalized and results used by management to make business decisions? Does the risk function play a part in to assist finance, actuarial and investor relations in communicating changes and volatility resulting from LDTI? If not, should it? We will articulate the role that risk functions play in being part of large transformation from the start as a stakeholder/input provider to subsequently being an independent reviewer (i.e., second Line of defense).

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: 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: Many ERM programs fail to include an active step of actual risk treatment and are therefore rightfully characterized as Entertainment Systems. Real ERM will use risk assessment to drive decisions about risk acceptance and risk mitigation. Entertainment systems use risk assessment to create reports and to hold discussions. No actions are taken often because ERM is not empowered to do anything.

Description: Many ERM programs fail to include an active step of actual risk treatment and are therefore rightfully characterized as Entertainment Systems. Real ERM will use risk assessment to drive decisions about risk acceptance and risk mitigation. Entertainment systems use risk assessment to create reports and to hold discussions. No actions are taken often because ERM is not empowered to do anything.

Description: This article will further discuss decentralized and centralized model development frameworks and provide tips and rationale for transitioning toward centralized model development.

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: 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: 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.