Description: As sponsored by the Canadian Institute of Actuaries (CIA), Casualty Actuarial Society (CAS), and Society of Actuaries (SOA), the study looks at causes of insolvency and decisions made by management, regulators, and policyholders over the life cycle of the insolvency. Further, this study considers ways the profession can be equipped to prevent or mitigate future insolvencies. In addition to directly benefitting the profession, the work is also intended to assist other insurance industry practitioners in understanding the complexities of insurance company solvency and the benefit of keeping the actuarial profession in the forefront of company management, operations, and regulatory communication

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: This session will look into life insurers’ anticipated business impacts as a result of the implementation of the new accounting standard for insurance contracts, IFRS 17. What will be the financial impact as of day one as a result of transition? How will embedded value for common life insurance products change? How will costs of reinsurance be affected? Will products have to be re-designed and/or re-priced? IFRS 17 may have more ramifications into the way insurers are doing business than we have anticipated so far. While we are working on the implementation of this new standard, we should not lose sight of the business impacts that will follow. At the conclusion of the session, attendees will be able to: Understand key impacts of the transition to IFRS 17; See illustrative examples of the impact on embedded value; and Understand key impacts on product design and pricing Country Relevance: US and Canada

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: Risk management techniques such as stress testing and setting capital targets are fairly well established for life insurers; however, the implementation of these techniques can vary considerably. A fundamental question is how to manage multiple valuation perspectives, and the list of possibilities keeps growing. Companies can potentially consider some or all of the following: U.S. reporting measures (Stat, GAAP), internal measures (EV, VNB, economic capital), international reporting measures (IFRS), rating agency models or other developing standards (ICS, NAIC group capital). This session will begin with a quick overview of the defining characteristics of different valuation frameworks. We will then provide more focused and practical considerations for managing risk and capital under different frameworks, using specific examples to highlight key trade offs.

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: 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: 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.