Description: In this essay, we focus on the “Prospective Solvency Assessment” aspect of the NAIC’s ORSA. This places a spotlight on an insurer’s strategic decision making from a risk and capital management perspective. It requires those in senior management to describe actions they would take to address possible adverse changes in their risk and solvency position, i.e., what they would do to reduce risk, increase capital, and/or adjust the business strategy. We contend that by applying a slightly broader scope than the specific requirements of the NAIC’s ORSA, insurers can develop this into a valuable process to support risk-based decision making. In this essay, we initially consider how to determine which metrics to include in such a process and then discuss some important business requirements and practical challenges for implementing a calculation approach to support it. Part of the essay collection "Risk Metrics for Decision Making and ORSA"

Description: The Federal funds rate increased by 4.25% in 2022 with further increases in 2023, which have already resulted in a liquidity crisis for a few banks. As the rapid increase of the interest rates pushed the new money rates above the yields of many life insurers’ investment portfolios, insurance companies are facing a dilemma between compressed margins from peer pressure and the concern with liquidity shortfalls and realized capital losses should mass surrenders happen. This paper discussed main emerging risks under rising interest rate environment, and several ways to mitigate these risks.

Description: This article on developing effective risk management strategies to protect your organization looked at the following topics: • Successfully identifying, assessing and managing risks for all stakeholders. • Identifying the appropriate strategy for your particular needs. • Ensuring the governance body understands risk. • Developing a risk management framework. • Incorporating risk management into your business planning.

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: Risk adjustment is needed under IFRS 17 to reflect the compensation that a company requires for bearing the uncertainty about the amount and timing of the cash flows that arises from non-financial risk. Companies are also required to disclose the method and confidence level used for the calculation of the RA. However, IFRS 17 doesn’t specify a method and a confidence level, nor does it provide a list of specific risks that are considered to be non-financial risk. Companies need to define them based on their own preferences or existing practices. This article will introduce available approaches and discuss the confidence level approach with potential consideration stemming from industry preferences and illustrative examples.

Description: This article argues that using Economic Capital as a metric does not capture all of the risks. The author suggests an alternative that better meets a corporation's overall risk management needs. This is value-based ERM, which is an approach that makes the quantification of enterprise value central to all aspects of the ERM process. It is a combination of two techniques—enterprise risk management and value-based management, and it captures all of the risks in a single metric.

Description: Insurance is a technically demanding, risk-based, capital intensive business. It is therefore not surprising that the industry is fertile ground for the development of technical models used to measure and price risk and manage capital. This session presents the forces which have significantly influenced the evolution of risk and capital management techniques and organization in the insurance industry, including regulatory changes, financial market instability, enlightened self-interest and ultimately a focus on shareholder value creation. Combining over 20 years in the industry in risk and finance roles with a rigorous academic approach, the presenter will provide insights into the most important business questions which insurers face today and the technical tools and frameworks used to answer them.

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 speakers will discuss the replication of long-term options, including a discussion of issues such as liquidity, transaction costs, slippage, impact of policyholder behavior, and machine learning algorithms to improve hedge effectiveness.