Description: Introduce basic concepts of the Health Risk-Based Capital and show case a few strategies around Risk-Based Capital.

Description: Businesses across the board have been drastically impacted by the effects of COVID-19 over the course of 2020, and insurance was not immune. Perhaps most relevant to this industry’s current capital and liquidity concern is the prevailing low interest rate environment. This has been a global trend in the last decade, and treasury rates in much of the developed world are at or near zero. Reduced investment returns limit an insurer’s ability to achieve their desired return on equity, eliminating previously profitable strategies that depended on the leverage of life and annuity balance sheets. Focus and activity have instead shifted to other methods to counteract the low interest rate markets and accompanying capital strain. Examples include increased mergers and acquisitions (M&A) activity within the insurance industry and an increasing focus by private equity firms in the insurance market for asset-intensive products, both of which reflect the goal of increasing yield rates on capital.

Description: This is a notice is a call of papers reflecting actuarial and insurance related themes.

Description: it discusses the framework and modeling of the market consistent valuation of fixed indexed annuity, against the backdrop of the convergence of accounting standards and internal risk management.

Description: Society of Actuaries (SOA) Research Institute’s Committee on Finance Research (CFR) and SOA Investment Section are interested in an exploration of the impact of Artificial Intelligence on investment professionals from a variety of perspectives. The result of this effort is intended to provide a useful resource for readers to become well-grounded on the issues surrounding the impact of these tools in an investment context and set the stage for future research.

Description: As AI becomes more pervasive, we must consider the potential repercussions and risks associated with its widespread use. The SOA seeks essays that delve into the multifaceted risks posed by (AI) over both the short and long term.

Description: The article provides a summary of the Drake Risk & Opportunity Forum held on October 21, 2022. The forum was a hybrid conference that attracted more than 200 attendees from 17 states, two countries, 48 employers, and five universities. Following an introduction by the Zimpleman College of Business Dean, the forum held six sessions. First was an insurance company CRO panel followed by a panel of non-insurance company CROs. Gary Zhu shared his outlook for interest rates and capital markets. The fourth panel discussed insurance capital models. The fifth panel discussed environmental, social, and governance (ESG) criteria when building insurance company investment portfolios. The final session of the day was a discussion about the NAIC, how they operate, and their priorities for the coming year.

Description: While the major market crash subsequent to the widespread contagion of the COVID-19 virus was readily predictable, the rapid recovery and ongoing growth subsequent to it was not. Market dynamics around the recovery resist characterization by most traditional analytical approaches. In examining the circumstances around the emergence of a large retail class of investors, one sees that ingrained human tendencies, particularly optimism bias, have fueled much investor behavior responsible for the recovery. Understanding this makes it possible to predict potential future crashes and develop behavioral interventions for subsequent market recoveries.

Description: Discount rate curve construction has been a topic of renewed interest following recent changes in financial reporting standards. Interpolation is a key component of curve construction and can be conducted via an assortment of methods. This article explores six popular interpolation techniques and assesses how they perform with regards to desirable features of such methods: continuity of forwards, stability and locality, simplicity, and market consistency.

Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).