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: Chairperson's summary of Joint Risk Management Section (JRMS) Council accomplishments during the 2021-2022 Section Council year, as part of the December 2022 Risk Management newsletter. Includes an introduction to new council members, appreciation to those departing the Council, and introduces the new JRMS Council Leadership. Discusses the accomplishments made, areas of focus and activities underway which benefit JRMS Section members.

Description: Chairperson's introduction to the Joint Risk Management Section (JRMS) February 2022 Risk Management newsletter. Includes a brief description of the JRMS section, summarizes the portfolio of benefits and continuing education opportunities available for those in the risk management profession, discusses the role of the JRMS Council to lead the efforts to provide those benefits and touches on changes underway.

Description: In this article, a numerical comparison of two commonly used FIA valuation methodologies is conducted to help insurers decide on the best methodology to use under GAAP LDTI and VM-22. We compared two modeling approaches to project the future index credits with the annual point-to-point crediting formula: Tthe stochastic equity crediting model and the deterministic option budget crediting model. The analysis is then extended to different levels of option budgets and the monthly average crediting formula. The advantages and disadvantages of the two equity return projecting approaches are also highlighted and will be discussed later in the report.

Description: The Joint Risk Management Section's chairperson explains the Joint Risk Management Section Council members plans for the coming year, with a heavy focus on research and continuing education.

Description: This is the second of a two-part article introducing and elaborating an “Adaptor” strategy for ERM. Such a strategy is intended to fulfil two purposes: first, switching among four styles of risk-coping strategies in order to track qualitative changes in a business’s risk environment; and, second, to support the business in navigating with resilience through all the seasons of a business-economic cycle, i.e., of boom, bust, moderate, uncertain. This latter requires careful study of the nature of resilience, the familiar idea of bounce back, and the less familiar idea of bounce forward. Part I of our article is underpinned therefore by a supplementary document (available from https://riskviews.wordpress.com/adaptor-exceptionalism/), which addresses the issue of structural change in a business or economy. It draws heavily on ideas taken from the discipline of control engineering, as well as some from ecology, social anthropology, and economics. The entire two-part article is thus an exercise in cross-disciplinary Systems Thinking.

Description: This article is dedicated to the non-statutory accounting treatment of common credit risk exposures of life insurers but does not cover all aspects of non-statutory accounting standards. It introduces the concept of and the valuation of credit risks, concerning life insurers.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

Description: Climate risk modeling is taking center stage. With businesses increasingly expected to show how they assess, adapt to, and mitigate the risks of climate change, companies are asking: What is the right climate risk modeling approach for our business?

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.