Description: Discussion of how to mature value-based contracts between health insurers and provider organizations to improve population health and financial sustainability. Centers around contract parameter consistency, revenue predictability and risk assessment. Intended audience are health plan and ACO actuaries and leaders. Authors are writing from the provider perspective in value-based contracts.

Description: The direct and indirect effects of the pandemic have impacted many actuaries’ work since the spring of 2020. This article addresses three considerations actuaries should account for when using risk evaluation models which are focused on projecting remote, highly unlikely losses: model effectiveness, asset risk, and insurance (mortality and mortality) risk.

Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: The article introduces a cashflow matching strategic asset allocation (SAA) framework for life insurers. Given the recent shift to higher interest rates after two decades of persistent low interest rates, insurers have an opportunity to enhance their asset liability management by rebalancing asset portfolios to narrow duration gaps and attain higher risk-adjusted yields. The framework aims to align investment objectives with economic, regulatory, and rating agency requirements. It prioritizes cashflow matching as the primary optimization goal and incorporates security-level asset information to assist portfolio managers in executing SAA seamlessly. The article highlights the benefits of the cashflow matching SAA framework, including greater attention to cashflow matching, unbiased results, readily executable strategies, and stable economic reserves. A case study is included to demonstrate the effectiveness of the framework in improving risk-adjusted return and cashflow matching for a representative liability block.

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: This article is part of a 2 part series on real options analysis (ROA). Before you can understand the limitations of ROA, you must first understand the difference between risk and uncertainty. This article goes into detail explaining the vast differences between the two phenomena.

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: Strategic Risk Management was given lower priority in a recent survey of ERM Objectives. This article tells how the author believes that recent events have caused many to shift to a more short term focus and in the future there will be a shift back to a priority for the longer term view that stands behind Strategic Risk Management when the environment settles down and things again seem to be more predictable.

Description: Risk management techniques do not include system science. System science requires that the uncertainty of the world is incorporated into the results. The articles explains the difference between risk and uncertainty. Learn how risk practices create an overoptimistic view of future outcomes.

Description: After a very challenging year, actuaries need to look back and consider the lessons learned. Have we returned to where we were, or have we moved on to a new norm? What does the COVID-19 pandemic market data tell us that will help us to prepare for future global crises?