Description: “DeFi,” or decentralized finance, is an emerging financial system built on blockchains without central intermediaries such as banks. Defi has experienced explosive growth in the last couple of years, with billions of capital locked in numerous DeFi protocols on blockchains. However, DeFi is still relatively unknown outside of the cryptocurrency/blockchain sphere. Established actuarial practice areas such as insurance, employee benefits, banking, and investments all contain DeFi application opportunities for those interested in exploring them. This article aims to provide an introduction to DeFi concepts with a few practical tips.

Description: The introduction and effective application of US GAAP LDTI have increased the scrutiny of those reviewing the updated financial results. That is because LDTI has resulted in significant changes in actuarial reserving and deferred acquisition cost (DAC) models for long-duration contracts, including significant changes to data, assumptions and required disclosures. In light of the changes, greater reliance has been put on companies’ model risk management (MRM) teams and model validations to assess the conceptual soundness and accuracy of their companies’ actuarial models and to assess that the underlying data and process utilized are suitable and appropriate. This article covers a brief background on model validation, walk through a sample LDTI-focused model validation framework/test plan, then provide insights on common LDTI-related validation observations based on the authors' prior validation experience.

Description: Emerging technologies like GenAI will be more disruptive than past technological developments, such as spreadsheets, low-code data and dashboarding tools, and cloud computing. This article will explore how how we can lead ourselves, our teams, and our organizations to thrive within a defined modern value proposition, using one of two archetype roles: actuaries as business partners or actuaries as developers.

Description: Healthcare risk adjustment methodologies have become increasingly complex, largely due to the belief that complexity enhances accuracy. However, a disconnect exists between how healthcare risk is assessed and how payments are adjusted. This article explores the stark difference between measuring the accuracy of a risk assessment model at a member level, and that of a risk adjustment mechanism at the health plan level. I show that a highly simplified approach that does not use diagnosis codes can have similar accuracy on risk adjusted payments to models that do. These findings suggest that accuracy is not a barrier to developing simpler risk adjustment methodologies, which can improve payment accuracy and reduce the financial incentive for coding diagnoses.

Description: An overview of bias and fairness in machine learning models, as they apply to use cases in life insurance.

Description: A brief description of the contents of the July 2022 issue along with are quest for assistance with SOA programming.

Description: Brief discussion on recent published experience studies and RFPs.

Description: In the past months, as COVID-19 has forced the reduced activity in the U.S. economy there has been some very substantial, but varied effects on the U.S. healthcare system. There is a lot of potential for confusion amongst providers and patients with regards to decisions related to which services ought to be rendered and which ought to be deferred. Two specific agencies – the Centers for Medicare and Medicaid Services (CMS), and the American College of Surgeons (ACS) have put together some guidance to add more clarity towards what types of services should be provided and what types ought to be deferred. In addition, this paper includes a discussion on what types of services are most likely to be deferred, and which ones will accrue pent-up demand to be released when many of the deferred services become available again.

Description: This is report summarizes the results of a Joint Risk Management Section sponsored study on negative interest rates and the insurance industry.

Description: This is a brief report that summarizes recent changes in the prevalent medical loss ratio methodologies and also looks at some of the more recent experience of health insurers that filed medical loss ratio reports.