Description: This session discussion is about tools, techniques, and methods used by the health valuation actuary HVA in dealing with business projections, cash-flow analysis, valuation on nonguaranteed premium business, target surplus analysis, reinsurance and claim reserves. From Record of Society of Actuaries Vol. 18, No. 1A.

Description: This article provides an overview of critical illness insurance: history, product description, and the three primary ways to offer critical illness coverage in the United States. This article also contains an illustration of an issue age premium rate calculation, as well as product features and rating applications specific to the various types of critical illness coverage.

Description: In 2018, the Financial Accounting Standards Board (FASB) adopted significant revisions to the U.S. GAAP insurance accounting literature, via Accounting Standards Update (ASU) 2018-12. The title of ASU 2018-12 is “Targeted Improvements to the Accounting for Long-Duration Contracts,” and as a result a common acronym for ASU 2018-12 is LDTI (long-duration targeted improvements).

Description: During the past few years, performance, cost, and risk trends for Medigap products have undergone substantial changes. Some of these trends could ease cost pressures on Medigap insurers, while others might increase them. Part 1 of this Issue Brief article discusses current changing trends and considerations around about how they are impacting Medigap.

Description: In 2018, the Financial Accounting Standards Board (FASB) adopted significant revisions to the U.S. GAAP insurance accounting literature, via Accounting Standards Update (ASU) 2018-12. The title of ASU 2018-12 is “Targeted Improvements to the Accounting for Long-Duration Contracts,” and as a result, a common acronym for ASU 2018-12 is LDTI (long-duration targeted improvements).

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.

Description: The article documents the efficacy of infra-low frequency neurofeedback regarding how it improves patient no show rates, decreases mental health symptoms by 40% or more, and self-harm and suicidal ideations.

Description: As actuaries in health care, we spend most of our time focused on reviewing claims, enrollment and clinical data. That information is captured while patients are in health care facilities. Yet most people spend only a small fraction of their time in these facilities. This article explores what types of data are available to actuaries the provide a more holistic view of the member so that a members physical and emotional needs, the social determinants of health can be taken into consideration along with their clinical needs. Two uses cases are presented so actuaries can see how these types of analyses can be leveraged to improve clinical, operational, and financial performance of risk bearing health entities. Understanding the customer (member) not just the patient enables actuaries to provide new insights and values to our stakeholders across a wide range of business and risk applications.

Description: On average, contracts receiving their first star rating receive 3.20 stars, compared to existing or established contracts receiving 3.90 stars on average. This 0.70 disparity in quality ratings creates significant hardship on new contracts when this lower star rating decreases their payments in the following calendar year. This article explores the disparity in Medicare Advantage Star Ratings between contracts receiving their first star ratings (“New” contracts) and existing contracts, including: 1. Understanding what happens to Medicare Advantage Payments when contracts receive lower star ratings? – What is the Star Rating cliff and why does it matter to new contracts? 2. Explaining when a contract receives their first star rating and how much enrollment this requires – When does the Star Rating cliff occur? 3. Noting which specific star measures “New” contracts struggle with, including those contracts under large parent organizations, contracts with rapid growth, and all other new contracts. – How can my contract avoid the Star Rating cliff?

Description: Read this report to learn more about interpretable machine learning techniques for health insurance fraud detection.