Description: This session explores the far-reaching changes to how clinical data will be sourced, processed and consumed in many payer use cases including risk adjustment, quality measurement, care management, claims adjudication, prior authorizations, and other payer functions. Robust, interoperable data exchange is a prerequisite for value-based care. Clinical data will be increasingly accessible to augment administrative (claims) data via standardized Fast Health Interoperability Resources (FHIR, pronounced 'fire'), at a fraction of the cost of a accessing a traditional medical record and in real-time. On May 1, 2020, HHS issued a final rule implementing interoperability and the patient access provisions of the 21st Century Cures Act of 2016, although enforcement has been delayed into 2021 because of the COVID-19 pandemic. The new regulations promote, “HHS’s vision and strategy for achieving a health information ecosystem within which all individuals, their personal representatives, their health care providers, and their payers are able to send, receive, find, and use electronic health information in a manner that is appropriate, secure, timely, and reliable to support the health and wellness of individuals through informed, shared decision-making, as well as to support consumer choice of payers and providers.”Once enforcement begins, CMS and the Office of the National Coordinator for Health Information Technology (ONC), have the authority to mandate adoption of interoperability requirements, and also penalize payers and providers for non-compliance or the delay and withholding of data.This ‘digital transformation’ of the clinical data ecosystem has far-reaching implications for enabling technologies including Natural Language Processing (NLP) and entire segments of services and vendors. It will eliminate many departmental barriers, duplication and error sources for health plans and impact every department and the underlying economics of delivering and managing care.

Description: After a sustained period of mortality improvement, U.S. population mortality improvement has stagnated in recent years. The presenters will discuss mortality improvement trends in the U.S. population and the causes of death contributing to these trends. U.S. population cause of death trends will be analyzed and compared to trends in the insured population. Mortality rates for major causes of death will be compared between the U.S. population and the insured population. In addition, assumption setting for both older age and later duration mortality as well as mortality improvement will be covered.

Description: Living benefits (GLWBs) in fixed index annuity products are typically accounted for under SOP03-1 under current GAAP guidance. LDTI requires companies to classify these riders as Market Risk Benefit (MRB). This webcast focuses on challenges and considerations of the MRB implementation. Industry practices of the reserving approach will be shared during this webcast, as well as hedging and pricing implications.

Description: The COVID-19 pandemic has had an impact on all aspects of daily life and its impact extends to how disability incidence may play out in the future. Product profitability will be shaped by how morbidity patterns, interest rate levels and other key drivers will play out into the future. In this session, our panel will discuss COVID’s impact on IDI morbidity experience as observed over the past 18 months, offer comparisons and insights in light of other insurance lines of business, and share emerging information on the on-going effects of Long COVID. Highlights will also include how regional differences and implications for insurers as the industry moves further into the COVID vaccine era.

Description: Variable annuity (VA) writers are facing many challenges in today's economic environment. Equity market volatility and low interest rates make guaranteed benefits increasingly expensive to hedge. Both GAAP Long Duration Targeted Improvements and the recent VM-21 changes will drive the need to manage the VA business under a new set of lenses. During this session, the presenters will explore the various in-force management tools VA writers have available and comment on their effectiveness.

Description: Analyzing large quantities of raw complex data is often a challenge in the health care industry. During this webcast, there will be a demonstration of how unsupervised machine learning techniques can be applied to such data to help bring useful insights. This will include examples that use unsupervised learning techniques spanning dimensionality reduction, clustering and anomaly detection.

Description: Throughout this session, the presenters will focus on tax rules pertaining to life and annuities, highlighting the recent changes to interest rates under Internal Revenue Code Section 7702 which determines whether a contract qualifies as a life insurance contract. In addition, recent annuity changes will be discussed such as required minimum distributions. Background and examples will be provided on the cash value accumulation test, the guideline premium requirements and the cash value corridor rules under 7702.

Description: 2021 Life Meeting - Session 5C: Assumption Setting in an Uncertain World 8.31.21 Recording

Description: Pricing is a central activity of actuaries to assure solvency and to manage risk. Life insurance is also a social good, not just a product, and the US population is massively underinsured. Pricing must strike a balance. Unlike a simple 4x4 matrix to produce pricing, today's technology offers the ability to evaluate literally thousands of variables, enabling a kind of precision pricing. It also heightens the possibility of introducing bias and discrimination through the use of unregulated third-party data.

Description: Insurers are increasingly using advanced analytics to model expected outcomes. Two well-known examples in the life insurance industry are models for predicting policyholder behavior and to manage risk selection. The presenters will explore the various types of advanced analytics being considered in models and how the associated risks of these models can be incorporated into an enterprise's risk framework.