Description: Actuarial models and modeling processes continue to grow in complexity, accelerated by the changes in U.S. Statutory, U.S. GAAP and IFRS reporting requirements in the past few years. Modeling and valuation teams are expanding their toolkits beyond the traditional vendor-maintained actuarial software and the MS Office suite of tools. Companies have started to use a variety of tools to improve model dev and valuation processes, including: - Data management - Automation and workflow - Reporting and analytics

Description: Pension risk transfer (PRT) has increasingly been a focus of private sector pension plan sponsors over the past decade. The U.S. PRT market had a record-breaking 2021. There are a few forms of PRT, and the data challenge applies to all of them. As an insurer, winning the PRT business is only the first step. There are more challenges when incorporating the data from an outdated admin system into the valuation system. A lot of manual intervention is needed for many companies, which brings different issues. Under LDTI, insurers also need better assumption management. By attending the session, you will: - Understand better different types of penstion risk transfer. - Understand the common challenges insurers face when taking on new PRT business. - Comprehend data-driven solutions that leverage different technologies to automate the reporting process. - Learn additional requirements related to PRT under LDTI.

Description: Risk adjustment in government regulated health plans has been around for over two decades. Yet the strategies for promoting risk score accuracy haven't varied much during that time. Most health plans, physician groups, and vendors promote chart reviews and/or in-home assessments as the primary vehicle for collecting data not perceived to be present in the routine claims submission and adjudication stream. We know that most organizations trying to coalesce encounter data from multiple sources often fail to ensure that all relevant data is ultimately submitted to CMS or a state Medicaid agency. Crucial data, often with significant financial value, frequently gets lost in the data pipeline, somewhere between the provider's billing system and the health plan and/or its vendors. Because 837-formatted data is difficult to read, it complicates payers and vendor’s abilities to track encounter data so that every service delivered to patients is credited to the applicable risk adjustment formula. It is incumbent upon successful risk-adjusted entities to be able to track encounter data, identify processes and/or vendors which may be mangling or losing data, in order to ensure that risk scores are sufficiently accurate to be compliant with plan sponsor requirements and to ensure appropriate revenue to the health plan or provider group.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.

Description: Dive into this review on Artificial Intelligence and Machine Learning (AIML) techniques for actuarial modeling for life & annuities. Presenters will showcase learnings from the recent SOA research paper "Predictive Analytics and Machine Learning – Practical Applications for Actuarial Modeling" (Nested Stochastic). The researchers will go over models and methodologies used to develop high-fidelity proxy models for various life & annuity applications.

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: FASB published LTDI requirements in 2018. The effective date for insurers who are SEC filers to publish US GAAP financials under LDTI is January 1, 2023. The effective date for non-public insurers is January 1, 2025. LDTI requires insurers to identify a new category of benefits called market risk benefits (MRBs) in insurance products. Broadly, variable annuity (VA) and fixed indexed annuity (FIA) guaranteed minimum death, accumulation, withdrawal, and income benefits fall under MRBs. Valuing MRBs requires calculating the liability on a fair value basis. Valuing FIA MRBs on a fair value basis can be complicated as several aspects of FIAs such as option budgets and policyholder behavior are driven by company management decisions and the competitive landscape.

Description: We're introducing a new “cutting edge” wealth accumulation predictive framework, the predictive equity analytics (PEA) for life products (annuity, insurance, group employer benefits, retirement plan). PEA enables life insurers and policyholders to avoid market downturns and capture returns from equities (stocks, ETFs and mutual funds) predicted to increase in next one to three trade days via fund prediction advisory by insurers, additionally by individual policyholders. The capability does not exist in insurance markets nor in financial intermediary markets.

Description: Creating a predictive model is one thing, but then taking that model and getting full stakeholder buy-in is another. Learn from presenters' experiences and insights on proving model effectiveness in the long-term care (LTC), life and annuity spaces. Explore various topics such as feasibility, model transparency, fairness and equity, ROI measurement, data assets and compliance, infrastructure and more. By the end of the session, you will gain an understanding of how to navigate the challenges of proving the effectiveness of predictive modeling and obtaining management buy-in.

Description: Gain insights into the expanding scope of fluidless underwriting and conversation around why this is not yet the new normal for traditional life insurance business. A panel representing reinsurance, direct company, actuarial, and underwriting perspectives will discuss current developments in fluidless underwriting and downstream considerations for mortality outcomes. The panel will address such topics as: - High-level observations from newer underwriting tools and processes - Regulatory considerations for adopting new underwriting tools and processes, - Pitfalls in assuming business as usual when adopting new underwriting tools and processes, and - Considerations when determining the mortality impact of new underwriting tools and processes. By attending the session, you will: - Identify recent trends and outcomes from fluidless underwriting programs and why it is not yet the norm. - Recognize the different regulatory considerations for newer underwriting tools and processes. - Appreciate the nuances between established underwriting practices and expectations for new underwriting processes. - Understand drivers of mortality outcomes and considerations when quantifying changes to underwriting.