Description: On July 17, FASB announced a deferral of the compliance deadline for the new Long Duration Targeted Improvements (LDTI) guidance: “a one year deferral for larger SEC filers and a two or three year deferral for all other entities.” This article highlights the opportunity for companies striving to comply to modernize in light of this deferral, and it provides a few specific suggestions to companies who pursue this path.

Description: The global doubling of information content has been estimated to occur at a rate of every 12 hours, and conceivably faster. This ‘infowhelm’ is a double-edged sword for insurers. While maximization of the global knowledge base can fuel innovation, it can occur only in the context of reasonable cost for a higher benefit. The insurance industry – underwriters and actuaries in particular – are challenged to constantly examine new innovations that promise to raise confidence and protective value for insurability, within legal limits. This report aims to be the first of several to alert the SOA of promising innovations for the profession.

Description: This report outlines a framework for interpretable machine learning algorithms for detecting fraud in health insurance. It discusses the challenges of using black-box machine learning models in the insurance industry and proposes various interpretable methods to enhance transparency and understanding.

Description: The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.

Description: This collection presents three essays analyzing the multifaceted impacts of AI and LLMs on retirees and retirement professionals, blending quantitative, philosophical, and forward-looking perspectives.

Description: Open source programming languages have become a common part of the actuarial toolkit over the years. Using tools like R and Python, highly technical actuaries can automate onerous processes, easily work with massive amounts of data, create models and generate reports. Although these tools excel at turbo-charging analysts, it can be difficult for benefits to inure to the rest of the organization given the learning curve (“Can’t you just put this in Excel?”), control risks (“So your entire process could break overnight if some random person in Nebraska changes something?”), and internal IT environments. Listen in on a panel discussion where presenters share their personal stories of working with open source in a production setting. Hear about real world business problems, how open source tools were used, what risks were present, how they were overcome and the final business result.

Description: Actuaries too often get pulled into data management activities, taking them away from the analysis work they enjoy and were hired to do. Without a good relationship between actuaries and IT, this can persist even at companies that have been through finance transformations. Gain insight as we focus on the important relationship between actuaries and IT, and how they can work together on best practices for extracting, managing and using the policy data needed for actuarial models. Join our discussion on a variety of data governance and control practices used when extracting policy data from administration systems. Actuaries and IT must work together to create clear data dictionaries that fully document all steps taken as policy data runs through the exchange, transform and load (ETL) processes prior to being used in actuarial models.

Description: Summary of an expert roundtable on generative AI in healthcare and health insurance, focusing on use cases, governance, trust, risks, and evolving actuarial skills.

Description: Navigate complexities of retirement financing considering longevity's impact. Use these tools to help differentiate longevity from life expectancy and avoid outliving financial resources.

Description: At the end of December 2020, Congress enacted the Consolidated Appropriates Act, 2021, which included revisions to Section 7702. Effective for policies issued after December 31, 2020, the fixed 4% and 6% minimum annual effective rates under these tests were changed to refer to a market-based rate called the “insurance interest rate”. For contracts issued in 2021, the insurance rate was set to 2% - defined as the lower of the Section 7702 applicable federal interest rate based on the 60-month period ending December 2019 (2%) and the Section 7702 valuation interest rate in effect for 2020 (3.5%). This reduction in the rates used for compliance testing required significant product, operational, and technology changes for companies to reach product compliance. After 2021, the insurance interest rate is redetermined annually with a minimum change floor.