Description: This is the first in a series of calls for essays on the impact of AI on mortality and longevity. For this call for essays, we are interested in the impact of AI on mortality and longevity modeling, forecasting and prediction.

Description: The Society of Actuaries (SOA) Board of Directors (Board) gathered in Chicago March 16–17 for its first meeting of the year. Among key topics on the agenda were the FSA pathway evolution update, Board elections, strategic planning and the use of AI in the workforce.

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: The landscape of the reinsurance industry is evolving amid profound changes in the insurance sector. Innovation is vital for reinsurers as risks become more complex and insurers transition from a role of "protection" to "prevention." Successful reinsurers are responding to market dynamics, particularly through open innovation – collaboration with startups.

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

Description: The article explores how integrating AI into insurer apps can address critical challenges in healthcare, such as patient no-shows, medication adherence, and cost management. These non-disruptive approaches aim to increase the effectiveness of patient interventions and improve medication adherence.

Description: ReMark's Global Consumer Study delivers 10 key insights to empower customers. Insurers can design products that meet customers' needs and enhance client experiences and add significant value to their business. At the same time with new products being launched seemingly continuously, it is important to take a step back and assess risk throughout the product management cycle.

Description: The actuarial profession is changing rapidly and the skills needed to be successful are also changing. In this session, we will debate key topics and questions, such as: What is the predominant future career path for actuaries? Specialists or generalists? Does the actuarial profession have a meaningful value prop outside our core domains e.g. insurance? What skills are needed for the actuary of the future? What’s the best approach to improve actuarial storytelling to non-actuarial audiences? What’s the role of actuaries to managing and predicting emerging trends and assumptions? What is the role of actuaries in relation to development and ownership of advanced modeling? Is the future actuarial profession smaller/larger than it is today? In either case, how does it differ from today in terms of make-up, deployment, function, purpose, value add, etc.?