Description: The first annual SOA Predictive Analytics Symposium was held in Chicago and exceeded all expectations in attendance, presentation quality, and relevant content.

Description: Latest development in IFRS 17 including a first look at companies disclosures and financial impacts, as well as a discussion of some of the run time issues that companies face.

Description: A multi-author collection exploring how catastrophe modeling can be adapted to life and health insurance, particularly in response to the 2025 Los Angeles wildfires.

Description: This report presents research into recent population mortality experience compiled by the Retirement Plans Experience Committee of the Society of Actuaries Research Institute as it pertains to development of mortality improvement assumptions for measuring obligations of retirement programs in the United States.

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 report presents research into recent population mortality experience compiled by the Retirement Plans Experience Committee (RPEC) of the Society of Actuaries Research Institute as it pertains to development of mortality improvement assumptions for measuring obligations of retirement programs in the United States.

Description: A discussion on the starting point of building a predictive model.

Description: In this paper we compute the conditional and unconditional probability of ruin for an individual who wishes to consume a fixed periodic amount from an initial endowment invested in a portfolio earning a stochastic rate of return. From Retirement Needs Framework, M-RS00-1.

Description: Ever wanted to do a Kaggle data science competition, but didn't know where to start? This presentation meets the needs of actuaries interested in predictive analytics and competitive data science. The presentation will describe the nature of competitive data science, and provide insights into lessons learned by competing in the Airbus Ship Detection Challenge at Kaggle, as well as other Kaggle competitions. The presenters will provide an introduction to neural networks and describe the nature of the Airbus Ship Detection Challenge and other Kaggle competitions, plus a solution approaches that can be used to obtain top scores using Convolutional Neural networks (CNNs), random forests and boosted trees.

Description: This is the user guide for the forecasting tool for the project Modeling and Forecasting Cause-of-Death Mortality by Socioeconomic Factors.