Description: This study explores the use of geospatial imagery and high-resolution image technology in the property and casualty (P&C) insurance industry, focusing on improving risk determination through accurate building location data. It introduces new methods like structure footprints and high-resolution elevation information to enhance underwriting, rating, and claims settlement. The study includes two case studies: one on flood risk in Hillsborough County, Florida, and another on wildfire risk in three California communities, demonstrating the significant impact of precise location and elevation data on risk assessment and insurance premiums.

Description: This report analyzes insurance product design, morbidity trends, and pricing models for individuals with pre-existing illnesses in China to improve coverage for substandard health populations.

Description: This report outlines a methodology for estimating mortality improvement factors based on data from insured lives. The report details challenges, methodology, results, comparisons with population data, limitations, and potential improvements.

Description: This report provides insights into statistical methods for imputing race and ethnicity in the context of insurance industry disparities. It discusses various imputation techniques, performance metrics, and the evolution of methods over time. The aim is to address the lack of consistent data collection on race and ethnicity in insurance datasets.

Description: This report is a technical paper on a Predictive Analytics Framework and Reusable Workflow for Experience Analysis in actuarial studies. It covers data analysis, machine learning models, methodologies, and model interpretation techniques.

Description: The Society of Actuaries (SOA) Research Institute’s Mortality and Longevity Strategic Research Program Steering Committee issued a call for essays to explore the application of artificial intelligence (AI) to mortality and longevity. The objective was to gather a variety of perspectives and experiences on the use of AI in mortality modeling, forecasting and prediction to promote discussion and future research around this topic. The collection includes six essays that were accepted for publication from all submissions. Two essays were chosen for prizes based on their creativity, originality, and likelihood of further thought on the subject matter.

Description: This podcast series is an exploration of the paper “Long Term Drivers of Future Mortality” written by Yair Babad and Al Klein. The podcast aims to raise awareness of the paper's valuable insights and make it a useful resource for actuaries, underwriters, insurers, demographers, academics, economists, public health officials, and others involved in estimating future mortality trends and their implications.

Description: This report gives an update on the Getzen Model of Long-Run Medical Cost Trends for 2025-2035+, providing insights on key considerations, input parameters, and projections. It includes observations on long-term medical cost trends, budgetary resistance, and regulatory impacts on healthcare expenditures.

Description: Toward a Predictive Analytics Framework files

Description: The primary purpose of this paper is to demonstrate, through a basic stochastic projection model, how inflation might impact savings and investing for retirement.