Description: This report evaluates the long-term mortality consequences of the COVID-19 pandemic from a cohort-based perspective using historical mortality data.

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 explores the behavioral factors influencing insurance purchase among Gen Z and Millennials for forward-looking purposes, leveraging a rich dataset spanning 22 markets across six continents.

Description: This project models and forecasts premature cardiovascular mortality, with a focus on two main risk factors - obesity status and education attainment level. It investigates the role of obesity and education in determining age-sex-specific premature cardiovascular mortality.

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: Exhibits for Predictive Analytics Framework

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.