Description: Impact of Family Structure on Aspects of Retirement

Description: Nobody is perfect, and actuaries are no exception. At some point, every actuary is bound to make a big mistake. These kinds of mistakes can have a significant impact on an actuary's reputation — whether that impact is positive or negative depends in large part on the way someone investigates, communicates, and resolves errors they uncover. By approaching mistakes with transparency and honesty, these big mistakes can actually enhance credibility and reputation, rather than harm it.

Description: This article summarizes findings from a recently published peer-reviewed study in the Journal of Risk and Insurance (Hanewald et al., 2025),[1], which examined how access to housing wealth affects stated demand for LTC insurance in China. We conducted a survey of homeowners aged 45-–64 across 49 cities in China and found that offering reverse mortgages or home reversion products significantly increased stated demand for LTC insurance. This article highlights the study’s key results and implications for insurers, actuaries, and policymakers.

Description: Recent LA wildfires exemplified the devastating impacts of compounding weather extreme events. This webcast explores the inherent risk posed by various types of compounding events, proposes statistical techniques to quantify the impacts of compounding events and how this will evolve under a warmer climate. As compound events continue to evolve, it will become increasingly important to monitor and consider risk models and analyses to provide more comprehensive and accurate risk assessments and, ultimately, support more effective disaster preparedness and mitigation efforts. One way to improve preparedness is the underwriting of these risks through geospatial imagery and high-resolution image technology, which have spurred advances across many industries. The webcast will further discuss the importance of building footprint data for risk assessment and examine how building data and vegetation cover data can be used in calculating underwriting wildfire risk metrics.