Why choose Geospatial Mortality Model (GMM)?
- GMM produces a smarter and more flexible mortality assumption by using geographical-based health, wealth, and lifestyle factors of your plan’s population.
- GMM can quickly provide a view of your population mortality.
- GMM’s accuracy provides a strategic edge in setting assumptions, asset-liability management, and managing surprises.
The catalyst
A recent study by the Society of Actuaries (SOA) demonstrated that the difference in life expectancy between the top and bottom socioeconomic deciles in the United States has increased nearly 800% over the past 40 years, underscoring the influence of health and wealth as key drivers in this expanding “longevity divide”[1].
Traditionally, pension mortality assumptions are set using the average mortality experience of a broad population. However, considering this growing longevity divide, plan sponsors are now more likely than ever to be over- or underestimating their population’s true mortality experience.
Our solution
WTW’s Geospatial Mortality Model (GMM), trained on nearly two million life-years of mortality data, customizes your mortality assumption with meaningful information gleaned from where your participants live, including rates of smoking, homeownership, healthcare access, and marriage, in addition to your population specific pension data (e.g., age, gender and benefit amount). WTW’s GMM has shown that predictive factors associated with where people live can highlight differences in age 65 life expectancy by over 10 years.
What does this mean for your plan?
Effective for all plan sizes
Improved asset-liability management
Seamless adaptability to plan changes
Better measurements to support risk transfer actions
The WTW difference
Leveraging our knowledge and experience gained over the last decade in the U.K. and Canada, WTW’s GMM is a new mortality model that considered over 110 socioeconomic factors to specifically identify the health, wealth, and lifestyle factors most predictive of longevity.
WTW’s GMM develops location-specific profiles based on these factors, resulting in a fascinating view of the United States. We can see how greatly life expectancy varies not only from state to state, but even within a single city. With GMM, plan sponsors can now gain powerful insights regarding the predictive characteristics informed by where their plan participants live, which ultimately drive the plan’s mortality experience and plan costs.
