Press Releases July 14, 2026 09:00 AM

WTW launches mortality model bringing enhanced predictive capabilities to U.S. pension risk transfer market

Willis Towers Watson unveils advanced mortality model to enhance U.S. pension risk transfer pricing and longevity risk management

By Jordan Park
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WTW

WTW has launched a new version of its Geospatial Mortality Model (GMM) designed for the U.S. pension risk transfer market. The model integrates geographic and socioeconomic data to improve mortality assumptions, supporting insurers and reinsurers in pricing and managing longevity risk more accurately. The model is based on extensive mortality data, including recent post-COVID trends, enhancing asset-liability management and competitive positioning in pension risk transfer.

WTW launches mortality model bringing enhanced predictive capabilities to U.S. pension risk transfer market
WTW
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Key Points

  • WTW's Geospatial Mortality Model leverages geographic, pension, and socioeconomic data to predict life expectancy more accurately.
  • The updated model incorporates post-COVID mortality experience through 2024, trained on nearly four million life-years of data.
  • The model aims to improve pricing precision and longevity risk management for insurers and reinsurers in the U.S. pension risk transfer market.

NEW YORK, July 14, 2026 (GLOBE NEWSWIRE) -- WTW (NASDAQ: WTW) today announced the launch of a new version of its Geospatial Mortality Model (GMM) intended for the U.S. pension risk transfer (PRT) market that will enable insurers and reinsurers to more accurately price and manage longevity risk.

The model – already used by U.S. pension plan sponsors to set longevity assumptions – is now available to insurers to enhance PRT pricing, strengthen asset-liability management, and improve visibility into longevity risk.

WTW’s GMM produces smarter, more flexible mortality assumptions by harnessing the predictive power of both pension and geographic data. Leveraging insights gleaned from where participants live, the model incorporates socioeconomic and health-related factors alongside participant-specific pension data that have been shown to be strongly predictive of life expectancy.

The model has been trained on nearly four million life-years of mortality data, including post-COVID experience through 2024, and developed by evaluating over 200 socioeconomic factors to specifically identify the health, wealth, and lifestyle factors most predictive of longevity. This ensures that GMM delivers the accuracy needed to gain a strategic edge in PRT pricing and asset-liability management, as well as mitigating unexpected outcomes.

Beth Ashmore, Senior Managing Director, Retirement, WTW, said: “We are thrilled to partner with our colleagues in Insurance Consulting and Technology to expand the reach of WTW’s Geospatial Mortality Model (GMM). GMM has already provided pension plan sponsors better insights into their plans’ unique longevity and we’re excited to bring this enhanced capability to the insurance market.”

Karen Grote, Managing Director and North American Life Division Leader, Insurance Consulting and Technology, WTW, said: “For insurers, accurate mortality assumptions are foundational to pricing and risk management. By making this proven model available to the insurance community, we’re giving PRT writers a powerful new way to sharpen pricing, enhance longevity risk management, and compete with greater confidence.”

About WTW

At WTW (NASDAQ: WTW), we provide data-driven, insight-led solutions in the areas of people, risk and capital. Leveraging the global view and local expertise of our colleagues serving 140 countries and markets, we help organisations sharpen their strategy, enhance organisational resilience, motivate their workforce and maximise performance.

Working shoulder to shoulder with our clients, we uncover opportunities for sustainable success – and provide perspective that moves you.

Learn more at wtwco.com.

Media contacts
Arnelle Sullivan: +1 718 208 0474 | [email protected]
Andrew Collis: +44 7932 725 267 | [email protected]


Risks

  • Potential inaccuracies if post-COVID mortality trends shift unexpectedly, impacting model reliability.
  • Dependence on the availability and quality of geographic and socioeconomic data for precise predictions.
  • Competition from other mortality modeling tools may limit market adoption or pricing power.

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