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Personalized Longevity Modeling and Its Impact on Social Security & Retirement Income Planning 

Jeremy Ko, CEO of ShoreUp Retirement Solutions, presented research on personalized longevity modeling and how individualized lifespan estimates can improve Social Security claiming decisions and retirement income planning. His work combines demographic and health data with statistical modeling to estimate expected lifespan more accurately than traditional planning assumptions or generic life tables. 

1. Why Longevity Modeling Matters in Retirement Planning

Life expectancy has increased significantly over the past century due to medical advances, improved public health, and better treatments for chronic disease. For example, life expectancy at birth in the United States increased from roughly 68 years in 1950 to about 78–79 years by the late 2010s, reflecting major improvements in healthcare and living standards.

Centers for Disease Control and Prevention life expectancy data:
https://www.cdc.gov/nchs/products/databriefs/db355.htm

Despite overall gains, lifespans vary widely across individuals based on health behaviors, socioeconomic status, and demographics. Research shows substantial differences across geography and income levels, including gaps of roughly a decade or more between high-income and low-income populations in the same cities

National research on income and longevity differences:
https://www.nber.org/papers/w21038

These variations create major challenges for retirement planning because advisors must estimate how long retirement income must last.

2. The Problem With Traditional Planning Assumptions

Many financial plans rely on simplified longevity assumptions, such as assuming all clients live to age 90 or 95. Jeremy Ko cited research analyzing tens of thousands of financial plans that found most planners rely on a limited range of assumed ages rather than individualized projections. 

Using generic longevity assumptions can create two major planning errors:

Overestimating lifespan

• Clients may withdraw too aggressively.
• Savings may run out late in life.

Underestimating lifespan

• Clients may underspend unnecessarily.
• Retirement lifestyle and consumption may be overly constrained.

Longevity risk—the risk of outliving assets—is widely recognized as a central challenge in retirement planning.

Social Security Administration discussion of longevity risk:
https://www.ssa.gov/policy/docs/ssb/v70n3/v70n3p1.html

3. Social Security Claiming Decisions and Longevity

Social Security benefits can generally be claimed between age 62 and age 70, with benefits increasing each year that claiming is delayed.

Key rules include:

• Benefits claimed at age 62 are about 30% lower than the full retirement benefit.
• Delaying beyond full retirement age increases benefits by roughly 8% per year until age 70.

Social Security Administration explanation of delayed retirement credits:
https://www.ssa.gov/benefits/retirement/planner/credits.html

For example, someone with a $2,000 monthly benefit at full retirement age (67) would receive approximately:

• $1,400/month at age 62
• $2,480/month at age 70

These tradeoffs mean that longevity expectations strongly influence optimal claiming age. People with shorter expected lifespans may benefit from claiming earlier, while those with longer expected lifespans benefit from delaying benefits.

4. Data Source: Health and Retirement Study

Ko’s research uses the University of Michigan Health and Retirement Study (HRS), a large longitudinal survey of Americans over age 50 that tracks health, demographics, and financial information over time.

Health and Retirement Study overview:
https://hrs.isr.umich.edu/about

The dataset includes information such as:

• Gender and ethnicity
• Education level
• Smoking status
• Self-rated health
• Financial assets and income
• Medical conditions and healthcare usage

Because the survey tracks participants over time, researchers can observe actual death ages, allowing models to compare predicted longevity with real outcomes.

5. The Personalized Longevity Model

The research uses a Cox Proportional Hazards Model, a widely used statistical method for estimating mortality risk based on individual characteristics.

National Cancer Institute explanation of Cox models:
https://www.cancer.gov/publications/dictionaries/cancer-terms/def/cox-proportional-hazards-model

The model estimates how certain attributes affect mortality risk relative to population averages. Examples of findings include:

• Individuals reporting poor or fair health had mortality rates more than double the population average.
• Current smokers experienced mortality rates roughly 50–70% higher than non-smokers.
• Being in poor health could reduce expected lifespan by 8–10 years compared with those reporting excellent health.

These factors dramatically affect retirement planning horizons. 

6. Bias in Self-Estimated Lifespans

The research also found that individuals are poor predictors of their own life expectancy.

The study observed a behavioral pattern known as “flatness bias”:

• Younger retirees tend to underestimate their survival probability.
• Older individuals tend to overestimate their survival probability.

This bias can distort retirement planning if advisors rely on clients’ personal estimates of longevity rather than actuarial models.

7. Financial Impact of Personalized Longevity Modeling

Using individualized lifespan projections to determine optimal Social Security claiming ages produced measurable financial improvements.

Average gains from personalized modeling were approximately:

• $9,000 for women
• $12,000 for men

This corresponds to improvements of roughly:

• 2.5% increase in benefit value for women
• 2.9% increase for men

These gains come solely from better timing of Social Security claiming decisions. 

8. Case Study Illustration

The presentation highlighted a simplified example comparing two individuals:

Individual 1

• White male
• Very good health
• Non-smoker

Personalized modeling estimated a longer lifespan and recommended claiming benefits around age 69.

Individual 2

• Black female
• Poor health
• Current smoker

Generic Social Security life tables predicted she would live longer because women generally outlive men. However, personalized modeling adjusted for health and smoking and estimated a much shorter lifespan.

The personalized model recommended claiming at age 62, which proved more beneficial because she died at age 68. Under generic life table advice, she would have delayed benefits and received little or no Social Security income. 

9. Comparison With Actual Claiming Behavior

The research also compared personalized claiming strategies with actual claiming patterns.

Findings showed that:

• Roughly 50% of women claim at age 62
• About 39% of men claim at age 62
• Only a small minority delay claiming until ages 68–70

Because many retirees claim early without personalized analysis, they often receive lower lifetime benefits than optimal strategies would produce. 

Social Security claiming statistics:
https://crr.bc.edu/briefs/what-explains-early-social-security-claiming/

10. Implications for Retirement Planning

Personalized longevity estimates influence several areas of retirement planning beyond Social Security, including:

• Sustainable withdrawal rates
• Required savings thresholds
• Roth conversion timing
• Tax-efficient withdrawal strategies
• Lifetime income planning

For example, if one spouse has a significantly longer expected lifespan, planners must consider survivor tax brackets and required minimum distributions when designing retirement income strategies. 

11. Client Engagement Challenges

Discussing longevity with clients can be sensitive because conversations about health and mortality may trigger anxiety or avoidance.

The presenter recommended several strategies:

• Build rapport before discussing longevity projections.
• Ask permission before discussing sensitive health topics.
• Frame longevity discussions around planning opportunities rather than mortality.
• Use private digital intake forms so clients can disclose health information confidentially.

These behavioral approaches help advisors incorporate longevity modeling without creating discomfort for clients. 

12. Longevity Modeling Tools and Resources

Several publicly available tools can help advisors incorporate personalized lifespan estimates into financial planning.

Examples include:

Society of Actuaries Longevity Illustrator
https://www.longevityillustrator.org

Income Conductor Social Security optimization software
https://www.incomeconductor.com

Northwestern Mutual Life Expectancy Calculator
https://www.northwesternmutual.com/life-and-money/how-long-will-i-live/

These tools allow planners to adjust lifespan assumptions based on health status, lifestyle factors, and demographic characteristics.

Key Takeaways

• Longevity varies widely across individuals, making personalized modeling important for retirement planning.
• Generic lifespan assumptions can lead to planning errors and suboptimal Social Security claiming decisions.
• Personalized longevity estimates can increase lifetime Social Security benefits by thousands of dollars.
• Behavioral biases often cause individuals to misestimate their survival probabilities.
• Integrating longevity modeling into financial planning can improve retirement income strategies, tax planning, and withdrawal decisions.