Underlying Mortality Assumption: What It Is and Why It Matters

What it is

The underlying mortality assumption is an actuary’s projection of future death rates used to estimate life expectancies. These projections — typically expressed through mortality tables — form the basis for pricing life insurance, calculating reserves, and measuring pension plan liabilities.

How it’s used

• Insurers use mortality assumptions to set premiums and determine reserves for death benefits.
• Pension plans use them to estimate the length of benefit payments and the present value of future obligations.
• Assumptions feed into valuation models and are adjusted for improvements in mortality over time.

Why accuracy matters

• If assumed mortality rates are too low (i.e., predicting fewer deaths than occur), insurers can underprice products and underreserve, resulting in unexpected claim costs.
• If assumed mortality rates are too high (i.e., predicting more deaths than occur), pension plans may understate life expectancies and underfund long-term obligations, creating funded shortfalls.
• Small errors in mortality assumptions compound over time and can materially affect liabilities and capital requirements.

Key technical considerations

• Mortality tables: Period tables reflect mortality in a specific year; cohort tables follow a birth cohort over time and capture long-term trends.
• Improvement scales: Actuaries apply projections (improvement factors) to account for expected declines in mortality rates.
• Selection effects: Newly underwritten lives (e.g., recently issued policies) often have different mortality experience than the general population.
• Uncertainty and longevity risk: Trends in medical advances, pandemics, or behavioral changes create uncertainty. Insurers and pension plans use margins, scenario testing, and longevity hedging (e.g., reinsurance, swaps) to manage risk.

Special considerations and recent data highlights (2020)

Source: CDC mortality statistics (2020)
– Overall death rate: 835.4 deaths per 100,000 population.
– Life expectancy at birth: about 77.0 years.
– Infant mortality: 541.9 deaths per 100,000 live births.
– Leading causes of death (per 100,000):
– Heart disease: 168.2
– Cancer: 144.1
– COVID‑19: 85.0
– Unintentional injuries: 57.6
– Stroke: 38.8
– Life expectancy changes 2019 → 2020:
– Males: 76.3 → 74.2 years
– Females: 81.4 → 79.9 years
– Life expectancy at age 65 (2020):
– Total: 18.5 years (down from 19.6 in 2019)
– Males: 17.0 years (down from 18.2)
– Females: 19.8 years (down from 20.8)

These figures illustrate how external events (for example, a pandemic) can meaningfully shift mortality experience and underscore the need for regular assumption review.

Managing and setting assumptions

• Follow regulatory and professional guidance when selecting base tables and improvement scales.
• Use credible, recent experience and supplement with industry tables where appropriate.
• Perform sensitivity testing and document the rationale for chosen assumptions.
• Update assumptions periodically to reflect emerging trends and actual experience.

Key takeaways

• The underlying mortality assumption drives insurance pricing and pension valuation.
• Inaccurate assumptions create financial risk: underpricing, reserve shortfalls, or underfunded pensions.
• Actuaries combine mortality tables, improvement projections, and risk-management tools to set and adjust assumptions in response to new data and trends.