Ultimate Mortality Table

Ultimate Mortality Table: What It Is and How It Works

What is an ultimate mortality table?

An ultimate mortality table is a statistical chart that shows the percentage of life insurance policyholders expected to survive to each listed age. Tables typically start at age 0 (100% of the cohort) and extend up to around age 120. Data usually come from populations of insured individuals rather than the general population.

Key characteristics

• Focuses on insured populations: based on policyholders from one or more insurers, not the entire country.
• Excludes recently underwritten policies: the first few years of newly issued policies are removed to avoid selection effects (new applicants often pass medical exams and are temporarily healthier than average).
• Stratified by risk factors: tables may be broken down by age, sex, smoking status, weight, ethnicity, region, or other relevant variables.
• Aggregate tables: some datasets combine multiple subgroups into a single aggregate mortality table representing the entire insured population studied.

Why insurers use ultimate mortality tables

Insurance companies rely on these tables to:
* Price life insurance products by estimating the probability an insured will die during the policy period.
* Decide whether to offer coverage or require additional underwriting for applicants.
Accurate mortality analysis is essential to product profitability and risk management.

Other uses

Investment and retirement planners may consult ultimate mortality tables to estimate life expectancies for retirement planning, annuity pricing, or longevity risk assessment.

Construction and data considerations

• Survivorship data underpin ultimate mortality tables—records of deaths and survivals across cohorts and time.
• To avoid bias, recently underwritten policies (early policy years) are excluded because of the healthier-than-average profile of new applicants.
• The breadth and quality of the underlying data affect accuracy: tables compiled from multiple insurers or large, representative datasets are more reliable than those based on a single company’s experience.

Examples and standards

Professional actuarial bodies (for example, the Society of Actuaries) publish widely used mortality tables that aggregate large data sets and provide separate and blended tables for men, women, and combined populations.

Limitations and special considerations

• Selection bias: excluding early policy years reduces bias but does not eliminate all sources of error.
• Population differences: insurer-specific tables may not generalize to other insurers or the general public.
• Changing mortality trends: improvements in healthcare, population behavior, and other factors can render older tables less accurate without regular updates.

Key takeaways

• Ultimate mortality tables estimate survival rates for insured populations by age and other risk factors.
• They exclude early policy years to remove selection effects from newly underwritten lives.
• Insurers use them for pricing and underwriting; financial planners use them for longevity and retirement planning.
• Accuracy depends on the size, representativeness, and currency of the data.