What it means
An insurer does not necessarily accept applicants at random, since health information and other underwriting factors can influence acceptance and risk classification. Recently accepted lives can therefore have mortality experience different from a broader population of the same age.
The selection effect creates a duration question, because someone aged forty-five who was recently underwritten may not have the same modelled risk as someone aged forty-five whose policy began many years earlier, and attained age alone cannot express that distinction. A select table can organise rates using age at selection and the number of years since that event, and together those coordinates identify the relevant attained age, so read the notation before choosing a cell.
The select period describes how long the model explicitly preserves duration-based selection effects. An ultimate rate applies after the table's defined select treatment ends, and this modelling boundary is not a sudden health change.
Underwriting advantages can wear off as lives age and health changes. The Society of Actuaries' 2014 study examined assumptions about the length and slope of select mortality in pricing, but its evidence concerns the surveyed industry practice and period, not one universal present-day selection duration.
The study discusses variations by issue age, product, risk class and other parameters, so a table designed for one insured population should not be copied blindly into another portfolio. Select and ultimate are not the same as insured and uninsured, because a table can address insured lives while including both select-duration and ultimate treatment, and the distinction concerns time since the relevant selection process within the model.
A mortality probability is also different from an observed death count. The table supplies a rate for a specified interval and population, while actual deaths in a small group can be higher or lower because of random variation and differences from the assumed population.
The table is an input to pricing and valuation, not a complete premium quotation, since expenses, interest assumptions, benefits, policyholder behaviour and other risks also enter the calculation. A lower selected mortality rate does not translate directly into an identical percentage reduction in a retail premium.
Table choice should be accompanied by purpose and adjustments, as an insurer can compare its own experience with a reference table and apply supported assumptions, whereas a percentage adjustment without evidence about the underlying population or duration pattern is weak support. Historical reference tables need dated treatment, and the SOA report describes earlier basic and valuation tables and the select periods used at that time, which should not be presented as current mandatory rules for every insurer or jurisdiction.
For a non-finance manager reviewing insurance projections, ask which population, issue ages, durations and table version were used. Confirm whether the displayed rate is select or ultimate and which year it covers, so that assumptions are examinable without treating a statistical average as an individual prognosis.
In practice
Real-world examples.
Example
A fictional insurer compares two groups both currently aged forty-five. One was recently underwritten; the other entered long ago. The actuary checks selection duration before assuming the same table cell applies.
Example
A manager finds an older study describing a common select period. The pricing team checks its own current table and portfolio. A historical survey's common practice is not an automatic legal requirement.
Example
A reference table suggests low mortality for recently selected lives. Actual experience in a small group is higher for one year. The actuary examines variation and population fit rather than treating one outcome as decisive proof that the whole table is wrong.
Formula
Calculation
Illustrative expected deaths = exposed lives x applicable one-year mortality probability. If a fictional selected group has 10,000 exposed lives and an assumed probability of 0.001, expected deaths are 10.
At a hypothetical ultimate probability of 0.0015, the same exposure gives 15. These invented rates demonstrate model sensitivity, not a published table or prediction for particular people.Case study
Seen in the real world.
Fictional case study: Oak Life's manager compares projected claims across policy cohorts using attained age only. The comparison overlooks when each cohort entered underwriting. The actuary maps issue age and elapsed duration to the relevant select cells.
It records table version and the assumptions used beyond the select period. The revised report explains why similar current ages can have different modelled claims. It separates assumption changes from observed outcomes and avoids promising exact future death counts.
Watch out
Common mistakes.
- Choosing a rate from attained age alone without the table's selection-duration convention.
- Treating the end of a select period as an abrupt health event for every insured person.
- Using a historical reference population as automatically representative of a current portfolio.
Questions
People also ask.
Does selected mean a randomly sampled population?
Not necessarily. Here it commonly refers to the underwriting selection process.
Is an ultimate rate a personal forecast?
No. It is the table's rate after the specified select treatment.
Does the table alone determine the premium?
No. Pricing also uses benefits, expenses, interest and other assumptions.
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