When we request offers for group insurance, insurers look at two things first: how many people there are, and what their age composition looks like. Everything else – covers, tiers, premium – is built on these two data points. The good news: both can be compiled without names, in a simple spreadsheet, typically within half an hour.
Why exactly these two data points?
The essence of group insurance is the risk pool: the insurer does not weigh each person one by one, it prices the group as a whole. Headcount is therefore more than a number: in a larger group the risk is more predictable, and entry conditions are typically simpler too – while solutions exist for smaller teams as well, as the article on headcount thresholds explains. The age profile is key because the risk in life and illness covers changes with age – premium calculations typically capture this through the group's age and gender mix. The insurer typically prices the group as a whole rather than weighing each person individually – above the free cover limit (the sum insured up to which no individual risk assessment applies), or with a small headcount, however, it may still ask for an individual declaration or a medical examination.
How to compile it without names
For a quote request a numbered list is typically enough, one row per employee – without names: serial number, year of birth, gender, job role (plus the site, where there are several). In practice a row looks like this: "12 – 1988 – female – office role – Győr". A simple spreadsheet does the job, and payroll or HR records produce it in a few steps. Completeness matters: include colleagues who are currently away, and flag the borderline cases (probation, part-time) – the company sets the rules for them, and I show you the options. The full package worth preparing is collected in the quote request data list.
What does the insurer do with it?
From the list the insurer calculates the average age, the age and gender distribution and the job-role mix, and typically forms a group-level premium per insured person. The distribution says more than the average: two teams with the same average age can be priced differently if one has an even age profile and the other is dominated by two age extremes. The age and gender mix typically moves the price of the life and health covers; in the accident element, occupational risk carries more weight. This is why accurate data pays off: an offer built on estimates is adjusted to the actual data at launch anyway.
And if the team changes in the meantime?
An offer is always a snapshot – the contract, however, lives with the company: joiners and leavers are tracked with a monthly list, and the premium follows the actual headcount. After a bigger reshuffle (a hiring wave, downsizing, a new site) it is worth having the numbers reviewed at the anniversary. Precise data at the start also simplifies later administration: with a clean list the monthly update runs smoothly, and the premium notice reflects the real headcount. How the two key data points become an offer and then a working contract can be followed on the How it works page.
A question about your own team? Book a free 20-minute consultation →
