Hiring · August 7, 2026 · 10 min read

Insurance hiring: past the scary numbers you can't verify

Insurance hiring is drowning in retirement statistics that don't survive a source check. Here is what the data actually supports, and how to hire on it.

← Part of The five pillars of hiring: what assessments measure

If you lead talent or operations for an insurer, you have almost certainly been handed a slide with a frightening number on it: half the workforce retiring by a given year, a million-plus roles about to empty, a generation walking out with the institutional knowledge in their heads. Those numbers set budgets and panic hiring plans. The awkward part, and the reason this guide exists, is that most of them do not survive a source check. Insurance hiring has a genuine demographic challenge, but it has been dressed up in statistics that trace back to nobody. Before you plan around a cliff, it is worth knowing which edge is real. This guide does the unglamorous thing first — it separates the claims you can defend from the ones you cannot — and then works from the defensible ones to a practical answer: in an ageing, shrinking-but-still-hiring profession, every hire has to count, so the screening stage is where the effort belongs.

Is the insurance retirement crisis real, or a statistic that got out of hand?

Both, in a specific way: the ageing is real, the famous figures are not. You have probably seen the claim that half the insurance workforce retires by 2028, or that hundreds of thousands of roles empty within a couple of years. We went looking for a primary source for those and could not find one — several are even misattributed to the US Bureau of Labor Statistics, which never published them. Treat them as folklore, and do not build a plan on a number you cannot cite.

This matters more than pedantry, because a headcount plan is only as sound as the figure underneath it. When a statistic circulates for years without an owner, it tends to be either badly out of date or invented to sell a service, and either way it will mislead your budget. The honest move is to say plainly which numbers you are standing on. So here is the one demographic figure that does hold up: according to US Bureau of Labor Statistics workforce data, roughly one in four insurance workers is 55 or older, making it one of the older workforces in the economy. That is directional, not a countdown — no precise headcount, no retirement date — but it is real, and it is enough to act on.

What does the verifiable insurance data actually say?

It describes a profession that is contracting and hiring hard at once. The US Bureau of Labor Statistics Occupational Outlook Handbook, in its 2024 to 2034 projections, expects claims-occupation employment to decline by about 5% — and still projects around 21,500 openings a year in claims roles. Those openings are almost entirely replacement demand, not growth: people leaving an ageing workforce faster than the total shrinks. Fewer seats overall, yet a steady stream to fill.

Sit with how unusual that combination is, because it shapes everything about how you should hire. A shrinking occupation would normally mean a comfortable buyer's market — plenty of experienced candidates, little urgency. The replacement dynamic breaks that intuition. The reason the seats keep opening is that the people vacating them are the experienced ones, and the reason the total is falling is that automation is absorbing the routine end of the work. So you are competing for a thinning layer of experienced talent to backfill roles that are themselves changing shape. Every hire in that picture carries more weight than the raw headcount suggests, because each one is either preserving institutional knowledge or losing it.

It is worth naming the sibling pattern too, without re-citing its numbers. Finance and insurance sit together as one of the lowest-turnover corners of the economy, which is a double-edged thing: people rarely leave, so you hire infrequently, but that same stability means the experience concentrated in each seat is deep and hard to replace when it finally does go. We work through the low-quits, high-stakes side of that in the companion finance and fintech hiring guide; the insurance twist is that the departures, when they come, are increasingly driven by age rather than churn — which makes them predictable, and therefore plannable, in a way ordinary attrition is not.

Why does screening on an insurance CV shrink the pool you can least afford to shrink?

Because it filters for a résumé the market is running short of, and rejects capable people who could do the job. When experienced insurance candidates are the scarce resource, insisting on prior insurance experience as your first gate simply thins an already thin pool. The work — reading a claim, applying a rule, treating numbers with care — is learnable, and plenty of able people outside the sector can be shown to already have it.

Think about what a claims examiner or an underwriting assistant actually does on a Tuesday. They read a document for the one fact that changes the outcome. They apply a policy rule consistently, even when the case is sympathetic or the file is messy. They notice when a number does not reconcile. None of those capabilities carries an insurance logo on it. A former banking operations analyst, a healthcare claims administrator, a logistics coordinator who has spent years applying rules to edge cases — these people can often demonstrate the underlying reasoning immediately, if you let them demonstrate it rather than screening them out at the CV for lacking the exact prior title. Skills-based hiring is not a slogan here; it is the only way to widen a pool that demographics are steadily narrowing. The general case for putting demonstrated ability ahead of pedigree is set out in the cost of a bad hire argument — in insurance, the bad hire is expensive in the same quiet, compounding way a mis-set reserve is.

There is a speed dimension too, and it cuts against the buyer's-market instinct. The experienced insurance candidate you do surface is not waiting patiently. Scarce, capable people collect multiple offers, so a fortnight of CV-sifting and unstructured rounds is a fortnight in which a sharper rival closes them. The point of testing for the work early is partly to widen the pool and partly to move quickly through it — reaching a defensible shortlist in days, so your evaluation is not the reason a good hire got away.

What does the AI era change for claims and underwriting hiring?

It moves the routine drafting to the machine and raises the price of good judgement. AI now drafts claims summaries and underwriting notes in seconds, usually competently and occasionally with confident errors. So the scarce, valuable human skill is no longer producing the summary — it is reviewing one: catching the misread clause or the figure that does not reconcile before it reaches a claimant, an insured, or a regulator.

This reframes what you are actually hiring for, and it reframes what a polished application proves — which is nothing. When any candidate can generate an immaculate covering note and a tidy CV from a chatbot, those artefacts tell you only that the person can access a language model. The signal you need sits one layer deeper: does this candidate treat a machine-generated draft as a finished answer, or as a first draft to interrogate? In claims and underwriting, where a plausible-looking summary can carry a subtle, costly mistake, that instinct is close to the whole job. It is the AI fluency signal that now runs through every knowledge role, but here it is the difference between a draft that gets quietly signed off and a mistake that gets caught. We go deeper on measuring it in how to assess AI fluency.

How do specialised assessments help when every hire has to count?

By letting you evaluate the whole applicant pool — insiders and career changers alike — on the actual work, in parallel, rather than reading CVs one at a time. Two capabilities carry it. Per-job generation makes a test shaped to this specific claims desk or underwriting team economical to produce. Parallel evaluation measures every candidate on the same terms at once, so a defensible shortlist arrives in days, not weeks.

For a sector where the scarce resource is judgement and the incoming pool is unfamiliar, that combination matters. A generic aptitude battery would tell you something adjacent to the job; an assessment built from the role's real requirements can put a candidate in front of a genuine claim or a real underwriting scenario and watch how they reason. A situational judgement test surfaces how someone applies a rule under ambiguity — the sympathetic-claimant-but-clear-exclusion case, the incomplete file, the pressure to say yes — and domain-specific candidate evaluation shows whether they read for the load-bearing fact and treat numbers with the care the work demands. This is the practical face of skills-based hiring: a career changer and a fifteen-year veteran take the same job-relevant assessment, and you see the capability rather than the CV. The broader evidence that structured, work-shaped evaluation beats interviews for signal is laid out in what work sample tests are and how to use them.

Because the modern claims or underwriting professional works with AI tools at hand, it is worth running the assessment where those tools are genuinely present — a work-sample session in which a machine-generated draft is part of the material. Then you observe the thing that actually predicts performance now: not whether they can produce a summary, but whether they interrogate the one the machine produced. That behaviour is only visible by watching the work, which is exactly what a job-relevant assessment is for.

What insurance assessments will not fix

Plenty, and being straight about it is the whole point of a post that started by debunking numbers. A job-relevant assessment compresses and sharpens one stage — evaluating whether someone can do the work — and it genuinely widens the pool. It does not manufacture experienced actuaries the market is not producing, it does not verify a licence, and it will not, on its own, reverse a demographic trend decades in the making. Naming the limits is what makes the claims that do hold up worth trusting.

• Licensing and credential checks — confirming an adjuster's licence, an actuarial qualification or a clean regulatory record is a verification task, not a skills one. An assessment measures reasoning and judgement; it does not authenticate a certificate.
• Genuine specialist scarcity — for a role that needs a qualified actuary or a niche reinsurance underwriter, a faster, fairer funnel reaches an empty market sooner. Testing cannot conjure a candidate who is not applying.
• The knowledge-transfer problem — a sharp new hire is not a like-for-like replacement for thirty years of institutional memory. Assessment finds capable people; deliberate handover and documentation are what actually move the knowledge before it retires.
• Pay and progression — misprice a claims or underwriting seat against a market competing hard for the same thinning pool, and a strong candidate walks whatever your evaluation showed.

So the honest close mirrors the honest open. Ignore the scary, sourceless numbers; take the real one — an ageing workforce quietly draining experience, projected to keep needing roughly 21,500 claims hires a year even as the occupation shrinks — and let it point you somewhere useful. In a profession where every hire counts and knowledge leaves with each retirement, the leverage is not a bigger funnel or a louder statistic. It is evaluating the widest defensible pool on the actual work, quickly enough that the capable people you reach do not get away. To see per-job generation and parallel evaluation run against one of your own claims or underwriting roles, book a demo.

The frightening insurance statistics mostly trace back to nobody; the real one is quieter. A quarter of the workforce is 55 or older, and it is leaving with the knowledge in its head. So what will actually answer that — another scare slide, or testing the widest honest pool for the judgement the work needs and moving quickly on the capable people you find?