Reinsurance
ISCO-08 2413, 3321Reinsurance underwriter, Reinsurance buyer, Treaty analyst, Facultative underwriter, Reinsurance accountant, Head of reinsurance
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Programme structuring and cession strategy
- Negotiating with reinsurers and brokers at renewal
- Assessing catastrophe and emerging risk exposures
- Critical reading of capital model outputs
Losing value
- Preparing renewal submissions and data packs
- Technical accounts and reinsurance statements
- Reviewing treaty wordings clause by clause
- Tracking recoveries and premium bordereaux
How the job will change
Much reinsurance work is document-heavy: renewal submissions, exposure data, treaty wordings, technical accounts, bordereaux, recovery claims on large losses. AI now prepares submission packs, compares wordings with the previous year to flag changed clauses, reconciles statements with reinsurers and tracks recoveries. Treaty analysts and reinsurance accountants see the largest share of their tasks absorbed.
The decisions stay human: how much risk to retain, how to structure a programme, which reinsurers to trust over time. A good reinsurance professional tomorrow will read catastrophe model outputs with a critical eye, understand the capital effects of a structure and maintain the long relationships with reinsurers and brokers that make the difference at difficult renewals.
- 2026-2027
- Automated preparation of renewal submissions and comparison of treaty wordings.
- 2028-2030
- Technical accounting and recoveries largely automated, smaller support teams.
- 2031+
- Small senior teams focused on structuring, capital and negotiation.
Reinsurance expertise rests on a few senior people who carry the market relationships. Automating analyst roles saves costs but weakens succession for these positions, which are hard to fill externally. See the seniority outlook below.
What if you hired fewer juniors?
Your 2036 seniors are the juniors you hire today.
Advanced settings modified
2026 2036
The AI Cookbook 2026
albert's guide to cut through the noise around generative AI and turn it into workforce decisions.
- What AI actually changes in jobs and skills
- Why the junior pipeline matters more than most companies realise
- How to integrate AI into workforce planning

Run these scenarios on your actual workforce
AI Impact Diagnostic: 6 to 8 weeks, your data in albert, three costed scenarios and the projected seniority mix for each job family.
How the numbers are built
Every headcount figure combines four assumptions. Three come pre-filled from public research and job family defaults. The strategic ceiling is yours to set.
Seniority outlook
A flow model with three levels of experience in the profession. Promotion and exit rates are set so that today's mix stays stable when hiring does not change: any gap you see comes from the junior hiring cut alone.
Sources
- International Labour OrganizationGenerative AI and jobs: a refined global index of occupational exposure (2025)
- ILO datasetTask-level GenAI exposure scores by ISCO-08 occupation
- AnthropicAnthropic Economic Index (June 2026 release, April and May 2026 usage data)
- Stanford Digital Economy LabCanaries in the Coal Mine? Six facts about the recent employment effects of AI