Insurance underwriter
AI Exposure Rank
78/100
Range 70–95/100 across source-weight sensitivity checks
Insurance underwriter has an AI Exposure Rank of 78/100, meaning its work is more exposed to current AI capabilities than approximately 78% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss.
Professionals·SGD 6,991/mo (4,800–12,200)·~4.7K workers in SG·Updated 2026-06-11
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
Why This Score
77% of tasks overlap with current AI
14% human advantage from judgment & presence
55% demand buffer from the local labour market
AI usage 37pp below theoretical exposure
These factors interact with each other — the final score is not a simple sum of these bars.
The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. Score stability: watch. How this works
Tasks AI can handle
With 77% AI task overlap (based on Felten AIOE, Anthropic Economic Index, and Eloundou GPT exposure), the Insurance underwriter tasks most exposed include: financial modeling, data extraction from filings, ratio analysis, report generation, transaction categorization, and regulatory document summarization.
- • Decline excessive risks.
- • Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.
- • Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.
O*NET tasks for this occupation with the most observed AI usage (Anthropic task data).
What AI can't do here
At 14% human bottleneck protection, the tasks that remain hardest to automate for Insurance underwriter include: judgment on risk vs. return, client advisory relationships, regulatory interpretation in edge cases, fraud detection in novel scenarios, and strategic capital allocation.
Main insulation channels: Deep preparation + High-stakes decisions — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), Eloundou GPT Exposure (Science, 2024), Pizzinelli et al. bottleneck model. Full methodology.
Singapore Now
Current labour market conditions and how they affect this role.
Cooling, but not collapsing. Vacancies and re-entry are softer, yet retrenchment remains low and hiring still exceeds resignations.
Vacancy
3.1%
↓ 3.1% YoY
Hiring
1.5%
vs 0.9% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
67.7%
find work in 12mo· -5.3pp
Professionals, Managers, Executives & Technicians · 2025 Q4
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Insurance underwriter has some offset potential, but it depends on transition pathways holding up in practice and on workers clearing the main switching frictions.
Published transition support
Related roles you could transition to
Exposure-reducingHigher AI exposure, but comparatively credible exposure-reducing moves exist — the strongest scores 67% match. Escape-route quality and labour demand matter alongside exposure.
Compare within Professionals
Modern roles using this occupation
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 77% of occupations · V8 AI Exposure Rank· University Degree
Raw scores
AIOE 1.327 · θ 0.621 · C-AIOE 1.147
Stability
watch · Optimistic 44% · Pessimistic 56%
Score range (best/worst case)
Exposure sensitivity 63–87% · Rank sensitivity 70–95/100 across source-weight sensitivity checks
Scoring basis
V8 AI Exposure Rank. A relative Singapore occupation index. It ranks AI task exposure; it is not a probability of job loss or a percentage of tasks.
Wage range (SGD/mo)
25th 4,800 · Median 6,991 · 75th 12,200
Evidence & sources
Data matching
direct · SSOC 24160
Real-world AI usage: -37% vs estimated
Data quality
medium evidence · 3 exposure sources · direct mapping
Capped at high · Final rating: medium · capped for conflicting signals
100% weighted task match · 16% effective coverage
AI overlap by data source
Weights: aioe 32% · anthropic 35% · eloundou 33%
Conflicting data signals
Tools & offset factors
Common tools (O*NET proxy)
What could slow it down
- Current demand support is thin, so offsets may take longer to show up.
Worker profile & local context
- Vacancy rate is 3.1% and was essentially flat versus last quarter.
- Hiring read: recruitment is running above resignation (1.5% vs 0.9%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 67.7% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 2 recent Singapore-relevant company signals.
Worker profile
Gender mix
41% male / 59% femalePublished Singapore worker composition for the detailed occupation family 24 Business & Administration Professionals.
Employment structure
Employee-heavy96% employees, 4% employers or self-employed workers.
Work arrangement
Mostly full-time4% part-time and 96% full-time in 2025.
Age profile
Mid-career heavy14% aged 15 to 29, 62% aged 30 to 49, and 24% aged 50 or older.
Qualification mix
Degree-heavyDegree 81%; Diploma / professional qualification 15%.
Gross wage by sex
Near parityPublished June 2024 gross wage medians: male $6,857, female $6,996.
Where this work is concentrated
Top planning areas
Sengkang, Bedok, Tampines19% of workers in this occupation group live in these three planning areas.
Residential concentration
Broadly distributed30% live across the top five planning areas in the 2020 Census.
Commute pattern
Mid-range commutesEstimated average commute 37.5 minutes. 33% take 46 minutes or more.
Role profile
How this role's work breaks down across key dimensions. This is a general profile, not an individual measurement.
Workflow dimensions (0 = low, 1 = high)
How this changes by career stage
Career stage can change the task mix and human context. These directional profiles are illustrative, not occupation-level forecasts of hiring or displacement.
Frequently asked questions
Will AI replace Insurance underwriter?
Insurance underwriter has an AI Exposure Rank of 78/100, meaning its work is more exposed to current AI capabilities than approximately 78% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss. AI Exposure Rank: 78/100 (High). Median wage: SGD 6,991/month.
What is the AI exposure rank for Insurance underwriter?
Insurance underwriter has an AI Exposure Rank of 78/100, rated High. It ranks higher than approximately 78% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Insurance underwriter?
Insurance underwriter has modeled transition pathways to related occupations. The strongest adjacent pathway is Financial product structurer, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Insurance underwriter salary compare in the live market?
Insurance underwriter earns a median gross wage of SGD 6,991/month in the live market (25th-75th percentile: SGD 4,800-12,200). This is 55% above median across all 562 scored occupations, and 8% above group median within Professionals occupations.