Money changer
AI Exposure Rank
67/100
Range 53–81/100 across source-weight sensitivity checks
Money changer has an AI Exposure Rank of 67/100, meaning its work is more exposed to current AI capabilities than approximately 67% of Singapore occupations. The evidence currently points to hiring or substitution pressure; this is a relative rank, not a probability of job loss.
Clerical Support Workers·SGD 3,750/mo (2,812–5,876)·~3.7K workers in SG·Updated 2026-07-17
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
How This Rank Is Built
The source percentiles are combined using the displayed reliability weights, then ranked against all 562 Singapore occupations. This is not a percentage of tasks and not a job-loss probability.
Likely job pathway
Hiring or substitution pressure
Current demand context
Mixed current demand
Pathway and demand are reported beside the exposure rank. Current demand does not change the rank.
The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. How this works
Tasks AI can handle
With 67% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Money changer tasks most exposed include: data entry, invoice processing, appointment scheduling, document filing, and standard correspondence drafting.
- • Raise vehicles, using hydraulic jacks.
- • Remount wheels onto vehicles.
- • Unbolt and remove wheels from vehicles, using lug wrenches or other hand or power tools.
O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).
What AI can't do here
At 19% human bottleneck protection, the tasks that remain hardest to automate for Money changer include: exception handling for non-standard requests, institutional knowledge of internal processes, coordinating across departments, and managing sensitive information.
Main insulation channels: High-stakes decisions + Non-routine work — 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), ILO GenAI (2025), Pizzinelli et al. bottleneck model. Full methodology.
From exposure to employment
What could change the employment outcome?
AI capability is only the starting point. Adoption, task design, human oversight, demand, mobility and evidence quality determine how exposure may resolve in practice.
Workplace adoption
Leading sectors represented
All listed industries have MOM sector adoption evidence; observed-sector average 56.4%.
Task structure
Task evidence limited
100% weighted task match · 0% effective coverage
Human advantage
Coordination and judgment still matter
At 19% human bottleneck protection, the tasks that remain hardest to automate for Money changer include: exception handling for non-standard requests, institutional knowledge of internal processes, coordinating across departments, and managing sensitive information.
Hiring and demand
3.2% vacancy rate · 2026 Q1
Clerical, sales and service vacancies recovered slightly from Q4 but remained well below a year earlier. Hiring turnover slowed, retrenchment incidence stayed low, and six-month re-entry improved. In 2025, this broad occupation group had 1.9% time-related underemployment and 12.1% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed +0.1% to +3.9% from 2018 to 2023.
Career transitions
Bank teller
moderate modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
medium confidence
4 exposure sources · sensitivity 53–81/100 across source-weight sensitivity checks.
These lenses are reported separately and do not alter AI Exposure Rank. Labour-market figures describe a broad official occupation cluster, not this SSOC occupation alone.
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
3.2%
↓ 20.0% YoY
Hiring
2.1%
vs 1.4% resign
Retrenchment
0.7
per 1,000 · low
Re-entry
68%
find work in 12mo· -10.5pp
Clerical, Sales & Service Workers · 2026 Q1
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Money changer 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-reducingThis occupation has higher relative AI exposure, and its best adjacent move ranks in the weakest quarter of exposure-reducing options. Mobility outcomes also depend on demand, wages, skills and access to credible transitions. See all occupations in this quadrant.
Compare within Clerical Support Workers
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 66% of occupations · V8 AI Exposure Rank· GCE O-Level / Secondary
Raw scores
AIOE 0.473 · θ 0.635 · C-AIOE 0.402
Stability
stable · Optimistic 37% · Pessimistic 48%
Score range (best/worst case)
Exposure sensitivity 52–81% · Rank sensitivity 53–81/100 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 2,812 · Median 3,750 · 75th 5,876
Evidence & sources
Data matching
direct · SSOC 42113
Real-world AI usage: -13% vs estimated
Data quality
medium evidence · 4 exposure sources · direct mapping
Capped at high · Final rating: medium · capped for conflicting signals
100% weighted task match · 0% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Conflicting data signals
Tools & offset factors
What helps
- Nearby moves and published transition support look reasonably strong.
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 3.2% and rose by 0.1 points from last quarter.
- Hiring read: recruitment is running above resignation (2.1% vs 1.4%).
- Retrenchment was low at 0.7 per 1,000 employees.
- 68% of retrenched workers re-entered employment within 12 months.
- Employer pressure is moderate, based on 3 captured Singapore-relevant company signals through 2025-06-18.
Worker profile
Gender mix
24% male / 76% femalePublished Singapore worker composition for the detailed occupation family 42 Customer Services Officers & Clerks.
Employment structure
Employee-heavy99% employees, 1% employers or self-employed workers.
Work arrangement
Mostly full-time13% part-time and 87% full-time in 2025.
Age profile
Older-skewing16% aged 15 to 29, 35% aged 30 to 49, and 49% aged 50 or older.
Qualification mix
Mixed qualificationsSecondary 29%; Diploma / professional qualification 28%.
Gross wage by sex
Female median 11% lowerPublished June 2024 gross wage medians: male $4,000, female $3,563.
Where this work is concentrated
Top planning areas
Jurong West, Tampines, Woodlands22% of workers in this occupation group live in these three planning areas.
Residential concentration
Moderately clustered35% live across the top five planning areas in the 2020 Census.
Commute pattern
Longer commutesEstimated average commute 39.7 minutes. 38% 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.
General role profile on a 0–100 scale. These are modelling inputs, not measurements of an individual job or worker.
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 Money changer?
Money changer has an AI Exposure Rank of 67/100, meaning its work is more exposed to current AI capabilities than approximately 67% of Singapore occupations. The evidence currently points to hiring or substitution pressure; this is a relative rank, not a probability of job loss. AI Exposure Rank: 67/100 (High). Median wage: SGD 3,750/month.
What is the AI exposure rank for Money changer?
Money changer has an AI Exposure Rank of 67/100, rated High. It ranks higher than approximately 67% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Money changer?
Money changer has modeled transition pathways to related occupations. The strongest adjacent pathway is Bank teller, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Money changer salary compare in the live market?
Money changer earns a median gross wage of SGD 3,750/month in the live market (25th-75th percentile: SGD 2,812-5,876). This is 17% below median across all 562 scored occupations, and 18% above group median within Clerical Support Workers occupations.