Bill collector
AI Exposure Rank
77/100
Range 74–81/100 across source-weight sensitivity checks
Bill collector has an AI Exposure Rank of 77/100, meaning its work is more exposed to current AI capabilities than approximately 77% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss.
Clerical Support Workers·SGD 4,087/mo (2,497–5,950)·~3.8K 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
8% human advantage from judgment & presence
52% demand buffer from the local labour market
AI usage 14pp above 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 ILO occupational exposure), the Bill collector tasks most exposed include: data entry, invoice processing, appointment scheduling, document filing, and standard correspondence drafting.
- • Record information about financial status of customers and status of collection efforts.
- • Locate and notify customers of delinquent accounts by mail, telephone, or personal visits to solicit payment.
- • Advise customers of necessary actions and strategies for debt repayment.
O*NET tasks for this occupation with the most observed AI usage (Anthropic task data).
What AI can't do here
At 8% human bottleneck protection, the tasks that remain hardest to automate for Bill collector include: exception handling for non-standard requests, institutional knowledge of internal processes, coordinating across departments, and managing sensitive information.
Main insulation channels: Non-routine work + 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), ILO GenAI (2025), Pizzinelli et al. bottleneck model. Full methodology.
Singapore Now
Current labour market conditions and how they affect this role.
Cooling, but not collapsing. Vacancies are softer, yet retrenchment remains low and hiring still exceeds resignations.
Vacancy
3.1%
↓ 11.4% YoY
Hiring
2.6%
vs 1.6% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
78.5%
find work in 12mo· -1.6pp
Clerical, Sales & Service Workers · 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
Bill collector 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 77% of occupations · V8 AI Exposure Rank· Polytechnic / ITE Diploma
Raw scores
AIOE 1.040 · θ 0.597 · C-AIOE 0.924
Stability
watch · Optimistic 48% · Pessimistic 60%
Score range (best/worst case)
Exposure sensitivity 69–82% · Rank sensitivity 74–81/100 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 2,497 · Median 4,087 · 75th 5,950
Evidence & sources
Data matching
direct · SSOC 42141
Real-world AI usage: +14% vs estimated
Data quality
high evidence · 3 exposure sources · direct mapping
100% weighted task match · 24% effective coverage
AI overlap by data source
Weights: aioe 31% · anthropic 34% · ilo 35%
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 3.1% and fell by 0.2 points from last quarter.
- Hiring read: recruitment is running above resignation (2.6% vs 1.6%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 78.5% of retrenched workers re-entered employment within 12 months.
- Employer pressure is moderate, based on 3 recent Singapore-relevant company signals.
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 35% higherPublished June 2024 gross wage medians: male $3,432, female $4,648.
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.
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 Bill collector?
Bill collector has an AI Exposure Rank of 77/100, meaning its work is more exposed to current AI capabilities than approximately 77% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss. AI Exposure Rank: 77/100 (High). Median wage: SGD 4,087/month.
What is the AI exposure rank for Bill collector?
Bill collector has an AI Exposure Rank of 77/100, rated High. It ranks higher than approximately 77% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Bill collector?
Bill collector has modeled transition pathways to related occupations. The strongest adjacent pathway is Travel consultant/Reservation executive, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Bill collector salary compare in the live market?
Bill collector earns a median gross wage of SGD 4,087/month in the live market (25th-75th percentile: SGD 2,497-5,950). This is 9% below median across all 562 scored occupations, and 28% above group median within Clerical Support Workers occupations.