Automation index
How much of a Back-office clerk’s job can AI already do?
A match comes earlier. The trade settling without hanging do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Back-office clerk
27%
AI can already do 27% of this job.
#180 / 623
View rankingBack-office clerk
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Matching, settlement and the fail.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Custody, corporates and the one that does not square.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Reconcile, the nostro and the gap.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Regulatory, the report and the deadline.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The desk, the rush and not leaving the fail.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it settles, and not hanging.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- matching
- settlement
- custody
- reconcile nostro
- fails
- corporate actions
- regulatory reports
- hung trades
Sources for this job· O*NET 43-4011.00· ESCO/ISCO 4312
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511