Automation index
How much of a Banker’s job can AI already do?
A score and a dossier come earlier. Looking the client in the eye, saying no, and owning the risk do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Banker
27%
AI can already do 27% of this job.
#135 / 623
View rankingBanker
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 |
|---|---|---|---|
Client file, scoring and desk KYC.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Product proposals, simulation and reporting.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Meetings, negotiation and close.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Risk calls, exceptions and escalation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Compliance, audit and the file.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
Book, referrals and long relationship.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Client file, scoring and desk KYC
- Meetings, negotiation and close
- Risk calls, exceptions and escalation
- Compliance, audit and the file
- Book, referrals and long relationship
- banquero
- banker
- gestor de banca
- private banker
Sources for this job· O*NET 13-2052.00· ESCO/ISCO 2412
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