Automation index
How much of a Credit analyst’s job can AI already do?
A score and a balance sheet come earlier. Saying no to a friend of the director does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Credit analyst
26%
AI can already do 26% of this job.
#192 / 623
View rankingCredit analyst
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 |
|---|---|---|---|
Scoring, accounts and a first opinion.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 48% | +9.6 |
Visit, check and ask for more papers.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Committee, director and terms.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Monitoring, alerts and refinancing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Rules, circular and documentation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not lending to someone who will not pay back.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Scoring, accounts and a first opinion
- Visit, check and ask for more papers
- Committee, director and terms
- Monitoring, alerts and refinancing
- Rules, circular and documentation
- analista de credito
- credit analyst
Sources for this job· O*NET 13-2041.00· ESCO/ISCO 2413
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