Automation index
How much of a Actuary’s job can AI already do?
Running a model and a reserving report is shorter. Signing the assumptions and answering the supervisor is not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Actuary
32%
AI can already do 32% of this job.
#25 / 623
View rankingActuary
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 |
|---|---|---|---|
Run models, reserves and sensitivities.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 55% | +13.2 |
Draft technical reports and memos.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Choose assumptions, tables and criteria.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Explain to the board and the supervisor.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Validate data quality and bias.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
Signed opinion and professional liability.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 18% | +2.2 |
| Total | 100% | 32% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Run models, reserves and sensitivities
- Draft technical reports and memos
- Choose assumptions, tables and criteria
- Explain to the board and the supervisor
- Validate data quality and bias
- Signed opinion and professional liability
- actuario
- actuary
Sources for this job· O*NET 15-2011.00· ESCO/ISCO 2120
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