IS WORK OVER?

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

Back-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.

TaskShareAIPoints
Matching, settlement and the fail.
Estimate

No public source provides a precise figure. This is our estimate.

18%48%+8.6
Custody, corporates and the one that does not square.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Reconcile, the nostro and the gap.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Regulatory, the report and the deadline.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The desk, the rush and not leaving the fail.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
That it settles, and not hanging.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%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

Last-mile manager

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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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