What will AI change in your workforce?

Your company's stance on AI(applies to every family: sets the adoption speed)

Credit analysis and lending

ISCO-08 3312, 2413

Credit analyst, Corporate credit analyst, Credit underwriter, Loan structuring analyst, Credit committee officer, Head of credit underwriting

AI exposure61%

Headcount need, in FTE

Exposure range
FTE
Need in 203083 FTE-17 FTE (-17%)
Need in 203567 FTE-33 FTE (-33%)
10080604020262028203020322035
How AI is used on these tasks todayIn AI conversations about this job's tasks: the AI does the task itself (automation) or helps the person do it (augmentation).Anthropic Economic Index, April and May 2026. Occupations: Credit Analysts, Loan Officers. AEI
Average automation potential of the job's tasks. Range for this family: 56 to 68%.ILO occupations used: Credit and Loans Officers (60%), Financial Analysts (62%). ILO data
Your call: how much of this potential do you want to capture? Nobody can set it for you.
3. Adoption speed midpoint 2030
Inherits your company stance, adjustable for this family.
Default 55%: Loan volumes depend on the market, not on analyst capacity; faster analysis mostly reduces staffing, while part funds closer portfolio monitoring.

Target AI skills

level 1 to 4
Foundation
Use AI assistants every day
Frame and phrase a request
Check and challenge AI outputs
Protect data and respect the rules
Applied
Rethink one's process with AI
Analyse data with AI
Produce content with AI
Delegate to and supervise AI agents
Expert
AI governance and risk management

Job skills

Growing in value

  • Judgement on atypical and large exposures
  • Defending a credit opinion in committee
  • Challenging model scores and AI-drafted memos
  • Sector and collateral expertise

Losing value

  • Spreading financial statements
  • Drafting standard credit memos
  • Manual covenant and ratio checks
  • Assembling annual review files

How the job will change

Much of an analyst's time goes into spreading balance sheets, recalculating ratios, checking covenants and writing a credit memo in a fixed template. AI now reads financial statements, fills the spreading model, drafts the memo and points out weaknesses. In retail and small business lending, the decision itself is increasingly automated within delegated limits, leaving analysts the files that fall outside the rules.

The job concentrates on large, complex or unusual exposures: LBO financing, property developers, groups in difficulty. A good analyst tomorrow knows when the model is wrong, can argue an opinion in committee and understands a sector well enough to see what the figures hide. Requirements on model explainability also bring analysts closer to the risk teams.

2026-2027
Automated spreading and pre-drafted credit memos on standard files.
2028-2030
Small-ticket decisions automated within delegations; analysts focus on exceptions.
2031+
Smaller teams of senior analysts on complex and large exposures.
Watch out

Credit judgement is learned on hundreds of ordinary files. If juniors no longer handle them, banks risk a generation of analysts unable to challenge the models they supervise. See the seniority outlook below.

What if you hired fewer juniors?

Your 2036 seniors are the juniors you hire today.

Seniors available in 2036-5%2 FTE short
Gap above 5% from2036
Your seniority mix today
Mid-level (3 to 10 yrs in the profession, the remainder)45%
For
Advanced settings
If every company makes the same bet, senior profiles will be scarce and expensive.
80601002026203020342040Senior need (held stable)Seniors available
Seniority mix, % of today's headcount
Juniors25%Mid-level42%Seniors28%

2026 2036

Free guide

The AI Cookbook 2026

albert's guide to cut through the noise around generative AI and turn it into workforce decisions.

  • What AI actually changes in jobs and skills
  • Why the junior pipeline matters more than most companies realise
  • How to integrate AI into workforce planning
Download the AI Cookbook
The AI Cookbook 2026, albert

Run these scenarios on your actual workforce

AI Impact Diagnostic: 6 to 8 weeks, your data in albert, three costed scenarios and the projected seniority mix for each job family.

Book a call

How the numbers are built

Every headcount figure combines four assumptions. Three come pre-filled from public research and job family defaults. The strategic ceiling is yours to set.

Public researchExposureAverage automation potential of the job's tasks, from the ILO's 2025 task-level scores.
Your decisionStrategic ceilingHow much of that potential you choose to capture. Your decision, not ours.
Your companyAdoption speedHow fast your company moves, set once for the whole company.
Job family defaultConversion to headcountHow much of the productivity gain becomes fewer people rather than more output.
Headcount effect

My simulation

Add the job families you want to compare. Your PDF report covers every family in your simulation, with the assumptions you saved.

albert

Get your AI impact report

A PDF built from the families and assumptions you set. It covers every family you adjusted.

Your settings are saved with your request, so whoever calls you starts from your own assumptions.