Credit analysis and lending
ISCO-08 3312, 2413Credit analyst, Corporate credit analyst, Credit underwriter, Loan structuring analyst, Credit committee officer, Head of credit underwriting
Headcount need, in FTE
Target AI skills
level 1 to 4Job 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.
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.
Advanced settings modified
2026 2036
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

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.
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.
Seniority outlook
A flow model with three levels of experience in the profession. Promotion and exit rates are set so that today's mix stays stable when hiring does not change: any gap you see comes from the junior hiring cut alone.
Sources
- International Labour OrganizationGenerative AI and jobs: a refined global index of occupational exposure (2025)
- ILO datasetTask-level GenAI exposure scores by ISCO-08 occupation
- AnthropicAnthropic Economic Index (June 2026 release, April and May 2026 usage data)
- Stanford Digital Economy LabCanaries in the Coal Mine? Six facts about the recent employment effects of AI