79/100
Very highHow much of this occupation’s work can be materially affected by current AI systems.
74/100
HighHow likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
Task-level evidence
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
TaskImportanceAI impactExposure
Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.High79
Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.High81
Generate financial ratios, using computer programs, to evaluate customers' financial status.High78
Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans.High79
Prepare reports that include the degree of risk involved in extending credit or lending money.High81
Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.High80
Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.Medium76
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.81
- Prepare reports that include the degree of risk involved in extending credit or lending money.81
- Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.80
- Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.79
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans.01
- Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.02
- Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.03
- Generate financial ratios, using computer programs, to evaluate customers' financial status.04
- Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.05
Where else this work leads
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
Methodology & sources
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
Confidence83/100
CalculatedAug 21, 2026
Read methodology →