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AI Automation Index
Will AI Replace Credit Analysts?
Task-level automation exposure for Credit Analysts: 11 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
69%
weighted task index
11 of 11 tasks scored
Median Salary
$83,510 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
64,390 people hold this job — size, not growth
Projected 2025–35
-4.3%
employment change
3,100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 52 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$51,920 – $133,270
Highest medians
New York $133,270
District of Columbia $132,590
Virginia $102,430
New Jersey $100,940
Hawaii $98,900
Lowest medians
Puerto Rico $51,920
West Virginia $57,320
Wyoming $62,210
Indiana $62,660
Missouri $65,350
State figures come from the 13-2041 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 69%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 59%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 24%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 5/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
11 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 83%
8 tasks
Medium exposure (≥ 35%) 17%
3 tasks
Low exposure (below 35%) 0%
0 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#1250 · Core · 70% automatable
Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.
data analysis — Analyzes structured financial data with tools
Assess risks to business operations. Analyze business or financial data.
#1254 · Core · 65% automatable
Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.
text generation — Draft transaction docs; human owns legal risk
Prepare contracts or other transaction documents.
#1255 · Core · 80% automatable
Generate financial ratios, using computer programs, to evaluate customers' financial status.
data analysis — Organizational calculations from given data
Calculate data to inform organizational operations.
#1251 · Core · 78% automatable
Prepare reports that include the degree of risk involved in extending credit or lending money.
data analysis — Reports and budgets drafted; human owns assumptions
Prepare financial documents, reports, or budgets.
#1259 · Core · 80% automatable
Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans.
data analysis — Analyzes structured financial data with tools
Analyze business or financial data.
#1257 · Core · 72% automatable
Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.
data analysis — Market trend analysis from provided data
Analyze market conditions or trends.
#1258 · Core · 72% automatable
Consult with customers to resolve complaints and verify financial and credit transactions.
text generation — Written correspondence to customers is largely automatable
Correspond with customers to answer questions or resolve complaints.
#21164 · Supplemental · 35% automatable
Contact customers to collect payments on delinquent accounts.
physical manipulation — In-person payment handling; some digital automation
Collect payments for goods or services.
#1252 · Supplemental · 59% automatable
Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.
data analysis — Financial status from documents, human verifies
Assess financial status of clients. Advise others on financial matters.
#1256 · Supplemental · 65% automatable
Review individual or commercial customer files to identify and select delinquent accounts for collection.
data analysis — Examines records for anomalies; human validates findings
Examine financial records.
#1253 · Supplemental · 55% automatable
Confer with credit association and other business representatives to exchange credit information.
decision support — Financial conferral aided; judgment and trust human
Confer with others about financial matters.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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