Credit Analysts
Business & Financial OperationsAI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.169
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.62
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 3% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Exposed tasks only| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.13-2041 | 0.0100100.0 | 0.00000.0 |
Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.13-2041 | 0.00000.0 | 0.0100100.0 |
Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.13-2041 | 0.00000.0 | 0.00000.0 |
Prepare reports that include the degree of risk involved in extending credit or lending money.13-2041 | 0.00000.0 | —0 |
| Not observed on any surface — 7 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval. | —0 | —0 |
Generate financial ratios, using computer programs, to evaluate customers' financial status. | —0 | —0 |
Consult with customers to resolve complaints and verify financial and credit transactions. | —0 | —0 |
Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans. | —0 | —0 |
Confer with credit association and other business representatives to exchange credit information. | —0 | —0 |
Review individual or commercial customer files to identify and select delinquent accounts for collection. | —0 | —0 |
Contact customers to collect payments on delinquent accounts. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Generate financial ratios, using computer programs, to evaluate customers' financial status.O*NET Task ID 1255 | 1.0 |
Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.O*NET Task ID 1250 | 0.5 |
Prepare reports that include the degree of risk involved in extending credit or lending money.O*NET Task ID 1251 | 0.5 |
Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.O*NET Task ID 1252 | 0.5 |
Confer with credit association and other business representatives to exchange credit information.O*NET Task ID 1253 | 0.5 |
Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.O*NET Task ID 1254 | 0.5 |
Review individual or commercial customer files to identify and select delinquent accounts for collection.O*NET Task ID 1256 | 0.5 |
Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.O*NET Task ID 1257 | 0.5 |
Consult with customers to resolve complaints and verify financial and credit transactions.O*NET Task ID 1258 | 0.5 |
Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans.O*NET Task ID 1259 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Occupation information
Recent Changes Related to This Occupation
Mar 2026: New Agentic Task Exposure (ATE) framework scores credit analysts among the highest-risk occupations (ATE 0.43-0.47) for agentic AI workflow displacement by 2030.
[Source: Gupta & Kumar (2026) Agentic AI and Occupational Displacement]These summaries were written by AI Changing Work from the source linked with each one, and any figures in them are given as AI Changing Work summarised them; they can differ from the source's own wording and from figures shown elsewhere on this page, so check the source before relying on them. AI Changing Work matched this page's occupation to an O*NET occupation and chose, by its own judgment, case by case, which summaries relate to that O*NET occupation; a summary appearing here does not mean that its source names this occupation.