Credit Analysts
Business and Financial Operations Occupations- O*NET-SOC code
- 13-2041.00
Analyze credit data and financial statements of individuals or firms to determine the degree of risk involved in extending credit or lending money. Prepare reports with credit information for use in decisionmaking.
- 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
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
- 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
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
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
11 rated tasks · 11 tasks with β ≥ 0.5 (100.0%)
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
- Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
- All rated tasks are in the O*NET 31.0 task list.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Release
- gh-main-0471612
- License
- MIT License, Copyright (c) 2024 OpenAI
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 (O*NET 27.2) | 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 |
Contact customers to collect payments on delinquent accounts.O*NET Task ID 21164 | 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®, Eloundou et al. (2023): see full notices on the Credits page
All O*NET 31.0 task statements (11)
All O*NET 31.0 task statements for this occupation (English, as published; task type core or supplemental). Exposure values are not listed here; they are in the table above, next to each task statement as worded in the O*NET version each source used.
| Task (O*NET 31.0) | Type |
|---|---|
| Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money. | Core |
| Prepare reports that include the degree of risk involved in extending credit or lending money. | Core |
| Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval. | Core |
| Generate financial ratios, using computer programs, to evaluate customers' financial status. | Core |
| Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations. | Core |
| Consult with customers to resolve complaints and verify financial and credit transactions. | Core |
| Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans. | Core |
| Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity. | Supplemental |
| Confer with credit association and other business representatives to exchange credit information. | Supplemental |
| Review individual or commercial customer files to identify and select delinquent accounts for collection. | Supplemental |
| Contact customers to collect payments on delinquent accounts. | Supplemental |
0.00/ 3· Robot exposure index
Scale, basis and source
Each physical task is graded 0–3 by the setting in which robots that have been deployed, sold or demonstrated could do it (0 none · 1 purpose-built robot settings such as a fenced machine cell or a bottling line · 2 structured facilities such as a hospital pharmacy or a container port · 3 unstructured settings such as a private home or a construction site), and the grades are averaged by share of working time
Physical tasks among this occupation's O*NET 29.3 tasks
Anthropic estimated the grades and time shares with Claude Opus 5 using web search, as of 2026.
The robot exposure index is a different measure from the AI exposure figures on this site and cannot be added to or compared with them. It does not measure actual adoption or cost, and 0 also covers occupations with no physical tasks.
Tier of each physical task
None of this occupation's O*NET 29.3 tasks is classified as a physical task.
Occupation information
Employment outlook (BLS)
- Employment, 2025
- 65K
- Projected employment, 2035
- 62K
- Change (2025–35)
- -4.3%
- Median annual wage, 2025
- $83,510
- Typical education needed for entry
- Bachelor's degree
Korea classification links (KSCO)
Links from the current release of the Korea Occupation Direct Crosswalk v1.1, made by AI Changing Work. Relation values are read from the O*NET-SOC occupation side.
| KSCO code | KSCO title | Relation | Status |
|---|---|---|---|
| 28212 | 신용 분석가 | Same scope | confirmed |
Relation and status definitions(kr-direct v1.1)
ILO occupational description
AI Changing Work links the ISCO-08 unit groups below to this occupation by applying the U.S. Bureau of Labor Statistics crosswalks (2018 SOC to 2010 SOC, 2010 SOC to ISCO-08) as published, without case-by-case selection; each link may be whole or partial.
- ISCO-08 2413Financial Analysts
The definition, tasks and examples below are the ILO's description of that unit group, not of this occupation.
Financial analysts conduct quantitative analyses of information affecting investment programmes of public or private institutions.
Tasks
- (a) analysing financial information to produce forecasts of business, industry, and economic conditions for use in making investment decisions;
- (b) maintaining knowledge and staying abreast of developments in the fields of industrial technology, business, finance, and economic theory;
- (c) interpreting data affecting investment programmes, such as price, yield, stability, future trends in investment risks and economic influences;
- (d) monitoring economic, industrial, and corporate developments through analysis of information obtained from financial publications and services, investment banking firms, government agencies, trade publications, company sources and personal interviews;
- (e) recommending investments and investment timing to companies, investment firm staff or the investing public;
- (f) determining the prices at which securities should be syndicated and offered to the public;
- (g) preparing plans of action for investment based on financial analyses;
- (h) evaluating and comparing the relative quality of various securities in a given industry;
- (i) presenting oral and written reports on general economic trends, individual corporations, and entire industries.
Example occupations
Bond analyst · Investment analyst · Securities consultant
Sources: ILO ISCO-08 — details: Credits
ESCO occupational vocabulary
Each entry below is matched to this occupation in the published ESCO-O*NET Crosswalk (version 1, 2022). The match types are that crosswalk's; the wording is AI Changing Work's.
- credit analystESCO 3312.2.1Same scope
Credit analysts investigate credit applications from customers and evaluate if the applications comply with regulations and guidelines of the financial loan-granting institution. On the basis of credit analyses they advise financial institutions whether customers are loan worthy. They perform tasks such as collecting data on the loan applicant, aquire additional information from other departments or institutions and indicating what sort of agreements the financial institution should reach with the credit applicant. Credit analysts also follow up on the development of the credit portfolio of clients.
- credit risk analystESCO 3312.4This occupation is broader
Credit risk analysts manage individual credit risk and care for fraud prevention, business deal analysis, legal documents analysis and recommendations on the level of the risk.
- loan officerESCO 3312.5This occupation is broader
Loan officers assess and authorise the approval of loan applications for individuals and businesses. They ensure complete transactions between loan organisations, borrowers, and sellers. Loan officers are specialists in consumer, mortgage, or commercial lending.
- credit adviserESCO 3312.2Overlapping, not the same
Credit advisers offer guidance to customers related to credit services. They assess the customer's financial situation and debt issues arisen from credit cards, medical bills and car loans in order to identify optimal credit solutions for customers and also provide debt elimination plans to adjust their finances if needed. They prepare qualitative credit analyses and decision-making material in respect of defined customers in conformity with the bank's strategy on credit policy, ensure the credit quality and follow up on the performance of the credit portfolio. Credit advisers also have expertise in debt management and credit consolidation.
- credit managerESCO 3312.3Overlapping, not the same
Credit managers oversee the application of credit policy in the bank. They decide the credit limits to be imposed, the reasonable levels of risk accepted and the conditions and terms of payment made to the customers. They control the collection of payments from their customers and manage the credit department of a bank.
- mortgage loan underwriterESCO 3312.6Overlapping, not the same
Mortgage loan underwriters ensure compliance with underwriter guidelines. They participate in the implementation of new underwriting guidelines. They also review closed and denied loans.
Source: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
License: CC BY 4.0 (EU Reuse Notice)
ESCO reuse terms and the modification notice are in Credits.
France classification links (ROME)
AI Changing Work's links between this occupation and ROME 4.0 job profiles, judged with AI models. Relations are read from this occupation's side. Links marked “provisional” were adopted on the judgments of two judges from the same model family (Claude) alone, or by an operator rule applied to their judgments, so they rest on weaker evidence than confirmed links.
| ROME code | ROME title (French) | Relation |
|---|---|---|
| C1202 | Analyste de crédits et risques bancaires | Same scopeprovisional |
| C1214 | Chargé / Chargée d'études crédits bancaires | Overlapping, not the sameprovisional |
| C1213 | Directeur / Directrice d'organisme de crédit | Related, not a matchprovisional |
| M1220 | Credit manager | Related, not a matchprovisional |
Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.
ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.
(AI Changing Work link release: fr-direct v1)
Spain classification links (CNO-11)
AI Changing Work's links between this occupation and CNO-11 occupations, judged with AI models. Relations are read from this occupation's side.
| CNO-11 code | CNO-11 title (Spanish) | Relation |
|---|---|---|
| 3402 | Comerciales de préstamos y créditos | This occupation is narrowerprovisional |
| 2613 | Analistas financieros | Related, not a matchprovisional |
Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.
(AI Changing Work link release: es-direct v1)
Germany classification links (KldB)
AI Changing Work's links between this occupation and KldB occupations, judged with AI models. Relations are read from this occupation's side.
| KldB code | KldB title (German) | Relation |
|---|---|---|
| 72144 | Finanzanalysten/-analystinnen - hoch komplexe Tätigkeiten | Overlapping, not the same |
| 72113 | Bankkaufleute - komplexe Spezialistentätigkeiten | Related, not a match |
Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.
(AI Changing Work link release: de-direct v1)
Japan classification links (JSCO)
AI Changing Work's links between this occupation and JSCO occupations, judged with AI models. Relations are read from this occupation's side.
| JSCO code | JSCO title (Japanese) | Relation |
|---|---|---|
| 184 | 金融・保険専門職業従事者 | Related, not a match |
| 281 | 営業・販売事務員 | Related, not a match |
Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.
(AI Changing Work link release: jp-direct v1)
Recent Changes Related to This Occupation
Summaries and relation labels are AI Changing Work's own; check any figures against the linked source.
Directly related 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]
Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources