Fraud Examiners, Investigators and Analysts
Business and Financial Operations Occupations- O*NET-SOC code
- 13-2099.04
Obtain evidence, take statements, produce reports, and testify to findings regarding resolution of fraud allegations. May coordinate fraud detection and prevention activities.
- 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
0.220
0.000Range of values carried here0.745Group-level value
Scale, 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
0.44
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
Show 16 hidden tasks23 rated tasks · 19 tasks with β ≥ 0.5 (82.6%)
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
| Task (O*NET 20.1) | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Review reports of suspected fraud to determine need for further investigation.13-2099.04 | 0.003320.9 | —0 |
Train others in fraud detection and prevention techniques.13-2099.04 | 0.003119.6 | —0 |
Prepare written reports of investigation findings.13-2099.04 | 0.002918.3 | —0 |
Conduct in-depth investigations of suspicious financial activity, such as suspected money-laundering efforts.13-2099.04 | 0.002717.0 | 0.004413.2 |
Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques.13-2099.04 | 0.002113.1 | —0 |
Analyze financial data to detect irregularities in areas such as billing trends, financial relationships, and regulatory compliance procedures.13-2099.04 | 0.001811.1 | 0.004011.9 |
Design, implement, or maintain fraud detection tools or procedures. | —0 | 0.025374.9 |
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 |
|---|---|
Prepare written reports of investigation findings.O*NET Task ID 16046 | 1.0 |
Document all investigative activities.O*NET Task ID 16053 | 1.0 |
Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques.O*NET Task ID 16035 | 0.5 |
Train others in fraud detection and prevention techniques.O*NET Task ID 16036 | 0.5 |
Research or evaluate new technologies for use in fraud detection systems.O*NET Task ID 16037 | 0.5 |
Prepare evidence for presentation in court.O*NET Task ID 16038 | 0.5 |
Negotiate with responsible parties to arrange for recovery of losses due to fraud.O*NET Task ID 16040 | 0.5 |
Advise businesses or agencies on ways to improve fraud detection.O*NET Task ID 16044 | 0.5 |
Review reports of suspected fraud to determine need for further investigation.O*NET Task ID 16045 | 0.5 |
Recommend actions in fraud cases.O*NET Task ID 16047 | 0.5 |
Lead, or participate in, fraud investigation teams.O*NET Task ID 16048 | 0.5 |
Interview witnesses or suspects and take statements.O*NET Task ID 16049 | 0.5 |
Design, implement, or maintain fraud detection tools or procedures.O*NET Task ID 16050 | 0.5 |
Gather financial documents related to investigations.O*NET Task ID 16051 | 0.5 |
Evaluate business operations to identify risk areas for fraud.O*NET Task ID 16052 | 0.5 |
Create and maintain logs, records, or databases of information about fraudulent activity.O*NET Task ID 16054 | 0.5 |
Coordinate investigative efforts with law enforcement officers and attorneys.O*NET Task ID 16055 | 0.5 |
Conduct in-depth investigations of suspicious financial activity, such as suspected money-laundering efforts.O*NET Task ID 16056 | 0.5 |
Analyze financial data to detect irregularities in areas such as billing trends, financial relationships, and regulatory compliance procedures.O*NET Task ID 16057 | 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
All O*NET 31.0 task statements (23)
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 |
|---|---|
| Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques. | Core |
| Train others in fraud detection and prevention techniques. | Core |
| Research or evaluate new technologies for use in fraud detection systems. | Core |
| Prepare evidence for presentation in court. | Core |
| Conduct field surveillance to gather case-related information. | Core |
| Testify in court regarding investigation findings. | Core |
| Advise businesses or agencies on ways to improve fraud detection. | Core |
| Review reports of suspected fraud to determine need for further investigation. | Core |
| Prepare written reports of investigation findings. | Core |
| Recommend actions in fraud cases. | Core |
| Lead, or participate in, fraud investigation teams. | Core |
| Interview witnesses or suspects and take statements. | Core |
| Design, implement, or maintain fraud detection tools or procedures. | Core |
| Gather financial documents related to investigations. | Core |
| Evaluate business operations to identify risk areas for fraud. | Core |
| Document all investigative activities. | Core |
| Create and maintain logs, records, or databases of information about fraudulent activity. | Core |
| Coordinate investigative efforts with law enforcement officers and attorneys. | Core |
| Conduct in-depth investigations of suspicious financial activity, such as suspected money-laundering efforts. | Core |
| Analyze financial data to detect irregularities in areas such as billing trends, financial relationships, and regulatory compliance procedures. | Core |
| Obtain and serve subpoenas. | Supplemental |
| Negotiate with responsible parties to arrange for recovery of losses due to fraud. | Supplemental |
| Arrest individuals to be charged with fraud. | 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
Robot environment tiers (E0–E3)
E0 noneDetails
Rated not doable by robots that have been deployed, sold or demonstrated, even in a purpose-built setting
E1 purpose-built robot settingsDetails
Rated doable in a setting built around the machine, such as a fenced machine cell or a bottling line
E2 structured facilitiesDetails
Rated doable in a human work facility that an institution keeps to a working standard, such as a hospital pharmacy or a container port
E3 unstructured settingsDetails
Rated doable in a setting that no institution keeps to a working standard, such as a private home or a construction site
Only tasks classified as physical are listed; other tasks have no tier in the source.
| Task (O*NET 29.3) | Robot environment tier |
|---|---|
| Conduct field surveillance to gather case-related information. | E0 none |
| Arrest individuals to be charged with fraud. | E0 none |
Occupation information
Employment outlook (BLS)
Values for the whole SOC 13-2099 group (Financial specialists, all other), not for this O*NET occupation alone.
- Employment, 2025
- 138K
- Projected employment, 2035
- 143K
- Change (2025–35)
- +4.1%
- Median annual wage, 2025
- $81,100
- 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 |
|---|---|---|---|
| 35120 | 감사 사무원 | Related, not a match | 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 3339Business Services Agents Not Elsewhere Classified
The definition, tasks and examples below are the ILO's description of that unit group, not of this occupation.
This unit group covers business services agents not classified elsewhere in Minor Group 333: Business Services Agents. For instance, the group includes those who establish business contacts, sell business services such as advertising space in the media, arrange contracts for performances of athletes, entertainers and artists, for the publication of books, the production of plays, or the recording, performance and sale of music, sell property and goods by auction and who design and organize package and group tours.
Tasks
- In such cases tasks would include -
- (a) obtaining information about services to be sold and needs of prospective buyers;
- (b) negotiating contracts on behalf of seller or buyer and explaining terms of sale and payment to client;
- (c) signing agreements on behalf of seller or buyer and ensuring that contract is honoured;
- (d) making sure that the business service purchased is made available to the buyer in the agreed format at the agreed time;
- (e) selling by auction various kinds of property, cars, commodities, livestock, art, jewellery and other objects.
- (f) organizing group tours for business or vacation travel and making bulk travel and accommodation bookings.
Example occupations
Advertising salesperson · Auctioneer · Literary agent · Musical performance agent · Sports agent · Theatrical agent · Tour operator
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.
- financial fraud examinerESCO 2411.1.9Same scope
Financial fraud examiners undertake anti-fraud investigations including financial statement irregularities, securities fraud and market abuse detection. They manage fraud risk assessments and prepare forensic reports including the analysis and verification of evidence. Financial fraud examiners liaise with regulatory bodies.
- insurance fraud investigatorESCO 3315.4Overlapping, not the same
Insurance fraud investigators combat fraudulent activities by investigating the circumstances of certain suspicious claims, activities related to new customers, buying insurance products and premium calculations. Insurance fraud investigators refer potential fraud claims to insurance investigators who then undertake research and investigations to support or deny a claimant's case.
- social security inspectorESCO 3353.1Overlapping, not the same
Social security inspectors investigate fraudulent activities in social security that affect workers' rights. They audit and examine applications for benefits and investigate company actions based on employee complaints. Inspections include labour-related activities such as non-payment of wages or expenses. Social security inspectors ensure that employees are treated fairly and in accordance to laws. They record and make reports on their findings to ensure validity of the claims they are investigating.
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 |
|---|---|---|
| K1506 | Inspecteur / Inspectrice de la concurrence, de la consommation et de la répression des fraudes | Related, not a match |
| C1121 | Inspecteur / Inspectrice des assurances | Related, not a matchprovisional |
| C1122 | Contrôleur / Contrôleuse prestations de la sécurité sociale | Related, not a matchprovisional |
| C1209 | Spécialiste de la vérification client | 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 |
|---|---|---|
| 2611 | Especialistas en contabilidad | Related, not a match |
| 3712 | Detectives privados | Related, not a match |
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 |
|---|---|---|
| 43384 | Berufe in der IT-Netzwerktechnik, IT-Koordination, IT-Administration und IT-Organisation (sonstige spezifische Tätigkeitsangabe) - hoch komplexe Tätigkeiten | Related, not a match |
| 53152 | Detektive/Detektivinnen - fachlich ausgerichtete Tätigkeiten | Related, not a match |
| 72244 | Berufe in Wirtschaftsprüfung - hoch komplexe Tätigkeiten | 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 |
|---|---|---|
| 249 | 他に分類されない専門的職業従事者 | 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)
Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources