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AI Automation Index
Will AI Replace Fraud Examiners, Investigators and Analysts?
Task-level automation exposure for Fraud Examiners, Investigators and Analysts: 23 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
57%
weighted task index
23 of 23 tasks scored
Median Salary
$81,100 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
High
132,130 people hold this job — size, not growth
Projected 2025–35
+4.1%
employment change
9,400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 51 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$39,270 – $125,110
Highest medians
District of Columbia $125,110
Maine $109,060
New York $107,490
Maryland $101,400
Massachusetts $99,970
Lowest medians
Puerto Rico $39,270
Kansas $51,160
Louisiana $59,860
Arkansas $60,600
Florida $61,660
State figures come from the 13-2099 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 57%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 3%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 35%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 22%
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 13/100
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
23 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) 51%
11 tasks
Medium exposure (≥ 35%) 35%
8 tasks
Low exposure (below 35%) 14%
4 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
#16053 · Core · 72% automatable
Document all investigative activities.
text generation — Legal documentation drafted; human owns accuracy
Document information related to legal proceedings.
#16054 · Core · 85% automatable
Create and maintain logs, records, or databases of information about fraudulent activity.
data analysis — Enter and maintain data in information systems
Maintain data in information systems or databases.
#16046 · Core · 60% automatable
Prepare written reports of investigation findings.
text generation — Draft legal docs; attorney owns accuracy
Prepare legal or investigatory documentation.
#16049 · Core · 40% automatable
Interview witnesses or suspects and take statements.
text generation — Draft legal docs; attorney owns accuracy
Interview witnesses, suspects, or claimants. Prepare legal or investigatory documentation.
#16051 · Core · 72% automatable
Gather financial documents related to investigations.
information retrieval — Gather financial records, human verifies authenticity
Gather financial records.
#16057 · Core · 80% automatable
Analyze financial data to detect irregularities in areas such as billing trends, financial relationships, and regulatory compliance procedures.
data analysis — Analyzes structured financial data with tools
Analyze business or financial data.
#16045 · Core · 55% automatable
Review reports of suspected fraud to determine need for further investigation.
information retrieval — Legal research strong, judgment and accountability human
Investigate legal issues.
#16047 · Core · 59% automatable
Recommend actions in fraud cases.
decision support — Drafts advice; accountability and context owned by human
Advise others on business or operational matters. Advise others on legal or regulatory compliance matters.
#16048 · Core · 43% automatable
Lead, or participate in, fraud investigation teams.
information retrieval — Legal research strong, judgment and accountability human
Investigate legal issues. Supervise employees.
#16055 · Core · 55% automatable
Coordinate investigative efforts with law enforcement officers and attorneys.
information retrieval — Legal research strong, judgment and accountability human
Investigate legal issues.
#16038 · Core · 30% automatable
Prepare evidence for presentation in court.
physical manipulation — Evidence collection is fieldwork with chain of custody
Collect evidence for legal proceedings.
#16050 · Core · 60% automatable
Design, implement, or maintain fraud detection tools or procedures.
decision support — AI drafts system specs; business context human
Develop business or financial information systems.
#16043 · Core · 15% automatable
Testify in court regarding investigation findings.
interpersonal — Testimony carries legal accountability and live presence
Testify at legal or legislative proceedings.
#16056 · Core · 55% automatable
Conduct in-depth investigations of suspicious financial activity, such as suspected money-laundering efforts.
information retrieval — Legal research strong, judgment and accountability human
Investigate legal issues.
#16036 · Core · 50% automatable
Train others in fraud detection and prevention techniques.
interpersonal — Job-skill training content AI-assisted; coaching human
Train personnel to enhance job skills.
#16035 · Core · 78% automatable
Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques.
information retrieval — AI surfaces updates; human internalizes knowledge
Update professional knowledge.
#16052 · Core · 70% automatable
Evaluate business operations to identify risk areas for fraud.
data analysis — Analyzes structured financial data with tools
Assess risks to business operations. Analyze business or financial data.
#16044 · Core · 62% automatable
Advise businesses or agencies on ways to improve fraud detection.
decision support — Drafts advice; accountability and context owned by human
Advise others on business or operational matters.
#16037 · Core · 78% automatable
Research or evaluate new technologies for use in fraud detection systems.
information retrieval — AI surfaces updates; human internalizes knowledge
Update professional knowledge.
#16041 · Core · 30% automatable
Conduct field surveillance to gather case-related information.
physical manipulation — Evidence collection is fieldwork with chain of custody
Collect evidence for legal proceedings.
#16039 · Supplemental · 60% automatable
Obtain and serve subpoenas.
text generation — Status notifications drafted and sent by AI
Obtain documentation to authorize activities. Inform individuals or organizations of status or findings.
#16042 · Supplemental · 3% automatable
Arrest individuals to be charged with fraud.
physical manipulation — Physically detaining suspects
Detain suspects or witnesses. Apprehend criminal suspects.
#16040 · Supplemental · 35% automatable
Negotiate with responsible parties to arrange for recovery of losses due to fraud.
interpersonal — Contract negotiation needs mandate and accountability
Negotiate contracts with clients or service providers.
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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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