High AI Risk Average

Financial specialists, all other

SOC Code: 13-2099

Financial specialists, all other carries a 51% AI exposure score (High automation risk), with a median annual wage of $80,190 and +3.1% projected employment growth from 2024 to 2034 (BLS), affecting approximately 137,100 workers. Full task breakdown, skills, and employer data are below.

AI Exposure Score
51% High

Proportion of tasks susceptible to AI automation (O*NET analysis)

Projected Growth
+3.1%
2024–2034 (BLS)
+4,300 jobs
Median Annual Wage
$80,190
BLS May 2024
How wage figures are sourced →

AI Exposure vs Industry Growth

Workforce demand by occupation Sanctioned bespoke signature viz (@signature-viz, KIZ-799) showing occupation-level workforce demand from BLS OEWS data. Pure SVG, no external dependencies.Projected Growth 2024-2034 (BLS)Technology+12.8%Healthcare+10.2%Professional+7.8%Education+5.8%Construction+4.5%Finance+4.6%Logistics+3.2%Government+1.2%Manufacturing-2.1%Retail-3.4%
National AI Exposure
40%
Average across all occupations
Avg Wage Growth
+3.2%
Median annual wage change
High-Risk Roles
127
Occupations with >70% AI exposure

Total occupations tracked

832

Covering all SOC major groups

Data currency

2024

BLS Employment Projections

AI exposure avg

40%

Fleet-wide median across all roles

Methodology confidence 92.0%
Industry standard

Composite score weighing O*NET task data completeness, BLS projection methodology, and cross-validation with employer risk grades.

Employment Projections

137,100
Employment 2024
141,400
Projected 2034
+3.1%
Change (%)
+4,300
Change (jobs)

Occupation Insight

Financial specialists, all other (SOC 13-2099) carries an AI exposure score of 51%, placing it in the High automation-risk tier. This score is computed from O*NET Database 30.0 task-level analysis, where each task an occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score in the 40–70% range indicates meaningful automation pressure on specific task categories, but the role as a whole still requires human judgment for coordination, exception handling, or client interaction.

The economic context matters alongside the risk score. BLS counted approximately 137,100 workers in this occupation in 2024, and projects a +3.1% change through 2034 — modest growth that keeps the occupation viable even as tasks evolve. Median annual compensation stands at $80,190, reflecting both skill scarcity and the value employers place on the tasks that remain difficult to automate. Entry typically requires Bachelor's degree, plus None of related experience.

For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Financial specialists, all other. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure. Pair the AI exposure score with the BLS employment projection and wage percentiles above for a complete career assessment.

Education & Entry Requirements

Typical Education
Bachelor's degree
Work Experience
None
On-the-Job Training
None

Top Tasks (O*NET)

  1. 1. Gather financial documents related to investigations.
  2. 2. Interview witnesses or suspects and take statements.
  3. 3. Prepare written reports of investigation findings.
  4. 4. Document all investigative activities.
  5. 5. Create and maintain logs, records, or databases of information about fraudulent activity.
  6. 6. Coordinate investigative efforts with law enforcement officers and attorneys.
  7. 7. Lead, or participate in, fraud investigation teams.
  8. 8. Testify in court regarding investigation findings.
  9. 9. Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation.
  10. 10. Prepare evidence for presentation in court.

Key Skills Required

  • Active Listening
  • Writing
  • Reading Comprehension
  • Speaking
  • Critical Thinking
  • Complex Problem Solving
  • Judgment and Decision Making
  • Active Learning
  • Coordination
  • Social Perceptiveness

Knowledge Areas

  • English Language
  • Economics and Accounting
  • Law and Government
  • Computers and Electronics
  • Administration and Management
  • Mathematics
  • Customer and Personal Service
  • Education and Training
  • Public Safety and Security
  • Administrative

Frequently Asked Questions

Will AI replace Financial specialists, all other?

Financial specialists, all other has an AI exposure score of 51%, indicating a high level of automation risk. Some tasks in this role can be augmented or partially automated by AI, but core responsibilities require human judgment.

What is the job outlook for Financial specialists, all other?

According to BLS Employment Projections 2024-2034, Financial specialists, all other is projected to grow by 3.1% over the decade. Current employment stands at approximately 137,100 workers.

What skills are needed for Financial specialists, all other?

Key skills for Financial specialists, all other include Active Listening, Writing, Reading Comprehension, and others. Typical entry-level education is Bachelor's degree.

How much do Financial specialists, all other earn?

The median annual wage for Financial specialists, all other is $80,190, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.

What education is required for Financial specialists, all other?

The typical entry-level education for Financial specialists, all other is Bachelor's degree. Employers generally expect None of related work experience. On-the-job training typically involves None. Requirements can vary by employer and specialization.

Which companies employ Financial specialists, all other?

Financial specialists, all other roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.

AI Exposure Rating

2.5
out of 5.0

High automation risk based on 10 analyzed tasks. A moderate share of tasks may be augmented by AI tools.

Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).

Related

Data sourced from official public datasets. See our methodology for details. Retrieved and formatted by PlainWorkforce Editorial