Credit authorizers, checkers, and clerks
SOC Code: 43-4041
Credit authorizers, checkers, and clerks carries a 60% AI exposure score (High automation risk), with a median annual wage of $49,130 and -6.2% projected employment growth from 2024 to 2034 (BLS), affecting approximately 12,000 workers. Full task breakdown, skills, and employer data are below.
Proportion of tasks susceptible to AI automation (O*NET analysis)
At 60% AI exposure, Credit authorizers, checkers, and clerks sits 23 points above the 37.2% average across 832 U.S. occupations - more exposed than 94% of them. Most of its core tasks still require human judgment.
Career outlook score
18/100
Credit authorizers, checkers, and clerks, weighted across 3 of 3 tracked dimensions
A weighted composite of median wage, projected growth, and AI-automation security (inverted exposure), each benchmarked against every other tracked occupation. It describes this occupation's standing across those three dimensions, not a guarantee of future outcomes.
- Median wage F $49,130
Annual median wage, percentile rank vs all tracked occupations
- Projected growth F -6.2%
Projected employment change, 2024-2034, BLS Employment Projections
- AI exposure (inverted) F 60% exposure
Lower O*NET task-automability scores lower risk; this dimension scores security, not exposure
Home health and personal care aides is #1 of 832 occupations by employment (4.3M, AI exposure rank #392 at 35%) vs. Bookkeeping, accounting, and auditing clerks #1 by AI task exposure (95%, employment rank #17 at 1.6M).
Automation-risk inventory
Exclusive cut of 832 occupations by PlainWorkforce automation-risk band. Orthogonal to the continuous AI-exposure distribution chart below and to this occupation's single risk badge, this is the corpus band mix, with Credit authorizers, checkers, and clerks in the High band.
Credential × growth standing
Open entry on a declining BLS line
Typical entry is HS / some college (333/832) while growth is Declining (111/832). This is the crowded high-risk corner of the map - automation and headcount pressure compound without a degree moat.
Where Credit authorizers, checkers, and clerks sits among all occupations
AI-exposure score distribution across 832 U.S. occupations (O*NET task analysis). This occupation is marked in rose.
Where Credit authorizers, checkers, and clerks sits by AI exposure
AI-exposure score distribution across U.S. occupations (O*NET task analysis)
60% Higher than most higher than 94% of 832 occupations
Occupations, banded by AI-exposure score
Each bar is a band; taller bars hold more occupations. The dashed line + filled bar mark this entry. Hover or tap any bar for its full count and share, and where it sits relative to this entry.
Source O*NET task data (U.S. Department of Labor) · BLS 2024–2034
Source: O*NET task data (U.S. Department of Labor); AI-exposure scores are PlainWorkforce's analysis. As of BLS 2024–2034.
Credit authorizers, checkers, and clerks vs. its occupational neighbors
AI exposure (horizontal) vs projected 2024–2034 growth (vertical) for occupations in the same SOC group. Credit authorizers, checkers, and clerks is marked in rose; the top-left corner is the most future-proof.
Source: BLS Employment Projections 2024–2034 (growth) and O*NET (AI-exposure analysis by PlainWorkforce).
Employment Projections
Where This Score Comes From
Credit authorizers, checkers, and clerks's High automation-risk tier is computed from O*NET Database 30.0 task-level analysis, where each documented task the 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 employment, wage, and education figures above come from a second, separate federal source: BLS Employment Projections 2024–2034, matched to the O*NET task data by Standard Occupational Classification (SOC 43-4041) code, with the wage figure cross-validated against the BLS Occupational Employment and Wage Statistics (OEWS) May 2024 survey. BLS figures are presented exactly as published, with no adjustment on our part; the SOC-code match is what lets Credit authorizers, checkers, and clerks be compared directly against every other tracked occupation on the same basis, not just roles that happen to share a job title.
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 Credit authorizers, checkers, and clerks. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure.
Education & Entry Requirements
Top Tasks (O*NET)
- 1. Evaluate customers' computerized credit records and payment histories to decide whether to approve new credit, based on predetermined standards.
- 2. Keep records of customers' charges and payments.
- 3. Compile and analyze credit information gathered by investigation.
- 4. File sales slips in customers' ledgers for billing purposes.
- 5. Obtain information about potential creditors from banks, credit bureaus, and other credit services, and provide reciprocal information if requested.
- 6. Interview credit applicants by telephone or in person to obtain personal and financial data needed to complete credit report.
- 7. Receive charge slips or credit applications by mail, or receive information from salespeople or merchants by telephone.
- 8. Mail charge statements to customers.
- 9. Examine city directories and public records to verify residence property ownership, bankruptcies, liens, arrest record, or unpaid taxes of applicants.
- 10. Relay credit report information to subscribers by mail or by telephone.
Key Skills Required
- Reading Comprehension
- Active Listening
- Speaking
- Critical Thinking
- Writing
- Social Perceptiveness
- Time Management
- Monitoring
- Coordination
- Service Orientation
Knowledge Areas
- Customer and Personal Service
- English Language
- Mathematics
- Law and Government
- Administrative
- Economics and Accounting
- Computers and Electronics
- Administration and Management
- Sales and Marketing
- Psychology
Frequently Asked Questions
Will AI replace Credit authorizers, checkers, and clerks?
Credit authorizers, checkers, and clerks has an AI exposure score of 60%, 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 Credit authorizers, checkers, and clerks?
According to BLS Employment Projections 2024-2034, Credit authorizers, checkers, and clerks is projected to decline by 6.2% over the decade. Current employment stands at approximately 12,000 workers.
What skills are needed for Credit authorizers, checkers, and clerks?
Key skills for Credit authorizers, checkers, and clerks include Reading Comprehension, Active Listening, Speaking, and others. Typical entry-level education is High school diploma or equivalent.
How much do Credit authorizers, checkers, and clerks earn?
The median annual wage for Credit authorizers, checkers, and clerks is $49,130, 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 Credit authorizers, checkers, and clerks?
The typical entry-level education for Credit authorizers, checkers, and clerks is High school diploma or equivalent. Employers generally expect None of related work experience. On-the-job training typically involves Moderate-term on-the-job training. Requirements can vary by employer and specialization.
Which companies employ Credit authorizers, checkers, and clerks?
Credit authorizers, checkers, and clerks 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
High automation risk based on 10 analyzed tasks. A majority of tasks in this occupation are susceptible to AI automation.
Nationwide occupations with similar workforce profiles
Two data-derived peer sets for Credit authorizers, checkers, and clerks: AI-exposure neighbors and BLS 2034 growth neighbors (distinct comparison neighborhoods, cross-occupation).
Similar AI exposure score
Nearest O*NET task-automability scores nationwide (60% here).
- Cargo and freight agents · AI exposure 60%
- Data entry keyers · AI exposure 60%
- Eligibility interviewers, government programs · AI exposure 60%
- Gambling cage workers · AI exposure 60%
Similar projected growth (2024–2034)
Nearest BLS employment change rates nationwide (-6.2% here).
- Coil winders, tapers, and finishers · growth -6.3%
- Paper goods machine setters, operators, and tenders · growth -6.3%
- Chemical plant and system operators · growth -6.1%
- Advertising sales agents · growth -6.4%
Peers are nearest-neighbor matches on published BLS + O*NET metrics among occupations with ≥10,000 workers in 2024; corpus ranks above sort the full tracked set.
Related Occupations
Showing 6 of 24 occupations with a comparable automation-risk profile.
What to do with this
Use Credit authorizers, checkers, and clerks' numbers above to compare, not just read in isolation.
- Compare Credit authorizers, checkers, and clerks side by side against another occupation on wage, growth, and AI exposure. Workforce comparison tool
- Understand how the AI exposure score above is actually calculated before treating it as a verdict. Reading AI exposure scores
- See the full ranked list of occupations with the lowest automation exposure nationwide. Safest occupations from AI
AI exposure scores estimate task automatability from O*NET data; they are not a certainty of job loss, and BLS growth projections are estimates, not guarantees.
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).