Tax examiners and collectors, and revenue agents
Most of the workflow is automatable. Human judgment remains for exceptions, clients, or ambiguity.
SOC 13-2081 · Business And Financial
Signal composition
how the 0-100 score is assembled
By seniority
multiplicative adjustment from category curve
Entry-level roles carry the brunt because they concentrate the most automatable subset of tasks. Senior work is insulated by judgment, relationships, and accountability.
Task-level analysis
scored 0-100 for current-generation AI feasibility, weighted by BLS-stated importance
Keep records on each active case including contact information and actions taken
Record-keeping of contact information and actions is straightforward data entry and database management. AI systems already automate CRM functions, logging communications, and maintaining case histories with minimal human intervention beyond initial system setup.
BLS evidence: Tax examiners and collectors, and revenue agents keep records on each active case including telephone numbers and actions taken.
Assess fees, interest, and penalties on additional taxes owed
Calculating fees, interest, and penalties is a rule-based computational task. AI can apply tax code formulas, compound interest calculations, and penalty schedules with perfect consistency. This is essentially automated calculation with minimal judgment required.
BLS evidence: If a taxpayer owes additional taxes, tax examiners adjust the total amount by assessing fees, interest, and penalties and then notify the taxpayer of the total amount owed.
Notify taxpayers of overpayment or underpayment and issue refunds or request payment
This is largely computational: calculating differences between filed and correct amounts, generating notifications, and triggering refund or payment request workflows. AI can perform these calculations and generate communications with minimal human oversight, requiring review only for unusual cases.
BLS evidence: Tax examiners and collectors, and revenue agents notify taxpayers of overpayment or underpayment and issue a refund or request additional payment.
Evaluate financial information using accounting procedures and knowledge of tax laws
AI excels at applying accounting procedures to structured financial data and can reference tax law databases comprehensively. Modern systems can analyze financial statements, identify discrepancies, and apply relevant tax code sections with accuracy matching or exceeding median human performance.
BLS evidence: They evaluate financial information, using their understanding of accounting procedures and knowledge of changes to tax laws and regulations.
Review filed tax returns to determine whether credits and deductions are lawful
AI can parse tax returns, cross-reference against tax code databases, and flag questionable deductions with high accuracy. Current LLMs with RAG can interpret complex tax regulations and apply them to specific cases, though edge cases and novel deduction structures may require human review.
BLS evidence: Tax examiners and collectors, and revenue agents review tax returns, and much of a tax examiner's job involves making sure that tax credits and deductions claimed by taxpayers are lawful.
Contact taxpayers to address problems and request supporting documentation
AI can generate templated contact messages, parse responses, and request standard documentation via email or portal. However, handling non-standard taxpayer questions, interpreting ambiguous responses, and managing escalated disputes still require human judgment in many cases.
BLS evidence: Tax examiners and collectors, and revenue agents contact taxpayers to address problems and to request supporting documentation.
Investigate and verify taxpayer claims of inability to pay taxes
Investigating inability to pay requires analyzing financial documents (which AI can do) but also assessing credibility, identifying hidden assets through investigative techniques, and making judgment calls about hardship claims that involve understanding human behavior and context beyond document analysis.
BLS evidence: When delinquent taxpayers claim that they cannot pay their taxes, collectors investigate and verify the claims by researching financial statements or mortgages.
Collect overdue tax payments from delinquent taxpayers
Collection involves negotiation, assessment of taxpayer circumstances through conversation, judgment about payment plans, and often requires legal escalation or physical asset seizure coordination. AI can automate initial contact and payment processing but cannot handle the adaptive negotiation and enforcement aspects.
BLS evidence: Tax collectors deal with overdue accounts and work with the taxpayer to settle the debt, including setting up payment plans or placing liens on assets.
Conduct field audits and investigations of income tax returns to verify information
Field audits require physical presence at business locations, examination of physical records, real-time interviewing of taxpayers with adaptive questioning, and judgment calls about credibility and environmental context that AI cannot perform remotely or through robotics at current capability levels.
BLS evidence: Revenue agents conduct field audits and investigations of income tax returns to verify information or to update tax liabilities, handling complex tax returns of large businesses and corporations.
Task heatmap
automation score by task, sorted by weighted contribution
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External signals and sources
category-level priors and BLS fields that feed the four non-task signals
- Karpathy/BLS Digital AI Exposure (0-10 scale rescaled to 0-100)
- BLS projected outlook: Decline (-2%)
- Indeed demand signal (monthly refresh pending)
- BLS typical entry-level education: Bachelor's degree
- Credential trend signal (annual refresh)
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