AI exposure: Credit Authorizers, Checkers, and Clerks
Authorize credit charges against customers' accounts. Investigate history and credit standing of individuals or business establishments applying for credit. May interview applicants to obtain personal and financial data, determine credit worthiness, process applications, and notify customers of acceptance or rejection of credit.
Reading this score
computedCredit Authorizers, Checkers, and Clerks sits in the top 5% of every occupation measured. 59.8% of what this job consists of, weighted by how important each task is to the role, is work current AI systems can produce with little standing in the way. Very few occupations score this high. The ones that do tend to share a trait: the output is a document, a calculation or a message, and nobody has to be in a particular room for it to count.
What holds the line here is context. Across this occupation's 16 tasks it averages 1.72 out of 3, the highest of the five friction dimensions. In plain terms, the work depends on knowledge the model cannot hold. Much of this job runs on things that were never written down: what this particular organisation does, what happened last week, what the person across the table actually meant. That context is the barrier, and it erodes as systems are given more access.
The most exposed thing this job does is Examine city directories and public records to verify residence property ownership,..., at 93.3%. The least is Interview credit applicants by telephone or in person to obtain personal and financial data..., at 26.7%. A gap of 66.7% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within office and administrative support occupations, this one is more exposed than most. The median across the 51 roles in the group is 54.6%, and only 13 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.
What would move this score. Of 16 tasks, 16 are currently banded exposed, 0 assisted and 0 untouched. For that distribution to shift materially would take systems being given deeper access to the organisation's own records and history, which is already happening. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Task by task
16 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Examine city directories and public records to verify residence property ownership, bankruptcies, liens, arrest record, or unpaid taxes of applicants. | 93.3% | 6.7% | 0.0% | 3.39 | exposed |
| Compile and analyze credit information gathered by investigation. | 73.3% | 26.7% | 0.0% | 4.40 | exposed |
| Evaluate customers' computerized credit records and payment histories to decide whether to approve new credit, based on predetermined standards. | 73.3% | 26.7% | 0.0% | 4.61 | exposed |
| Consult with customers to resolve complaints or verify financial or credit transactions. | 73.3% | 26.7% | 0.0% | – | exposed |
| Relay credit report information to subscribers by mail or by telephone. | 66.7% | 33.3% | 0.0% | 3.33 | exposed |
| Keep records of customers' charges and payments. | 60.0% | 40.0% | 0.0% | 4.54 | exposed |
| Mail charge statements to customers. | 60.0% | 15.0% | 25.0% | 3.82 | exposed |
| File sales slips in customers' ledgers for billing purposes. | 55.0% | 20.0% | 25.0% | 4.40 | exposed |
| Obtain information about potential creditors from banks, credit bureaus, and other credit services, and provide reciprocal information if requested. | 50.0% | 25.0% | 25.0% | 4.05 | exposed |
| Receive charge slips or credit applications by mail, or receive information from salespeople or merchants by telephone. | 50.0% | 25.0% | 25.0% | 3.94 | exposed |
| Contact former employers and other acquaintances to verify applicants' references, employment, health history, or social behavior. | 50.0% | 25.0% | 25.0% | – | exposed |
| Call customers to collect payment on delinquent accounts. | 45.0% | 30.0% | 25.0% | – | exposed |
| Prepare reports of findings and recommendations. | 45.0% | 30.0% | 25.0% | – | exposed |
| Review individual or commercial customer files to identify and select delinquent accounts for collection. | 45.0% | 30.0% | 25.0% | – | exposed |
| Prepare credit cards or charge account plates. | 40.0% | 35.0% | 25.0% | 3.00 | exposed |
| Interview credit applicants by telephone or in person to obtain personal and financial data needed to complete credit report. | 26.7% | 23.3% | 50.0% | 4.02 | exposed |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean. 5 task(s) lacked a usable O*NET weight and are shown but excluded from the weighting.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 3.31 | 0-4 |
| Embodiment | 0.38 | 0-3 |
| Presence | 0.53 | 0-3 |
| Accountability | 1.19 | 0-3 |
| Context | 1.72 | 0-3 |
| Verification cost | 1.06 | 0-3 |
What this means in practice
Where most of a role's weighted task load is exposed, the work that survives is usually the part of the job nobody wrote into the job description: deciding what should be produced rather than producing it, and being answerable for the result. The tasks lowest on this page are a better guide to where to spend your time than any general advice about the future of work.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.