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Credit controllers

allocating and reconciling cash received from customers, negotiating with customers to identify mutually acceptable solutions to credit and debt issues within organizational and compliance guidelines and persuading customers to pay amounts due on credit accounts. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: checking customer credit ratings is work AI now does quickly and cheaply, and calling customers to collect payment on overdue accounts is work it can't touch.

Your move: what you can actually do about this ↓

Which half fills your week decides your exposure. Moving toward the second half is a real, doable plan.

Your week, as this page understands it

Credit controllers perform financial, administrative and other tasks in relation to credit control and debt collection. The job title says “credit controllers”. The real job is the part underneath: calling customers to collect payment on overdue accounts. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But credit controllers is not one task. It is 48 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is calling customers to collect payment on overdue accounts, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
46%
changing shape
22%
staying human
31%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 57 out of 100 (51–63 allowing for uncertainty): partial exposure, across 48 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for credit controllers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

Shifting to AI

22 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

  • Allocating and reconciling cash received from customers

    This is reading one thing and writing another: cash in, a record out. That is the shape today's tools are built for.

    importance 95 · 4121/00
    Source: “Allocate and reconcile cash received from customers.” (UK task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Matching payments to invoices is standard accounting work that software already does well.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.

  • Checking customer credit ratings

    This is reading one thing and writing another: customer credit ratings in, a record out. That is the shape today's tools are built for.

    importance 90 · 4121/00
    Source: “Check customer credit ratings.” (UK task statement)
    How this row was scored

    Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Credit ratings come back automatically from bureau systems, so checking them is largely a machine step already.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Monitoring customer accounts to ensure timely payment and adherence to credit terms

    This is reading one thing and writing another: customer accounts in, a record out. That is the shape today's tools are built for.

    importance 90 · 4121/00
    Source: “Monitor customer accounts to ensure timely payment and adherence to credit terms.” (UK task statement)
    How this row was scored

    Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Watching accounts for late payment and breached terms is automatic monitoring software already performs.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Answering customer questions regarding problems with their accounts

    This is reading one thing and writing another: customer questions regarding problems in, a record out. That is the shape today's tools are built for.

    importance 90 · 4121/00
    Source: “Answer customer questions regarding problems with their accounts.” (UK task statement)
    How this row was scored

    Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Account queries are answered from records and set rules, which software already does at scale.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Changing shape

11 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

  • Collecting payments from customers

    The software now makes the first pass at payments, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.

    importance 95 · 4121/00
    Source: “Collect payments from customers.” (UK task statement)
    How this row was scored

    Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Credit controllers collect by phone and bank transfer rather than over a counter, though the conversation still needs a person.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Explaining credit terms to customers

    The software now makes the first pass at credit terms, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.

    importance 90 · 4121/00
    Source: “Explain credit terms to customers.” (UK task statement)
    How this row was scored

    Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Explaining credit terms is partly scripted from documented product rules, but customers usually want a person to answer questions.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Devising debt repayment plans that include payoff priorities and timelines

    The software now makes the first pass at debt repayment plans, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 85 · 4121/00
    Source: “Devise debt repayment plans that include payoff priorities and timelines.” (UK task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Building a repayment plan follows documented priorities and arithmetic that software produces, ahead of agreeing it with the customer.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Staying human

15 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

  • Calling customers to collect payment on overdue accounts

    The value here is that a specific person handles customers and stands behind it. That is earned, not computed.

    importance 95 · 4121/00
    Source: “Call customers to collect payment on overdue accounts.” (UK task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Software chases overdue accounts well, but a live call with someone behind on payments still needs a person who can read the situation.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Negotiating repayment plans with debtors

    The value here is that a specific person handles repayment plans and stands behind it. That is earned, not computed.

    importance 85 · 4121/00
    Source: “Negotiate repayment plans with debtors.” (UK task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Agreeing a repayment plan takes a live conversation where trust and flexibility matter as much as the arithmetic.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Negotiating with customers to identify mutually acceptable solutions to credit and debt issues within organizational and compliance guidelines

    The value here is that a specific person handles customers and stands behind it. That is earned, not computed.

    importance 85 · 4121/00
    Source: “Negotiate with customers to identify mutually acceptable solutions to credit and debt issues within organizational and compliance guidelines.” (UK task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Finding a workable answer to a debt problem takes a live conversation where trust and flexibility matter.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Show the other 38 tasks

  • Notifying credit departments when customers fail to respond to collection attempts

    shifting to AI

    This is reading one thing and writing another: credit departments in, a record out. That is the shape today's tools are built for.

    importance 80 · 4121/00
    Source: “Notify credit departments when customers fail to respond to collection attempts.” (UK task statement)
    How this row was scored

    Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Flagging a non-responding account to the credit team follows a simple, fixed rule.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Producing aged debtor reports to identify areas for improvement

    shifting to AI

    This is reading one thing and writing another: aged debtor reports in, a record out. That is the shape today's tools are built for.

    importance 70 · 4121/00
    Source: “Produce aged debtor reports to identify areas for improvement.” (UK task statement)
    How this row was scored

    Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Aged debt reports come straight out of the accounting system.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Compiling credit information gathered by investigation

    shifting to AI

    This is reading one thing and writing another: credit information in, a record out. That is the shape today's tools are built for.

    importance 85 · 4121/00
    Source: “Compile credit information gathered by investigation.” (UK task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Pulling credit information together from bureau and public sources is well-defined data work.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.

  • Creating and issuing invoices to clients

    shifting to AI

    This is reading one thing and writing another: invoices in, a record out. That is the shape today's tools are built for.

    importance 80 · 4121/00
    Source: “Create and issue invoices to clients.” (UK task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Raising invoices from order and price data is already largely automatic.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.

  • Reviewing customer files to identify and select overdue accounts for collection

    shifting to AI

    This is reading one thing and writing another: customer files in, a record out. That is the shape today's tools are built for.

    importance 85 · 4121/00
    Source: “Review customer files to identify and select overdue accounts for collection.” (UK task statement)
    How this row was scored

    Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Picking out overdue accounts from customer files is rule-based selection credit systems already do automatically.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Posting amounts paid to customer accounts

    shifting to AI

    This is reading one thing and writing another: amounts paid in, a record out. That is the shape today's tools are built for.

    importance 80 · 4121/00
    Source: “Post amounts paid to customer accounts.” (UK task statement)
    How this row was scored

    Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Posting payments to accounts is routine data entry, checked in the ledger later.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Setting up customer credit accounts

    shifting to AI

    This is reading one thing and writing another: customer credit accounts in, a record out. That is the shape today's tools are built for.

    importance 80 · 4121/00
    Source: “Set up customer credit accounts.” (UK task statement)
    How this row was scored

    Exposure score: 81 out of 100 (74–88 allowing for uncertainty): very high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Opening a customer credit account is structured system work, with identity and credit checks handled automatically.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Obtaining credit information from banks and other credit services

    shifting to AI

    This is reading one thing and writing another: credit information in, a record out. That is the shape today's tools are built for.

    importance 70 · 4121/00
    Source: “Obtain credit information from banks and other credit services.” (UK task statement)
    How this row was scored

    Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Pulling credit information from agencies is an automated system lookup, feeding a lending decision made elsewhere.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Maintaining accurate records of customer communications

    shifting to AI

    This is reading one thing and writing another: accurate records of customer communications in, a record out. That is the shape today's tools are built for.

    importance 85 · 4121/00
    Source: “Maintain accurate records of customer communications, actions, and account status.” (UK task statement)
    How this row was scored

    Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Logging customer contacts and account status is record-keeping that systems and AI already handle well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Posting customer statements and correspondence related to discrepancies and outstanding unpaid items

    shifting to AI

    This is reading one thing and writing another: customer statements in, a record out. That is the shape today's tools are built for.

    importance 75 · 4121/00
    Source: “Post customer statements and correspondence related to discrepancies and outstanding unpaid items.” (UK task statement)
    How this row was scored

    Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Producing and sending statements about unpaid items is templated correspondence software generates, with only light handling of post.

    The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Filing sales slips in customers' ledgers for billing purposes

    shifting to AI

    This is reading one thing and writing another: sales slips in, a record out. That is the shape today's tools are built for.

    importance 70 · 4121/00
    Source: “File sales slips in customers' ledgers for billing purposes.” (UK task statement)
    How this row was scored

    Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Filing sales slips against customer accounts is routine, though paper slips still need handling.

    The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Posting charge statements to customers

    shifting to AI

    This is reading one thing and writing another: charge statements in, a record out. That is the shape today's tools are built for.

    importance 70 · 4121/00
    Source: “Post charge statements to customers.” (UK task statement)
    How this row was scored

    Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Producing charge statements is automatic from the billing system, though posting them out involves handling.

    The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Processing incoming and outgoing correspondence and managing filing systems

    shifting to AI

    This is reading one thing and writing another: correspondence in, a record out. That is the shape today's tools are built for.

    importance 60 · 4121/00
    Source: “Process incoming and outgoing correspondence and manage filing systems.” (UK task statement)
    How this row was scored

    Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Sorting correspondence and keeping files in order is routine office work, largely digital.

    The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Arranging legal action against debtors

    shifting to AI

    This is reading one thing and writing another: legal action against debtors in, a record out. That is the shape today's tools are built for.

    importance 85 · 4121/00
    Source: “Arrange legal action against debtors.” (UK task statement)
    How this row was scored

    Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Preparing and instructing legal action is documented procedure, but running the case itself passes to a solicitor.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Managing credit accounts to ensure financial health

    shifting to AI

    This is reading one thing and writing another: credit accounts in, a record out. That is the shape today's tools are built for.

    importance 90 · 4121/00
    Source: “Manage credit accounts to ensure financial health.” (UK task statement)
    How this row was scored

    Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Watching limits, balances and risk across accounts is routine analysis of system data.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Responding to correspondence to address inquiries and requests

    shifting to AI

    This is reading one thing and writing another: correspondence in, a record out. That is the shape today's tools are built for.

    importance 60 · 4121/00
    Source: “Respond to correspondence to address inquiries and requests.” (UK task statement)
    How this row was scored

    Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Drafting replies to routine enquiries is well-documented writing software does well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Receiving information from salespeople or merchants by telephone

    shifting to AI

    This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.

    importance 50 · 4121/00
    Source: “Receive information from salespeople or merchants by telephone.” (UK task statement)
    How this row was scored

    Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Taking details over the phone and entering them is straightforward, repeatable work.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Verifying applicants' employment and references by contacting previous employers

    shifting to AI

    This is reading one thing and writing another: applicants' employment in, a record out. That is the shape today's tools are built for.

    importance 50 · 4121/00
    Source: “Verify applicants' employment and references by contacting previous employers.” (UK task statement)
    How this row was scored

    Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Checking employment history and chasing references is routine verification work AI handles well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Granting extensions of credit on overdue accounts

    changing shape

    The software now makes the first pass at extensions of credit, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 85 · 4121/00
    Source: “Grant extensions of credit on overdue accounts.” (UK task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Extending credit on an overdue account follows documented policy, but someone accountable has to make the call.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Processing credit applications and assessing creditworthiness using external sources of information

    changing shape

    The software now makes the first pass at credit applications, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.

    importance 80 · 4121/00
    Source: “Process credit applications and assess creditworthiness using external sources of information.” (UK task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.

    The rating behind it: Scoring an application from bureau data is highly automated, but lending decisions need an accountable person.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Interviewing loan applicants to obtain personal and financial data and to assist in completing applications

    changing shape

    The software now makes the first pass at loan applicants, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 50 · 4121/00
    Source: “Interview loan applicants to obtain personal and financial data and to assist in completing applications.” (UK task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Gathering an applicant's details is increasingly done through online forms, though some people still want help filling them in.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Identifying opportunities to improve credit management practices and implement changes

    changing shape

    The software now makes the first pass at opportunities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 75 · 4121/00
    Source: “Identify opportunities to improve credit management practices and implement changes.” (UK task statement)
    How this row was scored

    Exposure score: 49 out of 100 (42–56 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Ideas for better collections come easily; knowing which will work here needs local judgement.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Reviewing and analysing customer applications

    changing shape

    The software now makes the first pass at customer applications, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.

    importance 85 · 4121/00
    Source: “Review and analyse customer applications, seeking additional information when necessary, and making decisions on credit limits in line with regulatory and organizational requirements.” (UK task statement)
    How this row was scored

    Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.

    The rating behind it: Setting a credit limit follows documented affordability rules, but the decision carries regulatory responsibility someone accountable must hold.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Presenting loan and repayment schedules to customers

    changing shape

    The software now makes the first pass at loan, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.

    importance 60 · 4121/00
    Source: “Present loan and repayment schedules to customers.” (UK task statement)
    How this row was scored

    Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Walking a customer through a repayment schedule is partly scripted, but people want a person to answer their worries.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Identifying opportunities for performance improvement and taking ownership of specific changes that impact the role

    changing shape

    The software now makes the first pass at opportunities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 60 · 4121/00
    Source: “Identify opportunities for performance improvement and take ownership of specific changes that impact the role.” (UK task statement)
    How this row was scored

    Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: mistakes that are cheap to catch.

    The rating behind it: Spotting improvements can be drafted from data, but owning and driving the change through is a person's job.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

  • Relaying credit report information to subscribers by post or by telephone

    changing shape

    The software now makes the first pass at credit report information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 50 · 4121/00
    Source: “Relay credit report information to subscribers by post or by telephone.” (UK task statement)
    How this row was scored

    Exposure score: 42 out of 100 (35–49 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Relaying report details is simple, though phone calls and posted copies still involve a person.

    The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Communicating effectively with customers, suppliers and colleagues

    staying human

    The value here is that a specific person handles customers, suppliers and colleagues and stands behind it. That is earned, not computed.

    importance 80 · 4121/00
    Source: “Communicate effectively with customers, suppliers and colleagues.” (UK task statement)
    How this row was scored

    Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Routine messages draft easily, but working relationships with customers and suppliers are built by people.

    The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Persuading customers to pay amounts due on credit accounts

    staying human

    The value here is that a specific person handles customers and stands behind it. That is earned, not computed.

    importance 85 · 4121/00
    Source: “Persuade customers to pay amounts due on credit accounts, damage claims, or dishonoured cheques, or to return merchandise.” (UK task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Chasing payment well means reading the person and their circumstances, under strict fairness rules.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Speaking with customers to organise repayments

    staying human

    The value here is that a specific person handles customers and stands behind it. That is earned, not computed.

    importance 85 · 4121/00
    Source: “Speak with customers to organise repayments.” (UK task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Agreeing how someone will repay takes a live conversation where reassurance and flexibility matter more than the calculation.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Resolving complaints and verifying financial and crediting transactions by consulting with customers

    staying human

    The value here is that a specific person handles complaints and stands behind it. That is earned, not computed.

    importance 80 · 4121/00
    Source: “Resolve complaints and verify financial and credit transactions by consulting with customers.” (UK task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Resolving a complaint and checking the transactions behind it takes a live conversation with the customer.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Resolving complex customer needs and maintaining customer satisfaction

    staying human

    The value here is that a specific person handles complex customer needs and stands behind it. That is earned, not computed.

    importance 75 · 4121/00
    Source: “Resolve complex customer needs and maintain customer satisfaction.” (UK task statement)
    How this row was scored

    Exposure score: 31 out of 100 (24–38 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the value is that a specific person does it.

    The rating behind it: Untangling a complicated customer problem takes a live conversation and judgement about what the organisation can offer.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

  • Providing debt support and recommending write-offs

    staying human

    The rules require a named, qualified person to answer for debt support, and that person cannot be a piece of software.

    importance 75 · 4121/00
    Source: “Provide debt support and recommend write-offs when necessary.” (UK task statement)
    How this row was scored

    Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.

    The rating behind it: Supporting someone in difficulty and deciding to write off debt needs judgement and strict fairness rules.

    The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Continuouslying seek feedback, reflect on performance and making timely changes to improve personal effectiveness

    staying human

    The ratings behind this row put seek feedback, reflect well outside what today's tools can do on their own.

    importance 60 · 4121/00
    Source: “Continuously seek feedback, reflect on performance, and make timely changes to improve personal effectiveness.” (UK task statement)
    How this row was scored

    Exposure score: 28 out of 100 (24–32 allowing for uncertainty): low exposure, high confidence.

    Why it sits in this group: the five ratings behind the score, with no single dominant reason.

    The rating behind it: Reflecting on your own performance and changing how you work is something only the person doing the job can do.

    The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

  • Ordering merchandise repossession or service disconnection and turn over account records to solicitors

    staying human

    The rules require a named, qualified person to answer for merchandise repossession, and that person cannot be a piece of software.

    importance 75 · 4121/00
    Source: “Order merchandise repossession or service disconnection and turn over account records to solicitors.” (UK task statement)
    How this row was scored

    Exposure score: 27 out of 100 (20–34 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.

    The rating behind it: Repossession and legal referral are serious steps that need a responsible person to authorise them.

    The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Delivering high-quality products and services to customers using various channels

    staying human

    The value here is that a specific person handles high-quality products and stands behind it. That is earned, not computed.

    importance 75 · 4121/00
    Source: “Deliver high-quality products and services to customers using various channels such as face-to-face, telephone, digital, and written communication.” (UK task statement)
    How this row was scored

    Exposure score: 23 out of 100 (16–30 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the value is that a specific person does it.

    The rating behind it: Serving customers across phone, digital and face-to-face channels mixes scripted work with real conversations, some of them in person.

    The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

  • Collaborating with the wider organization

    staying human

    The value here is that a specific person handles the wider organization and stands behind it. That is earned, not computed.

    importance 70 · 4121/00
    Source: “Collaborate with the wider organization, team, communities, and external partners to achieve business objectives.” (UK task statement)
    How this row was scored

    Exposure score: 23 out of 100 (16–30 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the value is that a specific person does it.

    The rating behind it: Working with partners and communities towards shared goals rests on relationships rather than documents.

    The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

  • Tracing delinquent customers to new addresses by enquiring at post offices

    staying human

    This work happens in the physical world: delinquent customers, in a real place. Software cannot follow it there.

    importance 70 · 4121/00
    Source: “Trace delinquent customers to new addresses by enquiring at post offices, telephone companies, credit bureaus, or questioning neighbours.” (UK task statement)
    How this row was scored

    Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.

    Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.

    The rating behind it: Some tracing is database work, but asking at neighbours and offices means going out.

    The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

  • Receiving charge slips or crediting applications by post

    staying human

    This work happens in the physical world: charge slips, in a real place. Software cannot follow it there.

    importance 50 · 4121/00
    Source: “Receive charge slips or credit applications by post.” (UK task statement)
    How this row was scored

    Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: the same decision, made over and over; work that happens in the physical world.

    The rating behind it: Opening and handling incoming post is done by hand in the office.

    The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay
£28,500a year, before tax, the middle of the range, so half earn more and half earn less.ashe-t14, 2025 · ASHE 2025 provisional (reference April 2025)Provisional, because the ONS revises this figure in the autumn.
How we know this

Source: ashe-t14

Reference period: ASHE 2025 provisional (reference April 2025)

Rounding: Shown to the nearest £100. The exact published figure is in the downloadable dataset. We do not render pounds the survey cannot support.

People doing this job
29,000in the UK, 2026.nomis-aps · Apr 2025-Mar 2026 (latest APS 12-month period)This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: customer credit ratings in, a record out. The rows above are exactly that shape: checking customer credit ratings and allocating and reconciling cash received from customers. What it cannot do is be trusted in person, which is what customers run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

The exposed part of your job is the biggest part, and I am not going to dress that up: checking customer credit ratings is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 46% of this job's task weight sits in rows the software is already learning, 22% in rows that change shape rather than disappear, and 31% in rows it is nowhere near. That is the position, measured across 48 scored tasks. It is not a forecast about you.

What you have that the software does not is calling customers to collect payment on overdue accounts, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.

This week: one thing

Sit on the machine's side of the desk. Pick one real piece of customer credit ratings you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.

What you end up holding
a written list of the machine’s mistakes, in your handwriting
How long it takes
an evening, or an hour if you pick one job rather than one client

If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of customer credit ratings, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.

Over the next 90 days

Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is checking customer credit ratings” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.

Over the next 12 months

Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of calling customers to collect payment on overdue accounts you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, spend an hour with National Careers Service. It is free and government-funded, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest UK occupations to credit controllers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was debt, rent and other cash collectors: only about 17% of its durable work is work you already do. Your own job splits about 46/54: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “call customers to collect payment on overdue accounts”, and let the exposed end go.

How that was checked: this job was compared against all 412 UK occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

  • Debt, rent and other cash collectors

    Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “call customers to collect payment on overdue accounts”. Across the whole of both lists that adds up to about 17% of the work in that job the software is not taking.

    Why I am not recommending it: You would be starting most of it from nothing: about 17% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.

    Look at that job’s page anyway →

  • Financial accounts managers

    Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “negotiate with customers to identify mutually acceptable solutions to credit and debt issues within organizational and…”. Across the whole of both lists that adds up to about 16% of the work in that job the software is not taking.

    Why I am not recommending it: You would be starting most of it from nothing: about 16% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.

    Look at that job’s page anyway →

  • Financial administrative occupations n.e.c.

    Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “speak with customers to organise repayments”. Across the whole of both lists that adds up to about 9% of the work in that job the software is not taking.

    Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 58% of its own task list already scores in the top exposure band (62/100 in this release), so the same software is eating it.

    Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

  • The headline number you read somewhere

    The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 46% of its task weight, across 48 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.

  • “It’s too late for me to become something else”

    You are not starting from zero, and the page shows why: calling customers to collect payment on overdue accounts is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.

  • “I should learn to code”

    Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.

  • The “obvious” next job everyone suggests

    I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United Kingdom figures

The United States splits this work across more than one official group, of which Credit Counselors is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United States page →partial match

The other groups this work is counted across:

In US official statistics this job is counted as Credit Counselors and Loan Officers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Two honest options, and no deadline on either

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

A nearby route

There's no Space built for credit controllers yet.

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

The closest match is Power Automate Builders, a community for non-developers building the approvals, reminders and handoffs that keep working once real people use them. It overlaps with the part of your job that is growing: the reminder and escalation ladder around overdue accounts, rather than the conversations at the end of it. If that overlap isn't you, the free route below covers the same ground.

Try Power Automate Builders free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

After the trial it is a paid community, and you get identical data either way. If the overlap above is not your job, the moves above cost nothing and stand on their own.

Noted, and thank you. We’ll email you if a Space for credit controllers launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

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No Space for credit controllers yet. Should there be one?

Collab365 launches new communities where the need is real. If one for credit controllers existed, with researched problems, courses and people in the same boat, would you want in?

We use your email address for one thing: to tell you if a Space for credit controllers launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Credit controllers?
Not as a job, but it is already doing parts of the work. Across the 48 official task statements scored for Credit controllers (United Kingdom, SOC 4121), 46% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 57 out of 100 (range 51–63, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Credit controllers” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Check customer credit ratings” (93/100, very high); “Monitor customer accounts to ensure timely payment and adherence to credit terms” (93/100, very high); “Notify credit departments when customers fail to respond to collection attempts” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Credit controllers” stay human?
About 31% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Receive charge slips or credit applications by post” (10/100, minimal); “Trace delinquent customers to new addresses by enquiring at post offices, telephone companies, credit bureaus, or questioning neighbours” (19/100, minimal); “Collaborate with the wider organization, team, communities, and external partners to achieve business objectives” (23/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Credit controllers” do about AI?
Start from the ledger rather than the headline: 46% of this job's weighted core work is exposed, and roughly 31% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Credit controllers calculated?
Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 48 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

  • Provisional, because the ONS revises this figure in the autumn.
  • This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
  • The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
  • 12 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
  • Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Task statements
gaisi-indexProcessing: catalogue-bridge → ssc-relatedness-weighting → task-scoring → score-aggregation
Task weights
gaisi-index (relatedness)
Scores
Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment
ashe-t14 (ASHE 2025 provisional (reference April 2025))nomis-aps (Apr 2025-Mar 2026 (latest APS 12-month period))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

How we score a jobDownload this releaseLook up another job

Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.