US dataswitch to UK
Loan Officers
meeting with applicants to obtain information for loan applications and to answer questions about the process, supervising loan personnel and staying abreast of new types of loans and other financial services and products to better meet customers' needs. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: obtaining and compiling copies of loan applicants' credit histories. The tasks, though, are not you.
It would be a lie to soften that; meeting with applicants to obtain information for loan applications and to answer questions about the process is what this work rebuilds around. The routes below start from it.
Your week, as this page understands it
Evaluate, authorize, or recommend approval of commercial, real estate, or credit loans. Advise borrowers on financial status and payment methods. Includes mortgage loan officers and agents, collection analysts, loan servicing officers, loan underwriters, and payday loan officers. The job title says “loan officers”. The real job is the part underneath: meeting with applicants to obtain information for loan applications and to answer questions about the process. 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 loan officers is not one task. It is 30 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is meeting with applicants to obtain information for loan applications and to answer questions about the process, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 58%
- changing shape
- 17%
- staying human
- 25%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 60 out of 100 (55–66 allowing for uncertainty): high exposure, across 30 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 loan officers 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.
- 3 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.
Shifting to AI
17 tasksTasks 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.
Analyzing applicants' financial status, credit and property evaluations to determine feasibility of granting loans
This is reading one thing and writing another: applicants' financial status, credit and property evaluations in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Analyze applicants' financial status, credit, and property evaluations to determine feasibility of granting loans.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Credit and property data are already scored automatically, so software can draft the feasibility assessment, with staff confirming borderline cases.
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.
Explaining to customers the different types of loans and crediting options that are available
This is reading one thing and writing another: customers the different types of loans in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Explain to customers the different types of loans and credit options that are available, as well as the terms of those services.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (57–65 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; the value is that a specific person does it.
The rating behind it: Loan types and terms are thoroughly documented, so software explains them well, though customers often want a person to talk it through.
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 2/4 · how much data exists 4/4.
Obtaining and compiling copies of loan applicants' credit histories
This is reading one thing and writing another: copies of loan applicants' credit histories in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Obtain and compile copies of loan applicants' credit histories, corporate financial statements, and other financial information.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 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: Credit reports and financial statements can be pulled and compiled automatically, with only light chasing of the applicant for missing items.
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 1/4 · how much data exists 3/4.
Reviewing and updating credit and loan files
This is reading one thing and writing another: credit in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review and update credit and loan files.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Updating credit and loan files is record keeping software handles, though judging what a new document changes still takes some experience.
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.
Changing shape
5 tasksTasks 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.
Handling customer complaints and taking appropriate action to resolve them
The software now makes the first pass at customer complaints, 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 4 · CoreSource: “Handle customer complaints and take appropriate action to resolve them.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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: Software drafts good responses to common complaints, but an upset customer usually wants a person who can reassure them and act.
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 2/4 · how much data exists 3/4.
Approving loans within specified limits
The software now makes the first pass at loans within specified limits, 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 4 · CoreSource: “Approve loans within specified limits, and refer loan applications outside those limits to management for approval.” (O*NET 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: The decision follows written limits software can apply, but a bank still needs an authorised person answerable for approving the loan.
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.
Marketing bank products to individuals and firms
The software now makes the first pass at bank products, 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 4 · CoreSource: “Market bank products to individuals and firms, promoting bank services that may meet customers' needs.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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: Marketing material is easy to generate, but winning business from individuals and firms still leans on someone customers already know.
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 2/4 · how much data exists 3/4.
Staying human
8 tasksTasks 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.
Meeting with applicants to obtain information for loan applications and to answer questions about the process
The rules require a named, qualified person to answer for applicants, and that person cannot be a piece of software.
importance 5 · CoreSource: “Meet with applicants to obtain information for loan applications and to answer questions about the process.” (O*NET 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: reading one thing and writing another; 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: Software can gather application details and answer standard questions, but applicants often expect a person, and licensing rules govern who takes a mortgage application.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Working with clients to identify their financial goals and to find ways of reaching those goals
The value here is that a specific person handles clients and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Work with clients to identify their financial goals and to find ways of reaching those goals.” (O*NET 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 same decision, made over and over; the value is that a specific person does it.
The rating behind it: Software can sketch options, but working out what someone really wants from their money depends on trust built with that particular client.
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 3/4 · how much data exists 3/4.
Analyzing potential loan markets and developing referral networks to locate prospects for loans
The value here is that a specific person handles potential loan markets and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Analyze potential loan markets and develop referral networks to locate prospects for loans.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 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: Market analysis suits software well, but building a referral network is about long-running personal relationships with people who send business.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 3/4.
Show the other 20 tasks
Computing payment schedules
shifting to AIThis is reading one thing and writing another: payment schedules in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Compute payment schedules.” (O*NET task statement)
How this row was scored
Exposure score: 100 out of 100 (96–100 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: Working out payment schedules is standard arithmetic that software does instantly and accurately every time.
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 4/4.
Calculating amount of debt and funds available to plan methods of payoff and to estimate time for debt liquidation
shifting to AIThis is reading one thing and writing another: amount of debt in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Calculate amount of debt and funds available to plan methods of payoff and to estimate time for debt liquidation.” (O*NET 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: Working out debts, available funds and a payoff timeline is calculation software performs quickly from the figures supplied.
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.
Establishing payment priorities according to credit terms and interest rates to reduce clients' overall costs
shifting to AIThis is reading one thing and writing another: payment priorities in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Establish payment priorities according to credit terms and interest rates to reduce clients' overall costs.” (O*NET 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: Ranking debts by interest rate and terms to cut total cost is a calculation software performs reliably.
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.
Maintaining and reviewing account records
shifting to AIThis is reading one thing and writing another: account records in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Maintain and review account records, updating and recategorizing them according to status changes.” (O*NET 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: Keeping account records current and correctly categorised is routine system work software does quickly and consistently.
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.
Reviewing billing for accuracy
shifting to AIThis is reading one thing and writing another: accuracy in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Review billing for accuracy.” (O*NET 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: Checking bills against agreed terms and balances is exactly the kind of consistent, repetitive checking software excels at.
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.
Staying abreast of new types of loans and other financial services and products to better meet customers' needs
shifting to AIThis is reading one thing and writing another: abreast of new types of loans in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Stay abreast of new types of loans and other financial services and products to better meet customers' needs.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 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: Product and market news is public and plentiful, so software produces the summaries that keep someone current on new lending options.
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 4/4.
Submitting applications to credit analysts for verification and recommendation
shifting to AIThis is reading one thing and writing another: applications in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Submit applications to credit analysts for verification and recommendation.” (O*NET 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: Assembling the application file and passing it to credit analysts is routine paperwork that software handles end to end.
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.
Preparing reports to send to customers whose accounts are delinquent
shifting to AIThis is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Prepare reports to send to customers whose accounts are delinquent, and forward irreconcilable accounts for collector action.” (O*NET 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: Delinquency letters and account handovers follow set templates and account data, so software can produce and route them reliably.
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.
Matching individuals' needs and eligibility with available financial aid programs to provide informed recommendations
shifting to AIThis is reading one thing and writing another: individuals' needs in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Match individuals' needs and eligibility with available financial aid programs to provide informed recommendations.” (O*NET task statement)
How this row was scored
Exposure score: 74 out of 100 (67–81 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: Aid programme rules are published and eligibility matching is rule-based, so software can produce well-grounded recommendations.
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 1/4 · how much data exists 4/4.
Authorizing or signing mail collection letters
shifting to AIThis is reading one thing and writing another: mail collection letters in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Authorize or sign mail collection letters.” (O*NET 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; someone qualified has to answer for it.
The rating behind it: The letters themselves are templated and easy to produce, but someone with authority has to put their name to them.
The five ratings: output a model can produce 4/4 · 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.
Reviewing loan agreements to ensure that they are complete and accurate according to policy
shifting to AIThis is reading one thing and writing another: loan agreements in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review loan agreements to ensure that they are complete and accurate according to policy.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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 an agreement against policy is document work software does well, though unusual clauses still need an experienced person to look.
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.
Reviewing accounts to determine write-offs for collection agencies
shifting to AIThis is reading one thing and writing another: accounts in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Review accounts to determine write-offs for collection agencies.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Deciding which accounts to write off follows documented thresholds and account history that software can assess for review.
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.
Informing individuals and groups about the financial assistance available to college or university students
shifting to AIThis is reading one thing and writing another: individuals in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Inform individuals and groups about the financial assistance available to college or university students.” (O*NET 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: Student finance schemes are publicly documented, so software explains them accurately, with occasional in-person talks to groups.
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 1/4 · how much data exists 4/4.
Assisting in selection of financial award candidates using electronic databases to certify loan eligibility
changing shapeThe software now makes the first pass at selection of financial award candidates, 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 not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Assist in selection of financial award candidates using electronic databases to certify loan eligibility.” (O*NET 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: Eligibility can be checked against database rules automatically, though certifying someone for financial aid still needs an accountable staff member.
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.
Contacting applicants or creditors to resolve questions about applications or to assist with completion of paperwork
changing shapeThe software now makes the first pass at applicants, 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 not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Contact applicants or creditors to resolve questions about applications or to assist with completion of paperwork.” (O*NET 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: Software can chase missing paperwork and answer common questions, though confused applicants and creditors often need a person on the phone.
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.
Conferring with underwriters to resolve mortgage application problems
staying humanThe value here is that a specific person handles underwriters and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Confer with underwriters to resolve mortgage application problems.” (O*NET 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: Sorting out a stuck mortgage file is a back-and-forth between colleagues where judgement and give-and-take settle the outcome.
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.
Setting credit policies, credit lines, procedures and standards in conjunction with senior managers
staying humanThe rules require a named, qualified person to answer for credit policies, credit lines, procedures and standards, and that person cannot be a piece of software.
importance 3 · SupplementalSource: “Set credit policies, credit lines, procedures and standards in conjunction with senior managers.” (O*NET 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: Setting credit policy is a judgement call agreed between senior people about how much risk the firm will take.
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.
Contacting borrowers with delinquent accounts to obtain payment in full or to negotiate repayment plans
staying humanThe value here is that a specific person handles borrowers and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Contact borrowers with delinquent accounts to obtain payment in full or to negotiate repayment plans.” (O*NET 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 same decision, made over and over; the value is that a specific person does it.
The rating behind it: Getting payment from someone in financial trouble depends on a live conversation and a plan they will actually agree to.
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 3/4 · how much data exists 3/4.
Counseling clients on personal and family financial problems
staying humanThe value here is that a specific person handles clients and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Counsel clients on personal and family financial problems, such as excessive spending or borrowing of funds.” (O*NET 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 value is that a specific person does it.
The rating behind it: Talking someone through overspending or family money troubles depends on trust and sensitivity built with that particular person.
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 3/4 · how much data exists 2/4.
Supervising loan personnel
staying humanThe value here is that a specific person handles loan personnel and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Supervise loan personnel.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (9–17 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Getting the best from a team depends on knowing individuals, handling day-to-day issues in person, and earning their respect.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $76,690a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 274,330in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
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: copies of loan applicants' credit histories in, a record out. The rows above are exactly that shape: obtaining and compiling copies of loan applicants' credit histories and analyzing applicants' financial status, credit and property evaluations to determine feasibility of granting loans. What it cannot do is be answerable: applicants need a named person the rules will accept, and software cannot be that person. 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: obtaining and compiling copies of loan applicants' credit histories is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 58% of this job's task weight sits in rows the software is already learning, 17% in rows that change shape rather than disappear, and 25% in rows it is nowhere near. That is the position, measured across 30 scored tasks. It is not a forecast about you.
What you have that the software does not is meeting with applicants to obtain information for loan applications and to answer questions about the process, 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 copies of loan applicants' credit histories 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 copies of loan applicants' credit histories, 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 obtaining and compiling copies of loan applicants' credit histories” 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 meeting with applicants to obtain information for loan applications and to answer questions about the process 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, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, 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 US occupations to loan officers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was loan interviewers and clerks: only about 12% of its durable work is work you already do and it pays 34.8% less. I am not going to pretend that is comfortable news: 58% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “meet with applicants to obtain information for loan applications and to answer…” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
How that was checked: this job was compared against all 830 US 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.
Loan Interviewers and Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already meet with applicants to obtain information for loan applications and to answer questions…, and their equivalent is to interview loan applicants to obtain personal and financial data and to assist in…. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $50,020 against your $76,690, 34.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Credit Authorizers, Checkers, and Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already obtain and compile copies of loan applicants' credit histories, corporate financial statements, and…, and their equivalent is to interview credit applicants by telephone or in person to obtain personal and financial…. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% 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: 76% of its own task list already scores in the top exposure band (73/100 in this release), so the same software is eating it. It is a pay cut, in those words: $50,080 against your $76,690, 34.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 12,030 of those jobs against 274,330 of yours (OEWS May 2025), 4% as many seats.
Credit Analysts
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already handle customer complaints and take appropriate action to resolve them, and their equivalent is to consult with customers to resolve complaints and verify financial and credit transactions. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% 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: 78% of its own task list already scores in the top exposure band (70/100 in this release), so the same software is eating it. And it is a narrow door: about 64,390 of those jobs against 274,330 of yours (OEWS May 2025), 23% as many seats.
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: 58% of its task weight, across 30 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: meeting with applicants to obtain information for loan applications and to answer questions about the process 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 States figures
The United Kingdom splits this work across more than one official group, of which Credit controllers is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
In UK official statistics this job is counted as Credit controllers. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
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.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for loan officers, and we are not going to point you at the nearest one and call it a fit.
The working behind that
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 58% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for loan officers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for loan officers launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
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 Loan Officers?
- Not as a job, but it is already doing parts of the work. Across the 30 official task statements scored for Loan Officers (United States, SOC 13-2072), 58% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 60 out of 100 (range 55–66, band: high). 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 “Loan Officers” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Compute payment schedules” (100/100, very high); “Calculate amount of debt and funds available to plan methods of payoff and to estimate time for debt liquidation” (93/100, very high); “Establish payment priorities according to credit terms and interest rates to reduce clients' overall costs” (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 “Loan Officers” stay human?
- About 25% 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: “Supervise loan personnel” (13/100, minimal); “Analyze potential loan markets and develop referral networks to locate prospects for loans” (24/100, low); “Counsel clients on personal and family financial problems, such as excessive spending or borrowing of funds” (28/100, 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 “Loan Officers” do about AI?
- Start from the ledger rather than the headline: 58% of this job's weighted core work is exposed, and roughly 25% 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 Loan Officers 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 30 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
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 3 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
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt-with-imputed)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
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.
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}
}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.