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What questions should mortgage advisers ask before relying on automated sourcing results?

By TheFinanceBase Team 11 min read

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In UK mortgage advice, an automated sourcing list is a starting point—not proof that the search was complete or that a mortgage is suitable. Before relying on it, ask what the system did, which customer facts and assumptions it used, which lenders and products it searched, how it filtered and ranked them, whether the output indicates eligibility or supports a personal recommendation, and what human checks and records are required. The firm giving advice remains responsible for suitability under the FCA’s mortgage rules.

Why an automated result needs a deliberate review

Mortgage sourcing systems are intermediary tools that filter and rank mortgage products against criteria selected by a broker. That makes them efficient, but every result is shaped by the data available to the system and by the choices built into the search. A short list may reflect exclusions, stale lender information or a narrow interpretation of the customer’s circumstances rather than the best available route.

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The FCA’s mortgage materials distinguish between showing objective product facts and using customer information to produce a targeted result. The more a system interprets a customer’s circumstances, the closer its output can come to regulated advice. The FCA Handbook’s PERG 4 guidance says: “In that case the production of a list of results uses an element of opinion and skill (albeit automated) in translating the customer’s input into a display of a particular product or products.” The label on the software does not decide the regulatory status; what it actually does matters.

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Use the questions below as a review sequence. They are written for advisers and firms operating in the UK; another jurisdiction may impose different duties.

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1. What is the system actually doing?

Is it displaying facts or interpreting the customer’s case?

First identify the output’s function. A basic display may show rates, fees, loan-to-value bands and lender criteria without drawing a conclusion about the customer. A more complex tool can translate income, expenditure, credit circumstances and objectives into a filtered or ranked list. That second type uses automated opinion and skill, even if the screen calls the result a “search”.

  • Product information: Are you simply viewing published facts that you must interpret yourself?
  • Eligibility indication: Is the system estimating whether a lender’s stated criteria may be met?
  • Recommendation support: Is it selecting or ranking products using the customer’s needs and objectives?
  • Automated advice: Does it present a product as suitable, or generate a personal recommendation without a qualified adviser’s assessment?

Ask the provider to describe the decision logic in plain language, including whether any machine-learning model is involved, which rules are fixed, and where an adviser can override the result. Do not assume that every automated sourcing tool uses artificial intelligence, and do not assume that a tool avoids advice regulation merely because a human clicks the final button.

2. Are the customer’s inputs accurate, complete and current?

Check the facts that drive the search

A technically correct calculation can still be wrong for the customer if the inputs are incomplete. Confirm the information that affects affordability, eligibility and suitability, rather than accepting pre-filled values or broad assumptions.

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  • Income: employment status, basic pay, variable pay, overtime, bonuses, self-employment income and the period used to evidence each item.
  • Outgoings: regular living costs, commitments, childcare, maintenance, insurance and any expenditure that the system may not ask about explicitly.
  • Debts: credit cards, loans, overdrafts, hire purchase, student-loan treatment and repayment changes after completion.
  • Property and loan details: purchase or remortgage purpose, property type, tenure, value, loan amount, term, deposit and any future borrowing plan.
  • Objectives and preferences: payment certainty, term flexibility, fees versus rate, planned move, overpayment needs and tolerance for payment changes.
  • Personal circumstances: dependants, health-related financial impacts, recent life changes, residency or visa status and any vulnerability that affects communication or decision-making.

FCA reviews of automated advice in another financial sector found weak fact-finding, assumptions about customers and missing information about debts and outgoings. That work concerned automated investment services rather than mortgage sourcing, so it is cross-sector evidence about control risks, not a finding that every mortgage tool has the same defect. The practical lesson is to compare the system’s questionnaire with your firm’s fact-find and investigate every material gap.

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3. What market did the system search?

Coverage, exclusions and data freshness

Ask for a precise description of the market represented in the result. “Whole of market” or “available products” is not self-explanatory unless you know which lenders and product types are connected and what is excluded.

  • Which lenders, brands, direct-only products and specialist ranges are included?
  • Are any lenders absent because they do not provide data, require a separate portal or have temporarily withdrawn products?
  • What product categories are excluded—such as adverse-credit, shared-ownership, interest-only, new-build or specialist property cases?
  • When was each lender’s rate, fee and criteria data last updated, and how are withdrawals or temporary criteria changes handled?
  • Does the search include products that require a manual referral or additional underwriting rather than an instant decision?

FCA feedback on mortgage sourcing describes systems that filter and rank listings according to broker-selected criteria. Respondents also raised concerns that limitations in displayed information and filtering could make some products less prominent, including innovative products. That is consultation feedback, not a finding of proven market-wide harm, but it is a reason to inspect the search perimeter instead of treating the visible list as the entire market.

4. Which filters and ranking rules shaped the list?

Understand what the system removed and what it promoted

A result can change materially when you alter a single filter. Record the criteria selected by the adviser and identify defaults that were applied automatically.

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  • Loan-to-value, loan size, term, repayment type and rate type.
  • Income multiples, affordability stress assumptions and treatment of variable income.
  • Credit history, missed payments, debt-to-income limits and residency rules.
  • Property construction, lease length, occupancy, location and acceptable use.
  • Fees, initial rate, follow-on rate, incentives, early-repayment charges and portability.
  • Sorting logic: lowest initial payment, lowest total cost, lender preference, speed, commission, or another criterion.

Ask whether the ranking is objective, configurable by the firm, influenced by commercial arrangements or simply based on the order in which data is received. A product appearing first is not evidence that it is suitable, cheapest over the relevant period or available to this applicant. Run a sensitivity check: remove a non-essential filter, change the sort order and see whether materially different products appear. Investigate why a surprising product disappears.

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5. Does the result show likely eligibility or establish suitability?

Keep the two decisions separate

An eligibility indication usually means that the supplied facts appear to fit a lender’s stated criteria. It is not a binding offer and may not account for underwriting, valuation, documentation or facts the lender verifies later.

Suitability is broader. Under MCOB 4, a firm giving mortgage advice must take reasonable steps to ensure that an advised regulated mortgage contract is suitable for the customer. That requires matching the product and repayment structure to the customer’s needs and circumstances, not merely finding a lender that may accept the application.

Before relying on the output, ask:

  • What facts does the eligibility calculation test, and what does it leave to manual assessment?
  • Does the result account for the customer’s stated objectives, foreseeable changes and payment-risk tolerance?
  • Is the result being used only to narrow research, or is it part of the evidence for a personal recommendation?
  • What further checks are required before an adviser can recommend the product?

Explain to the customer, where relevant, that an automated match is an indication rather than confirmation of acceptance. Keep the recommendation rationale in the adviser’s own words.

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6. What could be wrong, stale or missing?

Test data reliability and failure modes

Ask how the system identifies data errors, conflicting lender criteria and incomplete records. Find out whether it displays the source and date of each key fact, flags a missing field, and prevents an adviser from treating an estimate as a confirmed rule.

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In feedback recorded in the FCA’s mortgage-rule review (FS25/6), respondents raised concerns that AI can make errors, hallucinate or produce incomplete data. Those are respondents’ concerns reported by the FCA, not a quantified FCA error rate. They nevertheless describe concrete failure modes: an invented criterion, a confidently stated but outdated rate, or a result that omits a relevant lender because data was unavailable.

For any unexpected result, obtain confirmation from the lender’s current criteria or application channel. Escalate contradictions rather than choosing the more convenient answer. A tool that cannot show when its data was refreshed, what assumptions it made or why a product was excluded should not be treated as the sole basis for a recommendation.

7. Where is qualified human judgment essential?

Non-standard cases and vulnerability

Standardised questions cannot capture every material circumstance. Arrange deeper fact-finding when the customer has complex income, recent credit problems, unusual property characteristics, immigration or residency issues, a significant future change, or a need for a specialist lender.

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Vulnerability may affect both the mortgage decision and the way information is gathered. Check whether the customer needs additional time, accessible communication, a trusted third party, or a different explanation of payment risk. FCA strategy material for mortgage intermediaries links good outcomes to understanding customer circumstances and vulnerability; an automated form does not replace that conversation.

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Set a clear sign-off point. A qualified adviser should review the full case, challenge the output where necessary and approve any recommendation. If the tool suggests a product that conflicts with the customer’s objectives or with information gathered outside the form, the adviser should resolve the conflict before proceeding.

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8. How should an adviser challenge a surprising result?

A practical review sequence

  1. Freeze the original search. Save or print the result, timestamp, inputs, filters, ranking order and displayed assumptions before changing anything.
  2. Reconcile the fact-find. Compare every material input with documents and the customer’s explanation. Correct omissions, not just obvious typing errors.
  3. Re-run with controlled changes. Change one filter or assumption at a time so you can see what caused a product to enter or leave the list.
  4. Check the lender source. Verify critical criteria, rates, fees and restrictions through the current lender information available to the firm.
  5. Investigate alternatives. Consider whether an excluded lender or specialist route could meet the customer’s needs better, and record why it was included or rejected.
  6. Apply suitability judgment. Compare the remaining options with objectives, affordability, foreseeable changes and payment-risk tolerance.
  7. Escalate unresolved uncertainty. Refer the case to a senior adviser, compliance function or lender contact when the system cannot explain the result.
  8. Record the decision. Note corrections, overrides, rejected products and the reason the final recommendation was suitable.

9. What should the firm retain as evidence?

Build an audit trail that another reviewer can follow

Keep enough information to reconstruct the decision, subject to data-protection and retention requirements. The record should identify:

  • the customer facts and documents relied on;
  • the system version or search date, if available;
  • lenders and product scope searched;
  • filters, ranking settings and assumptions;
  • the original result and any revised result;
  • checks against lender information;
  • adviser interventions, overrides and reasons;
  • the suitability analysis and customer explanation; and
  • any escalation, exception or manual referral.

FCA findings on automated advice warn that unrecorded interventions can make it difficult for a firm to demonstrate suitability. A note saying “system recommended product A” is not enough if the adviser changed an income figure, excluded a lender or selected a different product. Record what changed and why.

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10. Questions to put to the software provider

Topic Question Why it matters
Market scope Which lenders and product types are included or excluded? Shows whether the visible list represents the intended search.
Data quality How are criteria, rates and fees updated, dated and corrected? Reveals the risk of stale or conflicting information.
Logic Which rules, models and defaults filter or rank results? Explains why products appear, disappear or change order.
Customer inputs Which fields are mandatory, inferred or left for adviser judgment? Identifies hidden assumptions and fact-finding gaps.
Explainability Can the adviser see the reason for an inclusion, exclusion or eligibility flag? Allows a result to be challenged rather than accepted blindly.
Controls Can the firm restrict overrides, require review or route exceptions? Supports consistent human oversight.
Records What inputs, outputs, changes and timestamps are retained and exportable? Creates evidence for suitability and complaints handling.
Incidents How are errors, outages, model changes and data corrections communicated? Lets the firm assess affected cases and remediate them.

11. Red flags that should stop reliance on the result

  • The system cannot state which lenders or products it searched.
  • Rates or criteria have no update date, or the provider cannot explain data delays.
  • Mandatory fields are absent for debts, outgoings or material customer objectives.
  • A ranking is presented as “best” without defining the ranking criterion.
  • The output uses language of certainty despite being only an eligibility estimate.
  • The adviser cannot reproduce the search or export the original inputs and result.
  • The tool produces a recommendation for a vulnerable or non-standard customer without a qualified review step.
  • An unexpected result conflicts with current lender information and the system offers no escalation route.

When a red flag appears, pause the recommendation, complete the missing fact-finding and use independently verified lender information. If the issue could affect other cases, notify the firm’s compliance or governance function and assess whether previous recommendations need review.

12. A concise pre-reliance checklist

Before treating an automated sourcing result as part of advice, the adviser should be able to answer “yes” to each applicable question:

  • Do I know whether the output is factual information, an eligibility indication or recommendation support?
  • Have I verified the customer’s income, outgoings, debts, objectives and relevant circumstances?
  • Can I state which lenders and products were searched and which were outside scope?
  • Do I understand the filters, defaults and ranking order?
  • Have I checked the freshness and reliability of critical lender data?
  • Have I tested or investigated any surprising exclusion or result?
  • Have I considered vulnerability and any need for specialist human assessment?
  • Can I explain why the final product is suitable, rather than merely likely to be accepted?
  • Have I saved the inputs, output, intervention and rationale?

Regulatory scope and accountability

This guidance is framed around UK mortgage advisers and FCA materials, including MCOB 4, PERG 4, the FCA’s mortgage-sector feedback on automated tools and its strategy for mortgage intermediaries. The FCA’s automated-investment expectations are useful by analogy for fact-finding, governance, records and oversight, but they are not mortgage-specific rules. Firms should map the controls to their own permissions, advice model, Consumer Duty obligations and data-protection requirements.

Automation can reduce repetitive searching, but it does not transfer accountability to a software supplier or to a ranking algorithm. The adviser and the firm must still understand the customer, challenge the output and demonstrate why the mortgage recommended was suitable.

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