Britons Turn to AI for Money Help: Privacy, Speed and Smart Saving

Britons are embracing AI for money management faster than many financial institutions expected, but the real story is not simply about convenience. It is about trust, privacy, and the changing psychology of asking for help with personal finances. A new Post Office-commissioned poll of 2,000 adults suggests that AI is now part of the mainstream conversation around saving, bills, and budgeting, even as many people remain cautious about sharing money worries with other humans. The result is a distinctly modern contradiction: people want speed and privacy, yet they also want reassurance that the advice they receive is safe and sensible.

Background — full context​

The latest findings sit inside a wider shift in how people seek financial guidance online. According to the poll, 50% of Britons would now turn to AI for help with their finances, while independent blogs and podcasts, YouTube, and workplace conversations remain important sources of advice. The same research found that 13% would prefer AI over a human adviser, with 30% saying they would do so because they worry about being judged. That last figure may be the most revealing: for many people, AI is not just a productivity tool, but a low-friction substitute for a conversation they would rather not have.
That preference makes sense in remains a difficult subject. The poll found that older adults were more uncomfortable discussing finances with friends than younger generations, with 29% of Boomers expressing discomfort compared with 18% of Gen Z and 17% of Millennials. Yet the younger cohort is also the most experimental. Gen Z respondents were more likely to use search engines and large language models such as ChatGPT or Google AI, and a fifth said LLMs were more helpful for money tips than a financial adviser or a bank. That does not mean younger users are rejecting traditional finance entirely; it means they are building a hybrid advice stack that blends algorithms, social media, expert brands, and human judgment.
The research also suggests that money habits in the U than stereotypes sometimes imply. More than half of respondents said they track monthly expenses, build an emergency fund, and avoid impulse purchases. But discipline does not equal ease. A third of those surveyed said they had found it difficult to save any money over the last 12 months, which helps explain why simple, reassuring, and always-available AI tools are gaining traction. People do not just want an answer; they want the feeling that progress is possible.
Post Office Financial Services Director Ross Borkett framed the trend as paal shift away from silence around money. His comments emphasised that people are increasingly willing to discuss finances with friends, family, colleagues, online forums, or AI, and that the important thing is not the channel but the habit: track outgoings, save regularly, and build securely. The company also used the poll to reinforce the appeal of its own savings accounts, which it describes as simple and flexible, with starting balances as low as £1. That marketing message matters because it positions AI not as a replacement for financial products, but as the new front door to them.

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Why AI is becoming a money coach​

The appeal of anonymity​

One of the clearest findinat a significant share of people do not want a moral reaction when they ask for financial help. Thirty percent said they would prefer AI because they worry about being judged. That is a powerful insight because it suggests AI’s biggest advantage may not be intelligence at all, but social neutrality. A model does not sigh, interrupt, or look disappointed. For users who feel embarrassed about debt, overspending, or low savings, that matters.

Speed beats scheduling​

AI also wins because it is immediate. Traditional financial advice often requires appointmeng, or waiting for a bank response. AI can give a draft budget, a savings plan, or a bill-reduction checklist in seconds. That speed is especially attractive when the issue is small but urgent, such as trying to trim a grocery bill or decide whether a subscription is worth keeping. In practice, the convenience factor can be enough to get people to act.

Advice without the social cost​

The poll’s wider theme is that people are quietly assembling a no-awkwardness advice network. They will ask an AI tool, browse a blog, watch a video, or talk to a colleague before they will always speak to a professional. This is not necessarily a rejection of human expertise. It is a search for a less intimidating first step. In that sense, AI behaves like a digital practice run before the real conversation.

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Gen Z is normalising algorithmic money help​

Search first, ask later​

The youngest adults in the poll are the most comfortable using digital tools to start the moZ respondents were far more likely than Boomers to use search engines and large language models for financial help, and they were also more likely to rate those tools as useful. That suggests a genuine generational change in the default behaviour around advice-seeking. Instead of asking a parent, bank, or adviser first, many younger users ask a machine.

LLMs as a practical first draft​

This does not mean Gen Z trusts AI blindly. Rather, AI appears to function as a first draft generator. Users may ask for a budget framework, a comparison of saxplanation of a financial term, then refine the output with more specific questions. That kind of workflow fits the way large language models are best used: as conversational assistants that help people get oriented before making a decision.

Established experts still matter​

Even among younger groups, well-known money voices still have influence. The poll notes that Millennials and Boomers place primary trust in established savings experts like Martin s a useful point: AI is growing, but it has not displaced trusted brands. Instead, the market is becoming layered. People may use a model to explore a topic, then confirm the answer with a known expert or institution. That is a healthy behaviour if the confirmation step actually happens.

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Why privacy is central to financial AI​

Personal data and personal shame​

Money conversations are never just about arithmetic. They are about identity, status, relationships, and fear. That is why privacy matters so much. A perssharing their budget with an AI tool may still be reluctant to tell a partner, colleague, or adviser the same details. For many users, AI creates psychological space to be honest. It lowers the emotional barrier to disclosure.

The hidden trade-off​

But privacy also creates a new trade-off: the more comfortable people become with AI for finances, the more likely they are to share sensitive information with systems they do not fully understand. The poll itself shows that people wantafer socially, not necessarily because it is safer technically. That distinction matters. A user may perceive secrecy where the real issue is data handling, model retention, or vendor access.

Trust is earned in the details​

Financial tools do not get trust merely by sounding polite. They need clear explanations of what data is stored, what is used to generate recommendations, and what is never shared. Users may happily ask an AI how to reduce bills, but they are less ue answers if the tool is connected to real account data or spending history. In finance, transparency is not a nice-to-have. It is the product.

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Human advice is not disappearing​

Colleagues are now part of the mix​

One of the more interesting findings in the poll is that 27% of respondents were happy to talk to colleagues about their finances. That suggests a gradual normalisation of everyday money conversation in the workplace. It is not just a sign of AI growth; it is also evidence that money shame is weakening in some settings. Colleagues can be less intimidating than family, and sometimes more relevant than a generic online forum.

Blogs, podcasts, and YouTube remain useful​

The research also shows that independent blogs, podcasts, and YouTube are still part of the advice ecosystem. That matters because it proves AI is entering a crowded field rather than creating one from scratch. People are already used to asynchronous, self-serve advice. AI is simply the nexhaviour, with conversation added on top.

The bank is still the bank​

Traditional institutions still carry weight, especially when the advice involves regulated products, savings safety, or product terms. The poll indicates that people may turn to AI first, but not necessarily last. Banks and financial advisers retain their role where compliance, accountability, and product suitability matter moss not AI instead of humans, but AI before humans.

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What people are asking AI to do​

Budgeting and bill reduction​

The poll points to practical uses rather than glamorous ones. People want help saving money, reducing bills, and setting goals. That is the everyday, unsexy side of AI adoption, and it may be the most durable. Tools that can organise expenses, suggest ways to cut waste, or create a paydown plan are far moreeatedly than flashy one-off gimmicks.

Behavioural nudges​

AI can also act as a behavioural prompt. If someone asks for a savings strategy and receives a simple method like tracking outgoings, automating transfers, or using a rule-based budget framework, the tool is not just informing them; it is nudging them into action. That is important because many people do not fail financially for lack of information. They fail for lack of follow-rks like 40-30-20-10
The article’s mention of the “40-30-20-10 method” is a reminder that people like simple structures they can remember without an app. AI may help popularise these frameworks by explaining them in plain language, adapting them to local circumstances, or turning them into personalised plans. That kind of accessibility is where AI can add real value: not inventing financial wisdom, but making it easier to use.

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The promise of AI advice is also its danger​

Confidence without competence​

The biggest risk in financial AI is that the answers sound right even when they are incomplete, oversimplified, or wrong. Finance is full of edge cases: income variability, debt interest, household obligations, credit score effects, tax implications, and product terms. A chatbot can be helpful with general guidance, but it is not automatically equipped to spot the nuancndation safe for a specific person.

Over-generalised advice​

A model may tell users to build an emergency fund, cut discretionary spending, or automate savings, and those are sensible baseline suggestions. But if the user is already living close to the margin, the same advice may be unrealistic without a more careful look at income timing, debt burdens, or upcoming costs. Good advice is context-sensitive. That is the difference between a useful assistant and a digital slogan machine.

Advice is not regulation​

Another concern is that people may mistake conversational fluency for legitimacy. A well-written answer does not mean the advice is regulated, insured, or accountable. For high-stakes decisions, users still need to know whether a tool is merely informative or whether it is acting in a professional advisory capacity. AI can help people prepare for a conversation with a bank or adviser, but it should not pretend that preparation is the same thing as expert judgment.

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Why thnd building societies​

The front door is changing​

For financial institutions, the most important implication is that customer journeys are being rewritten. People may not begin on a bank homepage or in a branch. They may begin with a prompt: “How do I save more each month?” The first answer they trust may come from AI, not the institution. That means banks have to compete not just on rates and products, but on explainability and digital presence.

Simple beats sophisticated​

The Post Ofd simple savings products with low minimum balances is instructive. If customers are using AI to look for help, they are probably also looking for simplicity once they arrive at a product page. Complex terms, hidden conditions, and clunky onboarding are likely to lose out to cleaner propositions. In this environment, clarity is a competitive advantage.

Education becomes acquisition​

Financial literacy content may no longer be a side channel. It may become the first point of conversoviders, and advice brands that explain topics in plain language, and in a format AI can surface easily, are likely to benefit. The institutions that win may be the ones that make it easiest for both humans and machines to understand what they offer.

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The cultural shift is bigger than the technology​

Money talk is getting less taboo​

The poll’s most encouraging signal may be the broad willingness to discuss ns have long been stereotyped as private about money, but the numbers suggest that the silence is loosening. AI fits into that broader cultural shift because it offers a way to speak before one is ready to speak to a person. It is a bridge across embarrassment.

AI as a rehearsal space​

For many users, AI is best understood as a rehearsal room. It lets them test language, compare options, and ask basicling exposed. That is particularly valuable for younger adults just starting to manage bills, or older adults who want to check an idea before talking to family. The tool may not be the final authority, but it can reduce inertia.

The quiet productivity story​

There is also a less visible economic point here. If millions of people can make better savings decisions, reduce avoidable bills, and budget more consistently, the cumulative effect could bens in household financial resilience can translate into less stress, better planning, and more stable consumer behaviour. In other words, AI’s money impact may not come from dramatic wins, but from many small corrections.

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Strengths and Opportunities​

The poll’s biggest strength is that it captures a real behavioural shift rather than a hypothetical one. People are already using AI, searc forums to get money help, and the data shows that this is now a normal part of the advice landscape. That creates opportunities for consumers, institutions, and educators alike. AI can reduce the intimidation of financial planning, make budgeting more approachable, and help users start conversations they might otherwise avoid.
It also creates opportunities for better-designed financial products. If customers want simplicity, low entry points, and clear guidance, pund those expectations instead of fighting them. The Post Office’s emphasis on flexible savings and small starting deposits is a good example of product design aligned with user behaviour.
Other strengths and opportunities stand out:

• AI lowers the emotional barrier to asking for help.
• Younger users are already comfortable with machine-based guidance.
• Human and digital advice can complement each other.
• Regular tracking and emergency-fund habits remain strong.
• There is room for plain-language financial educatiat explain clearly may win trust faster.
• Workplace conversations can support healthier money habits.
• AI can help users turn abstract goals into practical steps.

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Risks and Concerns​

The risks are equally clear. Financial AI can be persuasive without being precise, and that is dangerous in a domain where small errors can become expensivnsitive information without understanding how it is stored or used. They may also over-trust a model ss confident. Those are not hypothetical concerns; they are the predictng advice feel frictionless.
There is also a generatiadults lean heavily on LLMs while older adults stay with famitry could end up with uneven levels of financial confidenhaviour. Some users will cross-check advice carefully; othe where mistakes can affect debt, savings, and long-term stabilitters.
The main concerns are straightforward:

• AI may produce over-generalised guidance.
• Users may confuse helpfulness with correctness.
• Sensitive financial data may be shared too casually.
• People with the least margin may be given unrealistic tips.
• Institution trust can be weakened if advice feels impersonal.
• A false sense of privacy may encourage oversharing.
• AI may encourage action without adequate checking.
• Financial guidance without accountability remains a structural risk.

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What to Watch Next​

The next phase will be whether AI advice moves from curiosity to habit. If users keep returning to AI for budgeting, saving, and bill-cutting help, the technology will become a permanent part of the UK financial advice ecosystem rather than a novelty. Watch whether banks and savings providers begin to design their customer journeys for AI-assisted discovery, because that would be a sign the market has fully internalised tlso be important to see whether financial education becomes more embedded in everyday s start including clearer warnings, verification stmpare options, they could reduce some of the risk they cvenience-versus-safety tension will remain unresolved.
Finallether the workplace becomes a more explicit setting for financialns. The fact that 27% of respondents are comfortable taggests employers may have a bigger role than before ink. That could lead to more support, more openness, and, ideally, more sus.
Britons are not turning to AI because they have stopped valuing human expertise. They are turning to it because it is fast, private, and emotionally easier to approach. That makes AI a powerful first step in financial decision-making, but not a substitute for judgment. The winners in this next stage will be the tools and institutions that combine convenience with clarity, and confidence with caution.

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Source: mirror.co.uk One in ten Brits prefer using AI for money help - reason why might surprise you

 

[ChatGPT]

Britons are quietly shifting whom — and what — they trust with their finances: a recent media report captured by community briefings shows one in ten adults in the UK now prefer an AI tool to a human when they seek routine money help, while roughly half would consider using AI for everyday budgeting and bill-cutting tips. shift toward AI personal finance is not a fringe phenomenon. What began as curiosity about chatbots and budgeting apps has become a practical, sometimes urgent, consumer behaviour change. In the survey coverage that prompted the headline, the stated drivers are familiar: convenience, speed, and an appetite for privacy when discussing money — a subject many find awkward with a person.
At the same time, te a larger arc in which generative AI assistants — from consumer chatbots to integrated workplace copilots — have expanded their remit from creative writing and search to transactional and decisional domains. Analysts and tech commentators have documented a rapid move of AI into budgeting, tax help, and investment research, and fintech firms are packaging these capabilities into consumer-facing products.
This article synthesises what the availd us about why roughly one in ten people now prefer AI for money help, what that preference actually looks like in practice, and the strengths and risks consumers and policy-makers must face as AI moves deeper into everyday personal finance. Where specific claims or quoted articles were unavailable for direct verification in the provided materials, I flag that clearly and treat those specifics with caution.

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How the "one in ten" headline maps to behavior​

What the figure actually signals​

A single-line headline — one in ten Brits prefer using AI for money help — can be read as a dramatic endorsement of machines over humans. In reality, the evidence in the reporting and surrounding commentary suggests a more nuanced picture: most consumers remain hybrid users who use AI for particular tasks rather than a wholesale replacement of human advisers. The poll coverage cited shows:

• About half of adults would consider using AI for everyday money help such as saving tips and bill-cutting ideas.
• A notable minority — distilled in headlines as “one in ten” — said they would prefer an AI tool over a human adviser for certain money questions.

Those two facts together point to a pragmatic adoption curve: many people are opensee it as a general replacement for the expertise, regulation, and trust that human advisers provide.

Why some people prefer AI​

The drivers named in the coverage and corroborating industry threads converge on three consistent motivators:

• Privacy and emotional comfort. Money can be intimate and embarrassing; some people prefer talking to a machine rather than disclosing debts or spending habits to another person. The survey commentary explicitly highlights privacy as a practical reason for picking AI.
• Speed and convenience. AI tools offer instant, always-on responses. For small, everyday questions0 a month?” or “Which direct debit can I cancel?” — the trade-off between a quick algorithmic answer and a slower human consultation often favours speed.
• Cost-sensitivity. Paid financial advice is expensive; many consumers see AI as a low-cost way to get directional guidance that helps them avoid trivial mistakes or steers them toward savings opportunities. This rationale underpins much of the growth in AI personal finance usage documented in industry roundups.

These motivations explain why preference for AI clusters around routine, transactional, or private tasks rather than complexns like retirement planning or mortgage structuring.

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What people actually ask AI about — and what AI does well​

The sweet spot for AI​

AI assistants shine at a narrow set of finance tasks where computation, pattern recognition, and templated guidance deliver clear value:

• Quick budgeting advice: categorising monthly expenses, flagging recurring charges, suggesting low-effort savings moves.
• Price and provider comparisons: checking standard rates, highlighting glaringly expensive subscriptions, or comparing basic insurance quotes.
• Administrative coaching: showing how to set up direct debits, apply for council tax reductions, or identify paperwork needed for an application.
• Spreadsheet automation: turning bank-export CSVs into categories and charts, an area where small automation dramatically reduces friction.

For these tasks, AI can be materially helpful because it reduces friction: it can process structured data faster than most consumers and surface ght otherwise miss.

Where AI struggles​

• Context and nuance. AI answers are only as good as the information fed to them. Complex personal situations — mixed incomes, irregular self-employed earnings, or tax nuances — require domain expertise and often human judgment. Industry analysis warns that AI should be framed as assistive, not authoritative, in these cases.
• Provenance and accuracy. Users and commentators have repeatedly observed that AI outputs require verification. Surveys find that many users treat chatbot respons and follow up with fact-checks. That behaviour mitigates but does not eliminate risk if users act immediately on unchecked advice.
• Regulatory constraints. Financial advice is regulated in many jurisdictions. Where AI gives what looks like bespoke financial advice, firms and vendors must navigate licensing, acco-keeping obligations — a thorny compliance problem that regulators and industry templates are actively addressing.

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The privacy paradox: why people trust AI with money questions — and why they shouldn't be complacent​

Perceived privacy vs. actual data flows​

Privacy is a leading reason people reach for AI about s simple: a chat with an AI feels like a private, anonymous exchange. But the reality of data flows is more complicated.

• Many consumer AI tools log conversations, retain derived data, and route queries through third-party cloud services. That data can persist in training datasets or product telemetry unless explicitly redacted or covered by clear data-retention policies.
• Enterprises and fintechs have started to publish AI governance and data-redaction templates for financial use-cases — an acknowledgement that sensitive financial data requires stricter handling than casual chat. Templates and policy guidance urge centralized account management, mandatory staff training, and explicit prohibitions on using personal AI accounts for company data.

The upshot: the perception of privacy can be misleading unless a user confirms the tool’s data practices and any related platform’s retention and re-use policies.

Practical safeguards consumers should demand​

• **Local procedevice models where possible for highly sensitive queries.
• Explicit data-retention and deletion controls during onboarding.
• Anonymisation and redaction mechanisms before uploading documents (bank statements, identity documents) to a cloud-powered assistant.
• Audit trails and provenance for recommendations that materially affect money decisions.

Industry analysts recommend consumers treat AI outputs as suggestions to verify, not final instructions — a habit many users have already adopted in practice.

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Regulation, liability and the new responsibilities for fintechs​

The regulatory gap​

Generative AI moves faster than rule-making. Financial regulators have long-established rules for what constitutes regulated financial advice, disclosure reility testing. Integrating AI into advice workflows raises immediate questions:

• When does a chatbot cross the line from general information to regulated advice?
• Who is liable if an AI suggestion causes financial harm — the vendor, the model provider, or the aggregator that integrated the model?
• How should provenance and data lineage be recorded so auditors and regulators can reconstruct decisions?

Industry playbooks and governance templates are emerging, but the coverage is uneven and often sector-specific. Firms experimenting with "Treasury GPT"–style assistants in enterprise finance have focused on controlled, internal use-cases. Consumer-grade deployments need equally careful guardrails if they’re to substitute for regulated advisory services.

Business responses we’re seeing​

• Product teams are embedding explainability and confidence indicators in outputs, making it clear when a suggestion is a “rule-of-thumb” versus a legally regulated recommendation.
• Vendors are building “human-in-the-loop” flows sises flagged by AI.
• Some fintechs are packaging AI as triage — surface the facts, highlight potential savings, then route the user to an adviser for any consequential decision.

These mitigations aim to preserve AI’s convenience while respecting the legal frameworks that govern financial advice.

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Real-world case studies and comparative narratives​

The human-versus-AI comparison: where people say machines win​

In consumer trials and media comparisons, AI typically outruns humans oe capacity to crunch numerous data points instantly. For ordinary money tasks — cancelling redundant subscriptions, suggesting low-cost bank accounts, or producing a simple monthly budget — the AI experience often feels superior.
Echoing this, community analyses on choosing between different AI personal finance assistants emphasise that selection depends on three things: where your data lives, how much auditability you need, and how you plan to verify results. That triage explains why some users prefer quick AI help while reserving human advisers for complex or emotionally charged decisions.

The food-culture contrast: value perception in consumer choices​

A secondary strand in the media coverage supplied for this piece contrasted two consumer behaviours: choosing a cheap, trusted provider (exemplified by chains like Greggs) and sampling an expensive, artisanal alternative. That comparison is emblematic: many people treatal decisions similarly — choose the known, low-cost route for routine spending, but occasionally experiment with premium options when the perceived value justifies the cost.
I should note an important caveat: the specific newspaper comparison piece about "the world's most expensive bakery vs. Greggs" was not available in the uploaded material for direct verification, so detailed claims from that article are treated as unverified in this analysis. However, the broader theme — how consumers balance cost, convenience, and perceived value — is relevant to why people reach for AI for money help in the first place.

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Strengths: why AI will stay in the money-help toolkit​

• Lower friction for routine tasks. AI reduces the cognitive load of small, repetitive money decisions. That lowers procrastination and leakage (missed cancellations or unclaimed rebates).
• Accessibility. For people priced out of traditional advice, AI offers cheaper guidance that can ve financial outcomes — for example, identifying a cheaper energy tariff or an overlooked benefits entitlement.
• Automation and integration. When integrated with banking APIs and spreadsheet automation, AI assistants can surface actionable insights witning messy data into a usable plan.
• Normalisation of verification behaviour. Evidence suggests many users already treat AI answers as starting points; that healthy scepticism reduces risk when combined with better educational nudges.

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Risks and blind spots — a practical checklist​

• Hallucination risk: Generative models can invent plausible-sounding but incorrect facts. Don’t rely on an AI for legal or tax certainty.
• Data privacy and reuse: Uploaded financial documents may be retained or used to fine‑tune models unless the vendor provides strong guarantees and deletion controls.
• **Regulatory misclassifts stray into regulated advice, users and vendors can be exposed to legal risk. Know whether the tool supplies general guidance or regulated advice.
• Vendor and model dependency: Consumers using a product built oninherit that model’s update and policy changes — a stability and governance risk.
• Equity and exclusion: Not every consumer has digital literacy or API-friendly bank help access but also entrench disparities if design and outreach aren’t inclusive.

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Practical advice for consumers and technologists​

For consumers​

• Treat AI outputs as *starting pointng on anything with material consequences.
• Prefer tools with explicit data-retention, deletion, and anonymisation options. Ask vendors how they store and reuse your promptUse AI for triage and automation (categorising expenses, finding obvious savings), but escalate to a licensed adviser for complex tax, retirement, or legal matters.

For product and compliance tnability* and provenance into outputs so end-users and auditors can trace how recommendations were derived.​

• Adopt a human-in-the-loop model for borderline regulated advice, andsuggestion is general information versus a personalised, regulated recommendation.
• Implement strict redaction and anonymisation when customers upload documents, and provide a clear deletion pathway.

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The future: what changes if one in ten becomes one in three — or one in two?​

If the preference rate for AIs from a notable minority to a large cohort, the implications extend beyond convenience:

• Market structure shifts: Advice marketplaces and banks will need to services. Commodity advice will be automated; human advisers will differentiate on complex judgment, interpersonal trust, and fiduciary responsibility.
• Regulatory maturity: Regulators will be pushed to clarify when AI-driven guidance is regulated advice and to mandate guardrails for provenance, auditability, and consumer redress. Templates for enterprise governance will likely be adapted to consumer settings.
• Societal effects: Greater reliance on AI for routine money decisions could improve outcomes at scale (fewer missed payments, better saving rates), but could also create systemic vulnerabilities if many consumers act on the same modelled guidance that is later shown to be flawed.

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Conclusion​

The headline — one in ten Brits prefer us — captures a symbolic moment in consumer behaviour: AI is no longer only a curiosity; for a meaningful slice of the population, it is a preferred tool for certain financial tasks. But preference is task‑specific and pragmatic. People turn to AI for privacy, speed, they still turn to humans for judgement, licensing and the emotional labour of financial planning.
The responsible path forward is a hybrid one: retain human expertise where it matters, and deploy AI where it reduces friction and extends access — but do so with strong governance: clear data practices, regulatory clarity, and designs that assume users will verify and question outputs. Consumers should treat AI as a powerful assistant, not an infallible adviser; businesses and regulators should close the governance gaps before those assistants shoulder heavier financial responsibilities.
Note on source material: the core findings discussed here are drawn from the survey reporting captured in the provided materials and corroborating industry analysis in community and product threads. A separate Mirror comparison of a high-end bakery and Greggs referenced by the user was not available for verification in the materials supplied; observations about that piece have therefore been treated as illustrative rather than evidentiary.

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Source: The Mirror https://www.mirror.co.uk/money/one-ten-brits-prefer-using-36779282/