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AI-generated answers are becoming another way consumers research and compare banking products—but they have not replaced search engines, bank websites, or other established channels. For banks, discoverability increasingly means not only earning a click, but also being included and described accurately when an AI answers a question such as “best savings account.” Clear product information, credible references, and ongoing checks of AI answers can help, but no established formula guarantees visibility or business results.

How consumers are using AI to research banking

Survey evidence points to a new discovery route, not a wholesale switch away from existing ones. In McKinsey’s May 2025 Global Banking Annual Review survey, 23 percent of respondents said they used generative AI for financial tasks at least monthly. Reported tasks included understanding financial products, seeking investment advice, and making comparisons. In a separate McKinsey survey fielded in July 2025 with 3,945 respondents, 49 percent said they used AI-based search; among those users, 53 percent said it helped guide brand discovery and purchase decisions. These figures describe different surveys and populations, not a single general adoption rate. McKinsey’s analysis discusses the findings.

Deloitte’s August 2026 article reports that, in a U.S. survey of almost 2,600 banking customers, 58 percent of surveyed generative-AI users had used these tools to research banking products. Search engines remained widely used: 84 percent of respondents had used them for the same purpose. AI is therefore an additional research path alongside familiar ones, rather than evidence that consumers have abandoned search or bank-owned channels. Deloitte’s survey report gives the U.S. context.

McKinsey also reports that large language models were more likely than traditional web searches to suggest digital-native lenders, fintechs, and credit unions in response to queries such as “best savings account.” The reviewed article does not provide a full platform-by-platform experimental protocol, so this is best understood as McKinsey’s reported finding—not a universal result for every bank, query, or AI tool. Read McKinsey’s article.

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Why visibility in AI answers matters

Banking discovery already involves more than a search-results page. Consumers may compare products on a bank website, use an aggregator, follow a referral, or encounter an offer through an intermediary or embedded-finance service. AI adds a conversational route: a customer might ask which account suits a particular need, how fees compare, or how to manage foreign-exchange costs when traveling. An answer may shape which providers the customer investigates, even if it does not lead directly to a bank-site visit.

That matters in a market where customers do not necessarily begin with their existing bank. McKinsey’s 2025 Global Banking Annual Review gives U.S. examples from its Banking Consumer Decision Journey Survey: 4 percent of new credit-card applicants chose their existing bank without exploring alternatives, down from 10 percent in 2018. For checking-account openings, the share attributed to the loyalty loop was 4 percent, compared with 25 percent in 2018. These are U.S.-specific figures from the report, not rates for every market or product. McKinsey’s annual review places them in the broader customer decision journey.

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McKinsey distinguishes traditional search optimization from generative engine optimization (GEO) this way: “Unlike SEO—which focuses on driving traffic—GEO is designed to ensure inclusion and correct framing inside the model’s answer.” In practice, a bank can care about both: visitors arriving through ranked links, and whether an AI answer includes the bank, describes its products correctly, and makes the limits of a recommendation clear. Inclusion alone does not prove that a customer saw, trusted, or acted on the answer.

What banks can do to improve the odds of accurate representation

There is no verified universal ranking formula for AI-generated banking answers. The following steps are practical ways to make information easier to find, interpret, and verify; they are not promises of a particular ranking, traffic increase, or sales outcome. McKinsey recommends clear decision-support content, reputable third-party references, and technically accessible pages. Deloitte similarly recommends structured product details and monitoring AI-generated shortlists and descriptions.

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  1. Make core product facts explicit. Publish current product names, rates, fees, eligibility rules, conditions, and limitations in clear language. Explain what can change and when a rate or offer applies. Put related facts together so a reader—or a system summarizing the page—does not have to infer a material term from scattered copy.
  2. Build pages around customer questions. Address practical comparison questions such as which account fits a need, what fees apply, who qualifies, and what trade-offs a customer should consider. Include meaningful caveats alongside benefits rather than presenting a product as universally suitable.
  3. Keep the information machine-readable and consistent. Use clear headings, structured page content, and consistent product naming across relevant pages. Where appropriate, provide machine-readable feature data. Make pages searchable, fast, and unambiguous so product details can be accessed and interpreted reliably. These are sound discoverability practices, not evidence that a particular technical change will secure AI citations.
  4. Earn credible references beyond the bank’s own site. Reputable third-party coverage and references can help establish context around a brand and its offerings. Prioritize accuracy and relevance over a volume of low-quality mentions; no source establishes a specific number or type of links that guarantees inclusion in AI answers.
  5. Audit answers across relevant queries and platforms. Ask realistic questions about products and customer situations, then record whether the bank appears, where it appears, what the answer says, and which sources it cites. Repeat the checks over time and compare like with like: platform, wording, geography, product, and audience all affect what an answer can show.

Visa Consulting & Analytics describes answer engine optimization (AEO), also called GEO, as work on content, data, and authority signals that helps answer engines understand, summarize, and cite offerings. It contrasts traditional SEO measures such as rankings and traffic with AI-answer visibility and machine-readable content. This is industry guidance from an advisory provider, not independent evidence that its recommended tactics produce a specified outcome. Visa also identifies privacy, security, and regulatory considerations for financial institutions. Visa’s AEO article discusses the approach.

How to measure discoverability without mistaking it for performance

A useful measurement program separates what an AI says from what customers do next. A single appearance in a generated response is not equivalent to a search ranking, a site visit, a qualified lead, or a completed account application. Track the answer itself and connect it to downstream outcomes only where measurement can support that connection.

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Measure What to record Why it matters
Presence and prominence Whether the bank appears for a defined question, and how prominently it is presented. Shows whether the brand enters the answer or shortlist for relevant queries.
Accuracy Product names, rates, fees, eligibility, conditions, and limitations as stated in the answer. Flags material errors that could mislead customers or create compliance concerns.
Usefulness Whether the response explains suitability, trade-offs, and caveats rather than giving a generic recommendation. Distinguishes a meaningful representation from a superficial brand mention.
Source quality Whether the answer cites current bank information and credible independent sources. Helps teams understand what evidence may be informing the answer.
Channel and audience The AI platform, exact query, geography, product, and customer segment tested. Makes comparisons interpretable and reduces the risk of treating one result as universal.
Trust and control Whether the experience makes sources and assumptions clear and keeps financial actions under customer control. Captures important qualities of the customer experience beyond mere visibility.

iQuanti announced an analysis of more than 30,000 conversational queries across four financial-services categories, three AI platforms, and more than 30 brands. Its announcement said high-traffic domains, fast interactivity metrics, and strong title-to-body relevance were consistent factors across platforms, while platform-specific signals varied. That is vendor-reported research, not an independently established or complete model for AI rankings. The iQuanti announcement describes the study’s scope and claims.

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Trust, privacy, and customer control are part of discoverability

Visibility is not the only concern when customers use AI for financial research. Deloitte’s 2026 U.S. survey found that 72 percent of surveyed banking customers were concerned about sharing financial information with generative AI tools, 64 percent worried about hidden bias, and 46 percent trusted the accuracy of banking recommendations from those tools. The same report says 79 percent trusted information on bank websites when researching products, compared with 49 percent for generative-AI tools. These results underline why banks should make authoritative information easy to verify and avoid treating AI exposure as a substitute for customer confidence. Deloitte’s findings are specific to its surveyed U.S. banking customers.

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For experiences that can take action—not just answer questions—Deloitte recommends customer-controlled AI that requires approval before acting. Banks should also account for privacy, security, and applicable regulatory obligations when making data and product information available to AI tools. Greater discoverability should not come at the cost of unclear data use or handing an agent financial authority the customer did not knowingly grant.

What the available results do—and do not—show

There are encouraging examples, but they should not be converted into promises. McKinsey reports that one financial institution introducing GEO optimization saw sixfold growth in monthly organic website traffic. The reviewed article does not identify the institution or provide enough detail to establish a general causal benchmark. It is a single reported case, not a typical expected result or proof that GEO alone caused the increase. McKinsey reports the case.

Visa’s AEO article also presents estimates for changes in search behavior and traffic, but its reviewed page does not clearly show the underlying study details alongside each headline figure. Those projections should not be presented as independently verified facts without consulting the cited footnotes and original studies. More broadly, consumer surveys establish reported behavior and attitudes; they do not establish a universal AI ranking mechanism or prove that a visibility tactic will deliver a commercial return.

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