Key Takeaways from This Blog:
AI is reshaping how consumers discover financial institutions, giving community banks and credit unions opportunities to compete with larger institutions based on relevance rather than size.
Clear, accurate, and accessible product information is critical to AI visibility, as institutions must ensure their rates, terms, eligibility, and competitive advantages are easily understood by AI platforms.
As AI agents make comparing financial products and moving money easier, banks and credit unions must prioritize visibility, trust, and competitive offerings to protect and grow customer relationships.
For decades, size bought visibility in banking. Large institutions could afford more branches, more advertising, and a greater share of the search results a consumer saw. Community banks and credit unions often competed with stronger service, local knowledge, or a better product. Getting noticed was the harder part.
Artificial intelligence is beginning to change the path to consideration. When someone asks an AI tool where to keep $50,000, who offers a competitive home equity loan, or which institution fits a particular need, the system does not start with branch count. It starts with the question and the information it can find to answer it.
That distinction matters because AI tools tend to offer a short list, not a page of results. A consumer who once compared ten institutions may receive three names and begin there. The institutions that do not appear may never enter the comparison.
New Media Advisors analyzed more than 14,000 AI-generated answers across 129 U.S. markets in its 2026 Banking AI Visibility Report. The study found that recommendations changed materially with the question being asked. In consumer categories, credit unions received about 61 percent of recommendations after the results were weighted for platform usage. Their share exceeded 70 percent in some home equity and personal-loan questions. In commercial banking, regional and community banks performed strongly in areas such as acquisition financing and commercial relationships.
The study is directional, not a forecast of account growth. AI results can vary by platform, location, and the wording of a prompt. Even so, the finding is worth attention: a specific local institution with a product that fits the question can surface beside, or ahead of, a national brand.
Consumers are already using these tools for financial research. McKinsey reported that 23 percent of surveyed consumers use generative AI for financial tasks at least monthly. The most common uses included understanding financial products and comparing options. Deloitte found that 58 percent of surveyed generative-AI users had used the tools to research banking products. The figure was 65 percent for Gen Z respondents.
A community bank or credit union does not need to outspend a national bank to be relevant to a well-formed question. It does need to be easy to understand. That means product terms, pricing, eligibility, and local availability should be current, consistent, and clear across the website and other places where consumers find information.
Consider a credit union with a strong first-time-homebuyer program. If eligibility is on one page, rates are on another, fees are in a difficult-to-read PDF, and membership information takes several clicks to find, the offer is hard for both people and systems to evaluate. A consumer may work through the details. An AI tool may simply identify another option with clearer information.
McKinsey recommends plain-language product explanations, transparent pricing, and explicit eligibility information for institutions seeking to improve visibility in AI responses. These are not merely website improvements. They are part of how an institution makes a case for inclusion in a consumer's initial set of choices.
Financial institutions cannot control every source an AI platform consults. New Media Advisors found that roughly 62 percent of citations used in national banking AI answers came from sources the institution did not own. Comparison sites, financial publishers, reviews, and local coverage can therefore influence how an institution is described and whether it is recommended.
That does not reduce the importance of an institution's own website. It makes accuracy outside the website more important. An outdated third-party product description, unclear membership explanation, or inconsistent fee disclosure can shape an answer before a consumer reaches the institution's site.
Management teams should test a practical set of customer questions across the major AI platforms. The goal is not to chase one score. It is to learn whether the institution appears, whether the product information is correct, which sources are being used, and where the description of the institution is incomplete or wrong.
AI-supported research is already present in consumer lending. PwC's 2026 Consumer Lending Radar found that nearly one-third of borrowers use AI tools to research loans. Half said AI had helped them make a borrowing decision, and 67 percent expected AI to inform their next borrowing decision.
A mortgage or home equity borrower can provide income, a target payment, a location, a down payment, and a home price, then ask which lenders to contact. The institutions included in that answer have an opportunity to compete. The others may not.
This rewards clear products and honest differentiation. A product can sound distinctive in marketing copy while appearing interchangeable when rates, fees, terms, and eligibility are compared. Clear information gives institutions with a real advantage the best chance to show it.
Consumers still place more trust in information from their financial institution than in AI-generated recommendations. Deloitte found that 79 percent of surveyed bank customers trusted information on their bank's own website, compared with 67 percent for traditional search engines and 49 percent for generative AI tools. PwC likewise found that three out of four consumers still want a person involved in loan approvals and closings, and 85 percent say they trust a lender more when it discloses its use of AI upfront.
That trust matters most after an institution enters the conversation. If an AI tool never identifies the institution as a credible option, the consumer has no opportunity to weigh its reputation, local presence, or service model. Visibility and trust now work together.
The next development is more consequential: AI agents that can monitor, compare, and eventually carry out limited tasks with customer permission. EY's 2026 research, based on more than 3,500 consumers and 50,000 simulated agentic-AI journeys, suggests that agents will increasingly sit between consumers and financial providers. EY also found that 70 percent of younger consumers would use AI agents as personal assistants.
The implications are particularly clear for deposits. A customer may leave a sizeable balance in a low-yield account because finding alternatives and moving funds takes time. An agent could identify a higher rate, compare the terms, calculate the difference, and present the choice. The customer still decides, but the effort required to find the alternative declines.
McKinsey estimates that if AI-enabled optimization moved 5 to 10 percent of checking balances into higher-yield alternatives, banking profits from deposits could decline by 20 percent or more. This is a scenario, not a prediction. Its value is in showing how quickly the economics can change when comparison becomes easier and more frequent.
This is not a reason for banks or credit unions to panic. It is a reason to examine how the institution is found and understood. Start with the questions customers are likely to ask: Who offers the best checking option for my needs? Which local credit union has a competitive auto loan? Where should I keep a large deposit balance? Then review the answers across the AI platforms your audience is most likely to use.
The response should involve more than marketing. Product leaders should confirm that the institution's terms and tradeoffs can be understood quickly. Digital teams should keep information structured and current. Compliance teams should consider how regulated products are described on third-party sites. Marketing teams should monitor AI visibility alongside search, reviews, traffic, and conversion.
The central question is straightforward: based on the information an AI tool can find, would a consumer understand why your institution is a good fit? Size still matters in banking. It can provide resources, reach, and resilience. In AI-driven discovery, however, a clear, relevant, and credible answer may be what earns the institution a place on the shortlist.
New Media Advisors, Banking AI Visibility Report, August 2026
McKinsey, Agentic AI in retail banking: The customer effect, May 2026
Deloitte, How much do bank customers trust gen AI?, September 2026
PwC, Consumer Lending Radar Survey 2026, June 2026
EY, Why agentic AI in banking will make execution the measure of success, June 2026