Financial Firms Are Spending on AI Search Before They Know What It Delivers

Banks and fintech companies are putting money into visibility on AI search services, but a new industry survey suggests that marketing teams are struggling to connect that spending to customer acquisition.

Financial-services companies are adding AI search to their marketing budgets before they have established how much business the channel generates. In a survey published in October 2026, 32.5 percent of respondents identified AI search visibility as an area of new investment, while only 27.5 percent reported positive early results. Another 22.5 percent had yet to measure the results.

The research, conducted by the Fin.Tech Marketing Community and digital agency Accuracast, surveyed 105 senior marketing professionals from banks, fintech companies, asset managers and other financial businesses internationally. Their responses suggest that spending on AI search is growing faster than companies’ ability to measure its commercial return.

Banks and brokers have spent years attracting customers through Google, paid advertisements and financial content. Those channels provide data on website visits, enquiries and completed applications. AI assistants are changing how people find financial information, with users receiving summaries and recommendations that sometimes remove the need to visit a provider’s website. A company can now appear in a prospective customer’s research without receiving a website visit that its marketing department can identify.

What Happens When Search Produces an Answer

Traditional search directs users towards websites where they find information about financial products and services. AI search services assemble answers from several sources, sometimes identifying providers and quoting information about their products.

Someone comparing online brokers may receive a summary of their charges and services without opening the individual websites. The names included in that response depend partly on the information available to the AI system and how it selects sources.

Financial businesses have begun investing in answer engine optimisation, or AEO, to improve their presence in those results. The work includes updating product descriptions, organising website content and checking that information about the business remains consistent across different sources.

Much of it resembles established search-engine optimisation. Banks already need accurate information about products, charges and eligibility conditions, supported by websites that search engines can read.

AI search introduces another consideration: the information appears in a response written by a third-party system, which may combine details from several sources or leave out qualifications that appear on the original pages.

A financial company can check what its own website says, but it has less control over how an AI assistant describes that information to a user.

Marketing teams therefore need to examine the answers customers receive as well as the position of their websites in conventional search results.

Measuring Interest Before a Customer Arrives

Counting appearances in AI-generated answers is relatively straightforward when a company tests a defined set of questions. Establishing what those appearances contribute to revenue is considerably harder. A broker mentioned in an AI response may reach someone comparing accounts for the first time. Another user may already be a customer, while a third is researching the market without intending to open an account.

Website analytics can identify some visitors who follow links from AI services. Others remember the company name and return later through a conventional search engine or by entering its address directly. The problem is familiar to financial marketers, although AI search adds another stage between the first contact and the eventual purchase. A person choosing a savings product may compare charges and access conditions over several days. Institutional buyers selecting financial software can spend months examining suppliers, consulting colleagues and completing technical reviews. By the time a contract is signed, the original source of interest may be difficult to identify. 

Marketing teams have developed methods for attributing sales to different channels, including customer surveys and data collected during enquiries. Those methods remain available, but they do not establish that every mention in an AI answer has commercial value. The October survey found that many respondents were still assessing their early AI search investments. The research records marketing priorities and respondents’ experiences rather than audited returns from the channel.

More Content, Uncertain Returns

AI is already established inside financial marketing departments. Three-quarters of survey respondents said they used the technology regularly, with much of the benefit coming from faster content production and other routine work. The research also found that 44 percent operated without a formal AI integration strategy, while 34 percent relied on informal performance indicators.

Marketing teams can now prepare several versions of a campaign, analyse material or produce initial drafts with less manual effort. Publishing more content, however, does not establish whether a financial business is attracting customers who eventually purchase its products.

Account registrations illustrate the problem. A digital broker may generate substantial interest through an advertising campaign but receive relatively few deposits. A fintech company selling enterprise software needs qualified enquiries that progress through technical assessments and procurement. Both businesses have to connect the initial marketing contact to activity recorded elsewhere. The survey found that 70 percent of respondents considered their digital marketing performance to be meeting or exceeding expectations. Yet fewer than one in six strongly believed that marketing tools alone would provide a lasting competitive advantage.

Spending on software and producing more material are relatively easy to measure. Tracking how the work contributes to sales requires closer cooperation between marketing, customer service and commercial teams.

Financial Products Leave Little Room for Inaccurate Information

The information used by AI search systems also creates problems when it is incomplete or outdated. A bank may change the interest rate or conditions attached to a savings product. A broker may revise its commission schedule or introduce new charges. If older information remains on a website or in third-party publications, an AI assistant may describe a product using terms that no longer apply.

Financial marketing departments already have procedures for reviewing product information and advertising claims. AI-generated answers introduce another place where customers encounter those claims, even though the financial institution does not write the response.

Companies can reduce the risk by maintaining accurate and consistent information across their own websites, product documents and public communications. They cannot guarantee that external AI systems will reproduce it correctly.

Similar concerns affect the use of AI within marketing teams. Employees dealing with customer information or internal business records need to understand which data enter the applications they use.

Half of the survey respondents included risks to audience trust in their AI governance policies. The study reported no negative audience reactions to AI use among respondents, although that finding does not establish how customers view the technology more generally.

Marketing departments still have to check what AI tools produce, especially where an error concerns charges, investment risks or the terms of a regulated financial service.

The Search Budget Has Another Competitor

Banks and fintech businesses already divide marketing expenditure between paid search, specialist publications, social platforms, partnerships and their own content.

AI search is being added to those budgets while companies continue evaluating the performance of established channels.

The survey found that 60 percent of respondents expected closer alignment between sales and marketing to generate strong returns over the next three to five years. Only 25 percent reported a strong impact from that alignment at present.

Financial companies have several reasons to improve the connection. Prospective customers may first encounter a provider through an article, register for information later and complete an application through a separate process. Different departments record those activities in different systems.

Linking them requires agreement about which customers a campaign is intended to attract and what counts as a completed commercial result.

A fintech company selling software to banks may judge its marketing by qualified enquiries and contracts. A brokerage business might examine funded accounts and customer assets. Counting website traffic or AI mentions alone leaves both companies with an incomplete view of their spending.

AI search monitoring is also affected by how providers test their visibility. Results depend on the questions submitted, the systems examined and the time of testing. A brand appearing in one set of answers may not receive the same exposure when users ask different questions.

The market for measuring AI visibility is developing while financial firms are still deciding how much to invest in it.

The survey gives no evidence that AI search is already outperforming established acquisition channels. It shows financial marketers allocating money to a form of search they expect customers to use, with many still working out how to trace the resulting business.

Exporis approaches AI search as part of financial customer acquisition, alongside the search, content and advertising channels that banks, brokers and fintech providers already use. A report showing how frequently a company appears in AI-generated answers answers one question. The commercial assessment starts with the people making those searches, the products they are researching and whether their enquiries eventually turn into customers. A brokerage business, for example, needs to distinguish general interest in investing from people comparing accounts they intend to fund. A fintech software provider has a smaller prospective audience and a longer sales process, making the quality of individual enquiries more informative than the volume of mentions. Exporis’s work in digital marketing, financial content and product positioning brings those measurements together. The priority is to connect search visibility with the customer activity that determines where marketing budgets are spent.