LLMs will reinvent how many consumers and businesses interact with their bank and use their financial data. LLMs are data query, synthesis, and analysis engines more powerful than any previous generation of AI tools. Customers will perform banking activities from within the LLMs themselves. LLMs are also taking shape as platforms, not simply modes of interaction. From platforms come marketplaces, like the smartphone since 2008, and now LLMs like ChatGPT, Claude, and Gemini. FI-branded banking apps will appear, and proliferate, on these marketplaces.
The last revolution in banking channels — smartphones and their app stores — created an endpoint of the scale that nobody could have imagined. Smartphone adoption in 2025 was 91% for US adults, and mobile banking has reshaped consumers’ relationships with their FIs. Today, mobile dominates consumers’ banking channel use: In 2024, 71% of US consumers said that they used their smartphone to access their bank accounts online and 59% said it was their preferred channel. LLM adoption is scaling even faster, since LLMs can be accessed for free, and do not require a hardware purchase like a smartphone. In March 2025, 34% of US adults said they had used ChatGPT, fewer than three years after its public release. That’s nearly double 2023, does not include usage of other popular LLMs, and today the number is surely higher. While LLMs will not fully replace mobile banking apps, they will become a new place for users to conduct banking.
Queries to an LLM will replace certain clicks in digital banking or taps in an app. Chatbots, which were once hailed alongside smart devices as the future of digital banking, have never evolved far beyond a search tool. They rely on hundreds of predictable responses, and now have instantly been rendered obsolete. LLMs instead understand context, address complex inputs, conduct research, and synthesize responses based on its memory of the user’s interactions.
Legacy chatbot use reflects their failed potential. Consumers’ most common use cases for chatbots are to get technical support (60%) and inquire about existing accounts (53%). A small fraction (15%) said they aimed higher and used chatbots for advice on investments and personal finances. And the value customers place in bots is minimal. Consumers who valued bots more than human representatives for certain tasks said they did so for quick responses (78%) and simple queries (69%). LLMs have far more potential, and chatbots are being redesigned to incorporate them.
With LLM banking, account balances and transaction data will never languish in the vacuum that they do today. LLMs will tie together banking data and internet research to generate financial insights and advice in moments. Budgeting will bypass PFM widgets like pie charts and cash flow graphs to generate on-demand financial advice based on customers’ precise context. These tools will suggest the FI’s products and services tailored to the customer’s expressed and implied needs. LLMs will help answer questions and even open accounts. This lines up with US consumers’ emerging AI shopping habits: 37% in one study said they were somewhat or very interested in using generative AI to get inspiration for product purchases.
LLMs as a banking channel may have an even bigger impact for small businesses than for consumers. Small businesses are too scrappy for an “AI strategy,” and are finding use cases organically: They’re using LLMs to save time and improve workflows — like running pricing data, synthesizing accounting records, drafting customer service emails, writing light code, or producing podcasts. In digital banking, small businesses will query LLMs to generate cash flow estimates without needing to open a spreadsheet. That will complement and could replace other business tools and radically change today’s cash management experience.
Tellers, bankers, and customer service representatives will benefit immensely from LLMs on the administrative side of digital banking. The trend in workplace uptake of AI tools is clear: As of August, 27% of white collar workers used AI a few times a week or more, according to a study. The most popular use cases can be derived from general-purpose AI tools: US workers who used AI in their jobs at least once a year said that they used it primarily for consumer use cases, like to consolidate information or data (42%) or generate ideas (41%).
Workers’ adoption of AI in the workplace will grow as internal, specialized AI tools are developed. One bank reported that employees had replaced about half of their search queries with an internal AI tool. Any internal AI should in the end augment the staff’s abilities, make operations more efficient, and facilitate a more personalized customer experience. That includes
enabling staff to be better-informed about their customers. LLMs, for example, will be integrated into admin portals to give FI staff quick summaries of a customer’s recent banking activity, help them generate personalized offers, and to create product and service collateral.
LLM banking must always be bound by safety mechanisms and standards when data is being shared. One important development has been the Model Context Protocol (MCP). MCP is an open protocol that standardizes how AI applications connect to data sources. FIs implement MCP servers that define what banking data is accessible, what operations are permitted, and how data is formatted for LLM consumption.
Today’s guardrails prevent the LLM from making changes to banking data — Narmi’s MCP, for example, is configured to be read-only. Agentic features, like autonomously initiated transactions, will have their day, starting with the lowest-risk use cases. But in the context of banking there should always be a human in the loop — the customer — to review and approve actions before the LLM takes them.
LLM banking should not cause an existential crisis for FIs. Questions over who should control a channel and own the customer provoked soul-searching during the headiest days of banking as a service (BaaS) and the rise of neobanks that raised billions in venture capital. Open banking and customer data portability likewise stoked fears over customer ownership. Both fears, while well founded, were ultimately blown out of proportion. And the purpose of LLM banking is different.
LLMs are an interface and a platform — a channel — that will facilitate consumers’ and business’ access to financial services. In some cases, they will even be customers' primary access point for their bank, like mobile banking is today for a wide array of banking tasks. Like smartphones, LLMs will not be an alternative to the bank. They will be an extension of the bank. FIs that enable LLM banking will own the customer — because consumers and businesses will switch to the banks that offer the channels they most demand.
