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Artificial intelligence is rapidly becoming an indispensable financial tool for small businesses, enabling them to forecast cash flow, track payments, and generate vital reports in a fraction of the time. This widespread adoption, however, presents a significant challenge for community banks and credit unions, as much of this activity occurs outside their traditional platforms and established customer relationships.
Market research underscores this swift transition. Intuit’s latest Small Business Insights survey reveals over 70% of small businesses are regularly utilizing AI tools. A separate Small Business Expo survey echoes these usage levels, noting that 79% of AI users report tangible reductions in costs or improvements in efficiency. Furthermore, a recent Forbes article highlighted how AI empowers small businesses to identify new funding avenues and helps alternative lenders expedite credit assessments, paving the way for fintechs and others to bypass traditional banks.
Yet, the transformative power of AI is equally accessible to the financial institutions that have historically served these small business clients. For community banks and credit unions, AI’s competitive threat simultaneously offers a strategic opportunity.
Financial institutions that proactively integrate AI-driven financial insights into their digital offerings stand to remain central to their customers’ financial operations. Those that fail to adapt, however, risk seeing their small business clients migrate to more technologically advanced alternatives.
Why Small Businesses Embrace AI
To grasp the implications for community banks and credit unions, it’s crucial to understand what small businesses value most about AI. For now, its primary appeal lies in providing much-needed relief rather than complete business transformation.
Small business finance teams are typically lean, often comprising a single individual responsible for bookkeeping, analysis, reporting, forecasting, and vendor management. AI tools free these individuals to focus on higher-value activities and can even avert the need to hire additional full- or part-time staff.
Common AI use cases include analyzing transaction histories, forecasting cash flow, categorizing vendor spending, tracking supplier cost fluctuations, and flagging upcoming payments. Increasingly, business owners are using AI to confirm cleared payments or received deposits, often bypassing their traditional banking portals entirely. AI is also instrumental in generating templated summaries for month-end closes and crafting reports for shareholders or boards.
Previously, such tasks consumed hours of manual effort and relied almost exclusively on basic data extracted from business banking accounts. To access this data, some users simply export CSV files and feed them into AI tools. Others leverage built-in AI features within platforms like QuickBooks. More sophisticated businesses are employing Model Context Protocol (MCP) servers, granting large language models (LLMs) direct access to read their account data securely.
Reasserting Primacy in an AI-Driven Landscape
The proliferation of readily available AI-powered financial analysis poses a fundamental challenge for financial institutions: it positions the bank as a passive data source rather than the hub of a customer’s financial activities.
This dynamic has significant implications for bank primacy. Historically, the primary financial institution was where customers logged in most frequently to review balances, monitor activity, and manage finances. As AI tools grow more capable, customers may increasingly interact with external AI assistants instead of their bank’s digital channels, potentially diminishing loyalty.
“Community banks and credit unions are now asking if this shift means they will have less traffic to their mobile app or digital banking platforms, and the answer is yes,” stated Yaro Melnyk, Group Technical Product Manager at Narmi, in an interview with The Financial Brand. “In the same way that banks had to digitize and then create mobile apps, and there was a process for doing that, they’re going to have to adjust to this new reality where LLMs are a new channel to engage with customers.”
However, community banks and credit unions can maintain central roles in customer relationships by embedding purpose-built AI tools that empower businesses to better understand and manage their finances.
Early Adopters Lead the Way
Grasshopper Bank exemplifies this proactive approach. In collaboration with Narmi, Grasshopper became the first U.S. bank to deploy an MCP server, revolutionizing how its startup and SMB clients access, comprehend, and act on their financial data. An MCP server creates a secure bridge, allowing AI assistants like ChatGPT or Claude to query live data directly using plain language, eliminating the need for users to export files or switch platforms.
Fintech firm EnFi, an AI-native lending platform that automates complex lending workflows for regional and community banks, is a Grasshopper client benefiting from this innovation. EnFi interacts with Grasshopper’s MCP server to securely share its financial information and receive actionable, AI-driven insights about its own business in real time.
EnFi’s Chief Financial Officer, Michelle Hipwood, who manages a “team of one” finance department, expressed her enthusiasm: “As CFO of an AI-first company, I find it incredibly exciting to leverage the same type of technology we provide our customers to transform our own financial operations.” Initial results show time savings of two to three hours or more per week, with the company actively exploring additional use cases. EnFi’s Chief Revenue Officer, Chris Aronis, likens AI to a “junior analyst” – requiring oversight but improving with feedback.
Strengthening Your Value Proposition
Community financial institutions don’t need to directly compete with massive AI platforms to remain relevant. Instead, their focus should be on ensuring they remain integral to the financial workflows small businesses rely on, including LLMs and other AI tools. Several practical strategies can help:
1. Integrate Financial Data Across Systems
AI’s utility significantly increases when it can analyze information from multiple sources. For many small businesses, the ability to connect data from various banks and financial partners with their daily AI tools is becoming a deciding factor in choosing a financial provider. “The more data it has access to, the more powerful it is,” Melnyk explained. “When financial institutions support integrations with ERPs and other financial tools, they create a richer environment for analysis.”
2. Prioritize Strong Security Controls and Governance
Security concerns remain a major hurdle for AI adoption. Financial institutions should lead by offering transparent controls. An initial implementation might offer read-only access, allowing AI to analyze financial data without initiating transfers or altering account settings. Clear governance frameworks can assure customers that new tools enhance insight without compromising security.
3. Help Customers Interact With Financial Data Naturally
Early AI interfaces often relied on open-ended chat prompts, which can be daunting for many users. However, emerging designs are beginning to resemble traditional software with familiar buttons, dashboards, and visual controls. Financial institutions that combine AI capabilities with existing, user-friendly digital banking interfaces may find it easier to drive customer adoption.
4. Start With Use Cases Customers Already Trust
AI already plays a critical role in areas like fraud detection and transaction monitoring, where customers generally accept automated systems that protect their accounts. Expanding from these trusted applications into analytical tools can help institutions gradually build customer comfort with broader AI capabilities. Financial institutions, Melnyk noted, “should be leading their clients to see that they shouldn’t be afraid of these tools, that they’re already helping you with fraud and they can help you with more.”
5. Encourage Internal Experimentation
Often, the biggest obstacle to adopting AI tools within financial institutions is internal hesitation. Some teams struggle to gain approval even for testing external AI platforms. Creating secure testing environments and pilot programs allows institutions to evaluate AI capabilities responsibly without exposing the organization to undue risk.
Source: thefinancialbrand.com