Partnering with William Mills Agency, Atlanta, we asked several leaders in the fintech world for their community bank industry predictions for the second half of 2026. We are running their responses in two parts. The first touches on AI-centered predictions, while the second will feature deposits, lending and security insights.
Two trends are opening up AI moving from co-pilot to co-work, and regulatory relief shifting toward a narrowing of Section 1071 of Dodd-Frank. With the new reporting dates for all but the largest institutions now in 2027, regulatory relief appears increasingly likely. As regulatory burdens ease, the outsized benefit will flow to smaller institutions, where reporting comes at a higher relative compliance cost. On the AI front, we collectively know how to use Copilot, Claude, or ChatGPT. The real trend is how we are just now beginning to use agentic interactions to reshape the depth and breadth of our individual and team contributions. 2026 is the year AI earns a permanent place in our operational workflows.
~ Matthew Wood, senior director, fintech AI, Tavant
At midyear, banks are moving from AI exploration to practical integration. The committee has been formed, priority use cases are taking shape, and the next step is determining how AI can improve operations, decision-making, customer service and risk management without creating new governance concerns. At the same time, cybersecurity strategies must become more proactive. Ransomware is no longer only a test of backup and recovery capabilities; extortion tactics increasingly target sensitive data, public trust and institutional reputation. Banks must strengthen detection, exposure management, data governance and incident readiness before an attack creates consequences that operational resilience alone cannot fully resolve.
~ Patrick Whelan, VP of sales, Fortuna Cysec
Community banks are converging on one theme: growth through deposits and small business. Cornerstone’s 2026 research puts deposit gathering atop the worry list, yet opening a business account still takes hours and only a handful of large banks offer a digital business application. The opportunity is enormous — big banks hold roughly two-thirds of small-business relationships that community banks were built to serve. Expect the winners to pair same-day digital business onboarding with AI that deepens relationships, and to favor fast, integration-light technology partners over multi-year builds.
~ Philip Paul, CEO, Cotribute
It’s no secret to community banks that a staffing change can be devastating. There’s often one person who holds all the institutional knowledge or owns a significant portion of the relationships. In commercial lending, for example, losing someone like this can be incredibly costly. However, we’re seeing how AI can help reduce that impact. When it’s able to capture that tribal knowledge that is so essential to the bank’s success, it reduces the impact of losing a credit analyst. It can also have the added benefit of extending banks’ existing teams, eliminating the need to recruit and train new resources.
~ David Eads, co-founder and CEO, Vine
As generative AI accelerates fraud sophistication, community banks will shift from document review alone to broader, multi-layered verification strategies. AI-generated financial packages and resurrected ‘zombie’ businesses will continue increasing in both volume and quality, placing greater pressure on traditional underwriting processes. We expect institutions to focus more heavily on cross-checking data sources, strengthening fraud intelligence sharing and adopting technology that identifies behavioral patterns across applications. The institutions that balance speed with stronger verification processes will be best positioned to protect growth while managing risk.
~ Patrick Lord, senior project manager, Rapid Finance
AI adoption in banking is no longer optional. However, as banks move from evaluating AI to actual deployment, a major concern is how to innovate safely without slowing down and without triggering regulatory concerns. Banking is full of both complexity and nuance; policies, procedures, exceptions, regulations and judgment calls that are not fully captured or always contemplated in the generic datasets that today’s large language models leverage. When AI is designed with banking-specific ontology, trained by bankers, regulators and financial lawyers, and grounded in real operational workflows, it can scale what institutions’ best operators already do.
~ Arjun Sirrah, founder and CEO, Titan
Community financial institutions have always competed on relationships. That advantage is about to get a significant upgrade. The midyear reality is this: CFIs that have been cautious about AI adoption are no longer behind. They’re positioned. While larger institutions spent the first half of 2026 running disconnected pilots across too many tools, CFIs can move decisively with a clear framework: Human, Robot, Agent. People handle what requires judgment and trust. Robots eliminate the repetitive. Agents reason across systems in real time, turning data into action without adding headcount. The institutions that operationalize this model in H2 will define the competitive standard heading into 2027.
~ Todd Michaud, president and CEO, HuLoop
The next evolution of data isn’t better reporting or better dashboards. It’s better decision-making. For years, banks have used data to understand what happened. Increasingly, they’ll use it to determine what should happen next. As AI matures, data will move beyond retrospective analysis to enable real-time operational decisions, from ATM cash availability and fraud detection to self-service performance and customer experience. The institutions that gain the greatest advantage won’t be those with the most data, but those that can connect it across systems, translate it into action and make better decisions in real time.
~ Maha Sivara, chief data officer, NCR Atleos
Heading into the second half of the year, banks are navigating a more complex deposit environment than anticipated. Although the Fed held rates steady in June, the competition for deposits continues, with CD Valet data showing 71 percent of CD rate changes were increases over the past 30 days. At the same time, institutions are facing new challenges from digital-first competitors, emerging stablecoin adoption, and AI-driven financial tools. In this environment, competitive rates are no longer sufficient on their own. The strongest performers will be those with sharper product design, strong digital discoverability, and the agility to respond to emerging technologies.
~ Mary Grace Roske, head of marketing and communications, CD Valet
As AI enables fraudsters to operate with greater speed and scale, community banks will focus on more effective and efficient fraud prevention. Successful fraud prevention won’t be defined by the volume of alerts generated, but by their ability to identify and act on the highest-risk items. This year, banks will prioritize AI-powered solutions that reduce false positives and provide explainable insights that help investigators make faster, more informed decisions, while maintaining confidence in AI-driven outcomes. Since fraudsters are using AI to operate smarter, community banks will rely on AI to stay ahead of fraud tactics, not simply keep pace with them.
~ Emiliano Giacchetti, CEO, ParaScript
Halfway through 2026, the AI story for community banks has flipped. The problem isn’t too little data. It’s that fraudsters now use AI faster than most banks can make sense of what they have. The biggest banks are answering with teams of data scientists. Community banks can’t — and shouldn’t — have to. A new kind of AI, built to evaluate risk rather than generate content, reads how a customer’s history, devices and behavior fit together, catching what any single check misses. The midyear trend: evaluative AI lets community banks catch sophisticated fraud as well as the megabanks, without their resources.
~ Beth Fiveson, VP, marketing, Pipl
Fraudsters are continuing to evolve their tactics, which is why so many banks are now turning to AI to strengthen their detection efforts. AI is now widely used to identify unusual activity and potential fraud in real time. Models can analyze transaction patterns, detect anomalies, and adjust as new data becomes available in a more flexible way than traditional rule-based systems. At the same time, institutions remain responsible for ensuring monitoring is governed, properly calibrated and manages the right risks.
~ Todd Robertson, senior vice president, ARGO
