Why Community Banks Are Getting Left Behind—and What We Can Learn from the Big Players

The Great AI Divide

Let’s not mince words: the American banking system is at a crossroads, and the road signs are written in code, not ink. As CEO of Frontier Foundry, a privacy-first AI firm, and a former FDIC innovation chief, I’ve watched the tectonic plates shift beneath our dual banking system. The result? A growing chasm between the AI “haves” and “have-nots”—a divide that threatens the very fabric of community banking in the United States.

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The Monopoly of the Core: A System Stuck in the Past

For decades, a handful of core banking vendors—think Fiserv, FIS, Jack Henry—have held the keys to the kingdom. Their platforms are the digital equivalent of a rotary phone: reliable, yes, but woefully out of sync with the needs of a modern, data-driven world. Most community banks, numbering in the thousands, are shackled to these legacy systems—systems that were built when the fax machine was still considered cutting-edge.

These core providers have little incentive to innovate. Why would they, when their market dominance is all but assured? If a community bank wants to integrate a new AI-powered fraud detection tool, the core vendor’s response is often a six-figure “integration fee” and a timeline that stretches into the next fiscal year. This is not just inconvenient—it’s existentially dangerous. In an era where cyber threats evolve by the hour and consumer expectations are shaped by Silicon Valley, being stuck in the technological slow lane is a recipe for irrelevance.

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AI: The New Great Divider

Meanwhile, the nation’s largest banks are writing a very different story. With deep pockets and armies of data scientists, they’re deploying AI across every corner of their operations. The numbers are staggering; early AI adopters in banking have seen a 15.8% increase in revenue, a 15.2% reduction in costs, and a 22.6% boost in efficiency. Fraud detection accuracy is up by as much as 20% at some institutions, while customer service automation has reached 90% efficiency.

But here’s the rub: these gains are not trickling down. The vast majority of community banks are still processing loans with workflows that haven’t changed since the Clinton administration. They’re drowning in compliance paperwork, unable to access their own data, and forced to watch from the sidelines as the big banks lap them—again and again.

Three (and a Half) Big Bank AI Success Stories

To illustrate just how wide the gap has become, let’s look at three specific examples of AI done right by the giants—and what community banks are missing out on.

1. JPMorgan Chase: AI-Powered Fraud Detection and Legal Automation

JPMorgan Chase has invested billions in AI, with a team of over 2,000 data scientists and machine learning experts. Their AI initiatives are as diverse as they are impactful. Take their COiN platform, which uses natural language processing to analyze legal documents—a process that once took 360,000 hours of lawyer time per year, now done in seconds. On the risk management side, JPMorgan’s AI-driven fraud detection systems continuously analyze transaction data in real time, flagging suspicious activity with a level of speed and accuracy that manual reviews could never match. The result? A 90% reduction in manual work, improved risk management, and a dramatic boost in operational efficiency.

2. Bank of America: Erica, the AI Assistant That Changed Everything

Bank of America’s “Erica” is the poster child for AI-powered customer service. Launched in 2018, Erica has handled over 2.5 billion interactions and now serves 20 million active users. This conversational AI agent doesn’t just answer FAQs; it helps customers manage their finances, alerts them to unusual account activity, and even guides them through complex transactions. The impact? A 19% spike in earnings, a quantum leap in customer satisfaction, and a model for how AI can transform both front- and back-office operations. Internally, BofA has also deployed AI to streamline IT support, HR, and compliance—freeing up human staff for higher-value work.

3. Wells Fargo: “Fargo” and the AI Pipeline

Wells Fargo has gone all-in on AI, deploying 191 distinct AI projects across the bank as of 2025^[13]. Their flagship initiative is “Fargo,” a virtual assistant built on Google’s large language models, integrated directly into the mobile banking app. Fargo responds to customer queries via text or voice, handling everything from transaction lookups to personalized financial advice. But Wells Fargo hasn’t stopped at customer service. Their AI-powered fraud detection system analyzes vast troves of transaction data in real time, flagging anomalies and reducing fraud incidents significantly. The bank’s holistic AI pipeline—spanning customer service, compliance, risk, and operations—has delivered massive efficiency gains, cost reductions, and improved customer trust.

4. BNY Mellon: Contract Management and Federated Learning

Let’s not forget BNY Mellon, which has used AI to revolutionize contract management. By implementing AI-driven document analysis, they’ve cut contract processing times dramatically, smoothing onboarding and compliance workflows. Their use of federated learning boosted fraud detection accuracy by 20%—a leap that’s simply out of reach for banks still stuck on legacy cores.

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Why Community Banks Can’t Just “Copy and Paste”

It’s tempting to say, “If JPMorgan can do it, why can’t my local credit union?” The answer is simple: the tech stack. Big banks have the resources, talent, and data infrastructure to build and deploy custom AI solutions. Community banks, by contrast, are often locked out of their own data, lack the budget for in-house AI teams, and are at the mercy of core vendors who treat innovation as an upcharge, not a baseline.

The result? Community banks are forced to choose between stagnation and extinction. They can’t automate compliance, can’t personalize customer service, and can’t compete on speed or security. In a world where fraudsters use AI to probe for weaknesses, this isn’t just a disadvantage—it’s a liability.

The Case for “Right-Sized” AI

So, what’s the solution? We need AI that’s built for the realities of community banking—not just stripped-down versions of big bank tools, but purpose-built, privacy-focused, and easy to integrate.

Here’s what “right-sized” AI looks like:

• Open Data Access: Regulators must require core vendors to provide open, affordable APIs so banks can actually use their own data.

• Plug-and-Play Solutions: Pre-trained AI models for fraud detection, loan underwriting, and compliance that don’t require an army of data scientists.

• Collaborative Networks: Community banks should pool resources to share AI tools and best practices, reducing costs and increasing collective bargaining power.

• Regulatory Sandboxes: Safe environments where banks can test new AI tools without fear of regulatory reprisal if something goes wrong.

Imagine a world where your local bank can deploy an AI-powered loan approval system that cuts processing time from days to minutes, or a fraud detection tool that learns from every transaction in real time. This isn’t science fiction—it’s what the big banks are doing right now.

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What’s at Stake: Survival, Not Just Success

The 2023 banking turmoil showed us what happens when risk outpaces technology. Banks that embraced AI weathered the storm; those that didn’t, didn’t. As we head deeper into economic uncertainty, the stakes couldn’t be higher. AI isn’t just a path to growth—it’s the only way for community banks to remain safe, sound, and relevant.

To the core providers: Stop holding community banks hostage. To the regulators: Tear down the walled gardens. And to every community banker reading this:

Demand better. The tools exist. The money exists. The will? That’s up to us.

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A Call to Arms (and Algorithms)

The next chapter of American banking will be written in code. Will community banks be co-authors, or just footnotes? The answer depends on whether we can democratize AI—not just for the giants, but for every bank that serves a real community, not just a balance sheet.

Let’s build a future where every bank, no matter its size, can harness the power of AI to serve its customers, protect its assets, and thrive in the digital age.

The AI revolution in banking is here. The only question is: who gets to join it?

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