The banking industry is one of the most heavily regulated sectors – and while many bankers view these regulations as excessive, they stem from the critical need to protect sensitive client and institutional data. Clients trust financial institutions with their most personal financial information, and in turn banks and credit unions have a responsibility to keep that data safe. Institutions also hold proprietary information that must be kept secure at all times, too – such as operational procedures, internal strategies, client portfolios, and more.
But as artificial intelligence takes hold across every industry, financial institutions are caught between a rock and a hard place: should they play it safe, the way they’ve always done, and keep their data under lock and key? Or do they embrace this new technology and grant it full access?
Right now, most banks experimenting with AI are opting for the former strategy. They’re checking the AI box by testing out various tools, but are hesitant to allow that technology a full peek behind the curtain. If AI implementation is simply a check-the-box exercise for your bank, you’re already at a disadvantage.
Safe Doesn’t Have to Mean Scared
There’s a growing gap between banks that are experimenting with AI and those that are strategically enabling it. Larger institutions are pouring time and resources into developing their own tools, but even they are running into limits.
Why? Because building foundational AI is complicated even for the largest banks. To try and circumvent this pain, big banks are starting with small use cases as pilots, losing sight of the foundational element they sought to solve initially. Which makes it unsurprising to see some of the most ambitious pilots stalling. This isn’t because of a lack of vision or even resources. AI can’t be added onto siloed processes and disconnected data. It needs to be approached as an institutional capability, not a feature.
That’s where small and mid-sized banks have a golden opportunity – to move deliberately but buy with scale in mind.The race isn’t about speed to pilot – it’s about designing today’s investments to serve tomorrow’s transformation. The banks that succeed won’t rush, they will take their time to lay a strategic, scalable foundation.
Most banks today are approaching AI scared – scared of falling behind, scared of sharing their data, scared of a lack of measurable ROI. The good news? There is a way to be both safe and smart in banking’s adoption of AI. But they have to start piecing together the building blocks for long-term success now.
Enabling Agents with Access, Safely
While the industry is abuzz with talks of agents, the reality is that this tech is often siloed to individual employee success instead of serving as bank-wide efficiency and intelligence gains. In this way, banks are playing it safe and testing the tech while losing out on the promises of agentic across all of their functions.
Instead of using agents essentially as a search engine, this tech is able to complete tasks autonomously with minimal human input. In a banking environment, an employee could leverage an agentic solution to:
- Analyze customers’ transaction history to identify potential fraud
- Alert the proper stakeholders when fraud is expected
- Create the necessary documentation when fraud is identified
Let’s take one example a layer deeper: fraud detection. To fulfill its responsibilities accurately and effectively, a fraud detection agent would need unfettered access to:
- Customer transaction history
- Account activity patterns
- Internal risk scores
- Escalation protocols
- Case management tools
- Audit trail
- Internal notes
- …and so much more
In this scenario, you’ve invested in an AI solution that gives you the ability to implement a fraud detection agent. But you’ve neglected to give it access to one or more of the information sources listed above due to data privacy fears.
Agents without access are essentially blind in one eye. They don’t have the full context they need to deliver accurate results. The solutions you invest in will only work as well as the data you feed them; if you can’t trust an AI solution by giving it access to your data, why invest at all?
Building Trust with Single-Tenant Implementations
General-purpose LLMs like Microsoft CoPilot are well-suited for a variety of tasks, but financial institutions require more purpose-built solutions.
Tools that feature single-tenant implementation should be at the center of every bank’s AI strategy. While many AI solutions source information from anywhere on the Internet, single-tenant implementation ensures that the platform only references the data you give it access to.
It lives within your existing infrastructure and leverages institution- and industry-specific knowledge alone, rather than pulling information from unverified sources. This means the environment in which the AI solution operates is custom to each institution and completely inaccessible to outsiders, removing the risk of data leakage and reducing the chance of hallucinations.
With single-tenant implementation, banks no longer have to worry about limiting what their AI agents can and cannot access. This feature allows AI solutions to source information from the bank’s intelligence core, A.K.A. the complex knowledge map of an institution’s proprietary data, existing systems, and technologies. The right tools will understand not just the data itself, but the relationships between each and every data point: if signal X necessitates action Y, the agent can trigger a response from platform Z.
Don’t get left behind
For banks lagging behind on AI adoption, or those who have begun working with AI but are still hesitant to trust it fully, this kind of efficiency is hard to beat. Not all AI solutions are created equal – banks certainly shouldn’t go out giving every platform full access to their data – but strategic investments in solutions purpose-built for the industry are necessary to remain competitive.
Don’t hold your bank back from success in an AI-first world. Look for platforms that understand and support your data privacy needs, and get on board. Otherwise, you’ll be left behind. It’s not about surrendering to AI – it’s about finding a solution to control it. Stop hedging, and start honoring what AI needs to work: full data access, full integration, and full trust. Anything less isn’t strategy; it’s self-sabotage.
