Relationship Banking Moves Back to Center as AI Reduces Admin Burden in Financial Services

Banking's AI wave has moved past experimentation. The industry is firmly in the "fast follower" phase, with proof points in productivity and revenue driving adoption and major institutions even using the technology to win clients during market turmoil. While customer-facing AI in banking apps gets the headlines, the majority of gains are coming from within the operational core, where AI is systematically erasing the administrative drag to strengthen risk management and free up bankers for higher value work.

Joseph Boragina is a Regional Vice President for Enterprise Sales at nCino, Inc., responsible for National Account Management across the North American Banking Industry. Before joining nCino, he spent five years at BMO Financial Group as a Commercial Relationship Manager and Credit Analyst, giving him firsthand experience of the operational friction he now works to solve. He is a two-time nCino MVP and believes AI's highest value in banking is giving employees the time and insight to build the relationships that matter most.

"AI isn’t just about replacing bankers. It’s about giving them time back to focus on meaningful conversations and relationships that really matter." Boragina speaks from experience on both sides of that equation. Having spent years as a commercial banker before moving to fintech, he understands that what makes banking distinctive has never been the product itself. It has always been the people delivering it.

A banker's job is a split screen. On one side is the high-value work of relationship management. On the other is a burden of administrative tasks. In a heavily-regulated industry, that burden is tedious, and it isn't optional. But Boragina believes that when products are commodities, institutions differentiate through their people, and that technology's job is to free those people up for what can't be commoditized: human connection. The stakes only grow as shorter tenures mean institutional knowledge can walk out the door, making the ability to surface and preserve that knowledge more critical than ever.

• Service standout: "When financial products are commodities, service becomes the core differentiator. That service is what people remember, it's what builds loyalty, and it's how you create not just 20- or 30-year relationships, but legacy relationships with their clients' children," says Boragina. "That creates true stickiness and unlocks real growth opportunities." That kind of relationship is only built face to face, which means the technology has to work behind the scenes, not in front of the customer.

• Beyond the binary: The solution, then, is to point technology not solely at the customer, but primarily at the employee. The approach reflects an industry trend toward tiered autonomy in banking, where AI handles rote tasks while humans retain control. The philosophy is one of augmentation, focusing on how technology can amplify, rather than replace, human expertise by keeping the human expert at the center of the process. "My philosophy is to always keep a human in the loop, because a skilled banker understands the intangibles that technology cannot. They can read the emotional quotient (EQ) in a room and the temperature of a situation," he adds. "In financial services, where decisions about money are deeply personal, that human judgment will always be the most critical component."

This philosophy attacks a systemic inefficiency. Commercial banking runs on data, and that data has long been a burden as much as an asset. For skilled professionals whose real value lies in relationships and judgment, hours spent on manual monitoring, chasing down information, and redundant cross-referencing is time taken directly from clients. AI is changing that equation, helping banks manage risk and compliance more efficiently, navigate the cyber risks associated with AI adoption, and do it on a foundation of GenAI-ready infrastructure.

• Signal from noise: "AI can proactively notify a banker when a customer's risk rating deteriorates or financials trend negatively," says Boragina. "This allows me to focus immediately on the 10 or 15% of my portfolio that is most at risk, instead of manually reviewing all 150 relationships. That shift from reactive forensic work to proactive risk management saves dozens, if not hundreds, of hours annually." When risk surfaces itself, bankers can act on intelligence rather than hunt for it.

• Document dialogue: "Think about the inefficiency: an underwriter, a relationship manager, and a credit committee all reading the same 30-page document. By leveraging AI, I can simply ask that document questions, have a conversation with it, and surface the key data for everyone," he notes. "This ensures you aren't wasting the time of highly skilled people on redundant tasks. You're reallocating that time to be with the customer." The document stops being a barrier and becomes a briefing.

But the true ROI isn’t just about productivity gains. According to Boragina, a more telling measure of success has emerged, especially after the economic shocks that saw several banks fold. The new benchmark is how effectively an institution manages risk. That change in focus aligns with principles of responsible AI and data management and reflects the three pillars of AI implementation being adopted by leading institutions. By managing risk more efficiently, bankers unlock the time for strategic conversations that build relationships and uncover new revenue.

• From clicks to clients: "The process is simple: I can automatically send notifications, the customer can upload their financial statements, the system spreads them, and covenants are satisfied," says Boragina. "That entire workflow takes three minutes. Now, I can have an intelligent conversation about that individual's business and industry, which is where you unlock new opportunities through strategic advice or referrals." The opportunity is real, but realizing it depends on more than the right tools. It depends on whether institutions are willing to invest in the trust, training, and cohesion that make those tools actually work.

But none of this transformation happens without trust. For these systems to deliver, employees must have confidence in them, which means institutions cannot just deploy technology and move on. Some are even exploring concepts like a shared AI model among banks to build that foundation. "An institution's tech strategy becomes convoluted when you overburden employees with a fragmented set of tools. The responsibility is on the institution not just to provide the technology, but to educate their people on how to use it," says Boragina. "That training is what makes employees more productive and, ultimately, better equipped to serve the customer."