Agentic AI Is Ready. Are Banks Prepared to Use It?

Agentic artificial intelligence (AI) has become more than a buzzword in a very short period of time.

One reason for its progress into legitimacy is its real potential to redefine how financial institutions deliver services and interact with their customers. Rather than simply performing pattern recognition or automation tasks in isolation, agentic AI is capable of perception, reasoning, action and crucially, self-learning.

In an industry typically known for incremental change, the accelerating traction of agentic AI may represent one of the most disruptive and impactful developments in financial services.

That’s the mindset of i2c CEO Amir Wain. In a recent conversation with PYMNTS CEO Karen Webster, Wain outlined some of the critical infrastructure elements needed for a successful agentic AI strategy.

Wain noted that although many institutions have embraced AI at the margins — particularly for tasks like fraud detection and product recommendation — most have only scratched the surface of what agentic AI can achieve. The main reason for this “under-exploration” is not simply a lack of ambition or imagination; it is the reality that many banks still rely on legacy systems that make data difficult to access in real time.

Wain stressed that agentic AI requires vast amounts of contextual information.

“If I still want to interact the same way that I did, then I’m really not maximizing the capabilities of agentic AI,” he said.

In other words, the tools themselves are ready, but the underlying infrastructure within financial institutions is often ill-equipped to serve up the necessary data fluidly and consistently.

A key element of this infrastructure challenge is the notion of the “one unified customer.”

According to Wain, even the best AI models perform sub-optimally when data is segmented by product or siloed in disparate systems. A unified view means that a single customer record spans checking accounts, credit cards, loans and every relevant service, so that the AI engine can draw upon a complete and holistic picture.

With that wealth of information, agentic AI can respond to customer needs far more intelligently, rather than having to piece together snapshots from multiple data sets.

“We’ve heard this so many times — customer centric and so on,” Wain said. “But this is truly an opportunity to architect your business, your enterprise, your infrastructure to be customer-centric.”

In practical terms, he told Webster, that might mean noticing a mismatch between a customer’s checking balance and their upcoming loan payment, anticipating that shortfall, and automatically suggesting solutions — without sending the individual to a separate product department.

The most powerful applications? Wain pointed to risk management, credit underwriting and streamlined customer service as particularly promising areas.

Using artificial intelligence to re-imagine the entire customer workflow — rather than merely dropping AI into existing workflows — produces what he called “truly customer-centric” banking. In an ideal future state, as Wain described, an AI-enabled system recognizes that a customer has a question regarding their credit card limit, cross-references their history with multiple accounts, analyzes their risk profile, and then responds quickly with a relevant solution. Rather than pushing the customer to a separate phone line or a new webpage, the agentic AI might handle everything from limit adjustments and product offers to compliance checks and final approvals, seamlessly and behind the scenes.

Still, fraud detection remains one of the most talked-about and mature use cases for AI — and i2c has integrated agentic AI into its fraud monitoring processes. As Wain noted, the difference is not merely detecting anomalies, which many existing systems can already do. Instead, agentic AI can take the entire workflow — starting from detection and extending all the way to adjudication — and automate large portions of it.

“Think about what the current fraud systems do,” he said. “They detect. Then, at best, a notification is sent out … but let’s think through the workflow, what needs to happen from there on?”

A truly robust system, as he went on to illustrate, can decide whether to decline a transaction, temporarily lock an account or escalate to a human analyst only for the most ambiguous cases.

According to Wain, i2c’s own fraud engine captures 40% of fraud at a decline rate as low as 0.5%, a performance he attributes partly to a “data flywheel” effect: the system continuously learns from every decision, feeding back into the core model to refine future judgments.

That same data loop underpins the promise of personalization, which Wain believes is moving from marketing pitch to tangible reality. For years, financial institutions have promised customized products, but delivering true personalization at scale has remained elusive, often because each product line had its own systems and processes.

With a “one unified customer” design and agentic AI working in tandem, however, Wain sees a path to high-touch, individualized service that was previously restricted to elite private banking. In this new model, every customer could theoretically get a premium, concierge-style level of support, without overwhelming the bank’s service infrastructure, because a properly architected AI platform never rests, scales quickly and becomes smarter with every customer interaction.

“We’ve been after AI before it became a buzzword,” Wain said, highlighting i2c’s commitment to building a future-ready data science team and technology foundation.

Ultimately, who stands to benefit most from agentic AI?

According to Wain, it will be the organizations — bank or non-bank — that possess a robust data infrastructure and a leadership team willing to make bold changes.

“Companies who have top leadership who is very supportive, can allocate resources and are willing to make those changes will come out ahead,” he said, pointing out that agentic AI can only deliver its full value when it can access real-time, integrated data.

With consumers already experiencing GenAI in other areas of their lives, inertia is not an option: “People see the power of AI, and they’re willing to pay for it,” Wain said, underscoring that increased investment in flexible data architectures could soon be table stakes for everyone in financial services.

For i2c, this has meant a concerted effort to develop a modern data model, build a large data science team and embed agentic AI in fraud prevention and beyond. Wain was confident that embracing this new frontier will yield serious benefits, from operational cost savings to strategic differentiation.

But the core message applies industry-wide: agentic AI is more than the latest buzzword.

Whether tackling fraud, personalizing product offerings or seamlessly orchestrating complex customer interactions, the institutions that seize the agentic AI opportunity — and invest in the modern infrastructure to support it — are likely to set the pace for the next wave of innovation in payments and banking.

If Wain’s predictions hold true, that wave is coming much faster than anyone imagined even a few years ago.

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