The agentic bank: A generational shift in banking operations

Banks have spent the past two decades digitalizing. Branches became apps. Paper became workflows. Processes that once relied on manual intervention have been streamlined, automated and accelerated.

Yet despite these advances, much of the industry’s operating model remains fundamentally unchanged. Behind every digital experience is a complex network of handoffs, approvals, reviews, exceptions and decisions that still require significant human coordination. Banks have become more digital but not necessarily more autonomous.

At the same time, client expectations continue to rise. Consumers now expect real-time experiences, making it harder to justify why opening an account, securing a mortgage, resolving a payment issue or completing compliance checks can still take days or even weeks. Meanwhile, banks face mounting pressure to improve efficiency, strengthen risk management, navigate evolving regulations and drive growth in increasingly competitive markets.

This is the context in which agentic AI has emerged. Unlike previous waves of automation that focused on optimizing individual tasks, it can orchestrate entire outcomes. It represents a shift from process execution to outcome orchestration — and with it, a fundamentally different vision for how banks operate.

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Why this AI moment in banking is fundamentally different

The financial services industry has navigated technology waves before: core banking modernization, the internet, mobile and cloud. Each brought genuine change. But what is happening now is different because the market, the client relationship and the operating model are all changing simultaneously.

This is not simply another technology upgrade cycle. Competitive advantage is no longer determined solely by access to capital, products or distribution. Increasingly, it rests on how effectively institutions combine intelligence, automation, data and decision-making across their operations.

The relationship with partners such as IT service providers is changing, too. Contracts that once centered on headcount and capability delivery are increasingly structured around outcomes. Banks are navigating this reconfiguration while managing growing technology, vendor and governance demands.

This convergence of forces acts as a structural reset, with agentic AI sitting at its center.

How agentic AI differs from traditional automation

The distinction between agentic AI and traditional automation matters because it’s frequently misunderstood.

Traditional automation — including robotic process automation, workflow orchestration and rule-based decision engines — operates deterministically. You map a process, define the steps and anticipate the branches, and the system executes accordingly. It’s predictable by design.

Agentic AI operates differently. The system is given an objective rather than a set of instructions. It evaluates data, context and changing conditions to determine the most effective path to an outcome.

A useful analogy: Traditional automation follows a map, while agentic AI navigates toward a destination. There is no fixed sequence of actions. The route evolves based on what the system encounters, learns and prioritizes in real time.

This is the mindset shift that banking leaders need to internalize. Agentic AI is a different class of capability — one that expands what banks can meaningfully ask technology to do.

Banking use cases where agentic AI delivers the greatest impact

Not all banking processes are equal candidates for agentic transformation. The greatest opportunities tend to share three characteristics: complexity, regulatory scrutiny and a heavy dependence on cross-functional decision-making.

To better understand where the impact is likely to be felt, NTT DATA convened banking specialists in retail, wholesale, wealth management, governance, risk and compliance. Several high-priority domains emerged.

• Lending and mortgages stand out as the clearest near-term opportunity in retail and small and medium-sized enterprise (SME) banking. These processes are document-intensive, multiparty, jurisdiction-sensitive and chronically slow. Completing a mortgage process in days rather than weeks is increasingly becoming an engineering problem with a credible path to a solution.
• Cross-border payments represent a similar opportunity in wholesale banking. Settlement cycles that span days are increasingly difficult to justify in a world where domestic real-time payment rails are becoming the norm. “Atomic settlement" — frictionless, near-instantaneous cross-border exchange — remains one of the industry’s most ambitious goals. NTT DATA is developing an AI validator asset in partnership with SWIFT to accelerate adoption of new cross-border payment schemes.
• Fraud detection, anti-money-laundering and know-your-customer processes represent a third high-priority domain. Banks face a compounding challenge: The same AI capabilities they are adopting for transformation are also being used by bad actors.

The response must be intelligent, adaptive and fast. We operate end-to-end fraud-detection platforms for major global banks and are developing the next generation of intelligent controls. According to Intelligent Banking in the Age of AI research, 53% of banking decision-makers already regard AI as highly effective in fraud prevention.

Across all three domains, the highest-value opportunities are not individual tasks but whole business processes where autonomous orchestration can create value at scale.

Data and knowledge are the foundation of every effective AI agent

Every conversation about agentic AI eventually arrives at the same question: What knowledge are agents acting upon?

One of the most consistent findings in our banking AI engagements is simple: Systems that underperform often do so because of data. An agent cannot make sound decisions without reliable, contextually rich information, and most AI failures ultimately trace back to inadequate access to the right knowledge at the right moment.

The value of a well-governed data and knowledge layer is both operational and competitive. As the knowledge infrastructure beneath them evolves, agents become more accurate, adaptive and effective. Every correctly processed transaction, every resolved exception and every successfully navigated edge case becomes institutional intelligence that can be applied again and again.

Customers experience the benefits through faster decisions, more personalized service, fewer errors and more seamless interactions.

Investments in data quality, data architecture and knowledge management are therefore foundational conditions for AI to perform at the level that banking demands.

The performance data reflects this directly, as shown in our 2026 Global AI Report: A Playbook for Banking and Financial Services. Among banking and financial services organizations that have fully aligned their AI and business strategies, 84.1% report a profit uplift of at least 5% from AI. Among those with no strategic alignment, that figure drops to 58.3%.

AI leaders are also significantly more likely to achieve stronger revenue growth and higher profit margins. The difference between leaders and laggards is not primarily a question of technology access; it’s a question of strategic coherence, data readiness and disciplined execution.

How leading banks approach governance and sovereign infrastructure

Speed without oversight is not a viable strategy in a regulated industry. As agentic systems become more capable, governance becomes even more important. Trust will ultimately determine the speed of adoption in banking.

Governance in the agentic era operates on two levels:

• Organizational AI governance: The policies, risk frameworks, steering committees and accountability structures that determine how AI is deployed, monitored and controlled across the enterprise.
• Agent governance: The controls, audit trails and oversight mechanisms that govern what individual agents are permitted to do, when human intervention is required and how decisions are logged and explained.

Both are necessary. Conflating them creates structural blind spots that regulators and risk teams will eventually expose.

Sovereign AI is an equally urgent priority. For banks, the principle is straightforward: training data, model protocols and inference activities must remain within the institution’s controlled perimeter. No sensitive customer data, proprietary risk models or regulatory-adjacent information should reside on infrastructure the bank does not control.

NTT DATA participates in the emerging global consortium on sovereign AI and works with banks across regions to build the frameworks and infrastructure that make this standard by design.

Research reinforces the urgency. Many organizations still lack formal AI policies, while concerns about AI security continue to outpace governance maturity. The gap between ambition and governance readiness remains wide, but it is also where sustained competitive advantage can be built.

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The agentic bank is not a distant vision

The conditions for transformation are already in place. Most banks now have a GenAI strategy, adoption is accelerating and investment continues to rise. The organizational will is present, with AI initiatives increasingly driven from the C-suite.

So, what does an agentic bank actually look like?

Mortgage approvals that once took weeks are completed in days — or hours. Compliance monitoring operates continuously rather than periodically. Cross-border payments move at the same speed that customers expect from domestic transactions. Fraud controls adapt in real time to emerging threats. And most importantly, employees spend less time navigating complex processes and more time applying judgment, building relationships and creating value for customers.

What matters most is the quality of alignment between AI strategies and business outcomes, the maturity of data and knowledge infrastructure, the strength of governance frameworks and the ability to execute transformation at scale.

We support banks throughout this transformation — from strategy design and governance to implementation and operations. With deep expertise in retail, SME, wholesale and capital markets, a growing suite of proprietary assets and accelerators, and an implementation footprint spanning Europe, the Americas and Asia Pacific, we operate not as a technology vendor with a banking interest but as a banking transformation partner with technology depth.

The agentic bank is not a five-year roadmap. It’s already under construction.

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