AI in financial services: Where value is already emerging

Not so long ago, banking was a paper-driven business. Legions of clerks working in back rooms processed forms in triplicate and manually sorted, stamped and stored documents. Entire operations were built around the physical handling of information. It was time-consuming, resource-heavy and prone to human error.

As digitalization accelerated, banks moved quickly to modernize. Now, online banking is front and center, automation has sped up processes and systems are more connected. Fast-growing financial institutions are increasingly data-driven — and the customer experience has improved as a result.

AI is the next area where measurable value is emerging. This is not the flashy GenAI dominating the headlines but the kind embedded in workflows to analyze patterns, flag risk and enable faster decisions.

AI: More than a chat window

For many customers, AI in banking means chatbots. But while these bots offer automation, they’re not intelligence in the deeper operational sense. Predefined scripts determine responses, and they don’t anticipate what comes next.

Banks operate in an environment defined by speed, regulation and rising customer expectations where decisions must be made fast and accurately and risk must be minimized. In this context, purely reactive tools have limits.

AI operates differently. It assesses information continuously and acts on it in real time to identify irregularities as they emerge. Instead of waiting for a query, AI evaluates situations and responds immediately, which reduces risk and keeps issues from escalating.

For financial institutions, the value often lies in AI’s ability to achieve all of this with limited human oversight. This capability is already being applied to critical functions within these institutions.

Operational impact: Banking on intelligence

AI is becoming part of the backbone of the modern banking system. It’s streamlining processes, taking on repetitive tasks and reducing bottlenecks. It supports transaction monitoring, compliance checks and customer onboarding, and its reach extends to risk management, credit assessment and internal workflows.

But in some areas, its impact is particularly significant and measurable.

Fraud detection

An unwelcome reality of digital banking is the increase in fraudulent activity. Criminal organizations adapt to checks and balances as quickly as financial institutions implement them. AI is instrumental in detecting fraud in real time because it can flag patterns that would be impossible to detect manually.

When banks identify suspicious activity earlier, they intervene more quickly — often before the loss escalates. They also need less time to investigate fraud and can speed up restitution when applicable. This protects these institutions’ reputations and builds trust with their customers.

Onboarding

If you’ve ever been onboarded by a bank, you know how tedious and admin-heavy the process is, with multiple forms, numerous points of contact and myriad verifications. AI strips away much of this busy work to make the process faster and more efficient — for both the bank and the customer.

Know your customer (KYC)

For financial institutions, establishing trust and building loyalty are key to earning lifetime customer value. That begins with knowing your customers. Central to KYC is AI’s ability to understand behavior over time. AI can monitor patterns, recognize changes and build a more complete picture of how customers interact with the bank as well as their broader financial habits.

This level of insight puts banks in a better position to offer relevant guidance and opens the door to cross-selling opportunities grounded in a thorough understanding of customer needs.

Credit assessment

For banks, it’s critical to know who they can lend to, and how much. AI is also delivering value in this area as it analyzes repayment histories, transaction behavior and other relevant information. By applying the same criteria across applications, it helps reduce the risk of subjective decision-making and brings greater consistency to the process.

Beyond these examples, AI is also changing how banks think about risk and day-to-day operations. Instead of working off static reports or waiting for monthly reviews, institutions can see patterns forming as they happen. Such visibility makes it easier to respond early rather than react late.

A better employee experience elevates customer experience

Employee experience and customer experience go hand in hand. When employees are happy, empowered and able to do their best work, customers feel it — and AI plays a role in enabling that.

While many fear that digital agents will replace humans in the workplace, that fear is largely unfounded. Roles may evolve, but there will always be a need for humans in the loop. Research from Brookings found that, contrary to widespread fears, AI adoption has been linked to firm growth and increased employment.

So, what does this look like for an employee on a day-to-day basis?

Simply, it means less time bogged down in paperwork, less repetitive work and fewer manual processes to navigate. Staff who interact with customers daily can now focus on high-value interactions rather than administrative tasks. When they engage with customers, they have a clear view of their history, recent activities and current needs. They spend less time gathering information and more time using it.

Meanwhile, customers feel the difference as they experience a streamlined and frictionless service.

• ALSO READ → Human AI: Building the ethical foundations for the future of banking

AI implementation: Where it goes wrong

However, while many financial institutions aren’t short on AI ambition, they may find it difficult to navigate its complexity.

Legacy systems, siloed data and disconnected platforms make it harder to scale beyond isolated pilot projects, and organizations often want to do too much too quickly. They launch pilot projects without clear ownership or measurable outcomes, when the real discipline lies in applying a detailed strategy, focus, integration and discipline to those that will demonstrate clear value early.

Governance is just as important. AI can’t sit outside risk, compliance and security frameworks, which must be built in from the start. When they are added later, projects may stall.

NTT DATA: Wired for intelligence

We know AI in banking is not a one-size-fits-all exercise. Each institution has its own legacy systems, data landscape and regulatory pressures. That’s why we work with you to assess your unique environment and priorities — and, as a full-stack provider, support you from the ground up, from strategy and core infrastructure to network capabilities, applications, data, security and AI implementation.

We embed intelligence in the systems that banks already rely on, and our hyperscaler partnerships, global experience and deep industry knowledge help you integrate and scale solutions efficiently.

Banking may have moved on from paper, but it has not moved on from process. The difference now is that those processes are increasingly supported by intelligence in ways that improve business outcomes.

• ALSO READ → High-yield AI in financial services depends on secure, scalable networks