Across the banking industry, institutions have invested billions modernising their technology stacks. Cloud infrastructure, digital platforms and artificial intelligence have become central components of transformation strategies as banks attempt to improve operational efficiency, deliver faster credit decisions and simplify customer interactions.

Yet despite these investments, the underlying structure of many banking operations has changed far less than expected. Lending processes can still take days or weeks. Information often moves through multiple internal teams before a credit decision is made. From the customer’s perspective, what should be a single interaction with a bank frequently involves several disconnected processes behind the scenes.

For Will Jung, Chief Technology Officer at nCino, the problem is rarely the availability of technology itself. The deeper challenge lies in how banks organise the processes and decision-making structures that operate on top of those systems.

nCino develops cloud-based banking software that enables financial institutions to manage onboarding, lending and relationship workflows within a unified platform designed to support the full lifecycle of a banking relationship. The company’s platform connects customer onboarding, loan origination, credit analysis, documentation management and portfolio monitoring so that information gathered at one stage of the relationship can inform decisions throughout the lifecycle.

“My role as Chief Technology Officer at nCino is really being able to drive the strategy to execute on our vision of powering a new era in financial services,” Jung says. “That means aligning our engineering organisation globally and making deliberate decisions about where banking technology will be in three to five years, but also ensuring that the platform evolves in a way that allows banks to transform how they actually operate rather than simply replacing existing systems.”

Jung says many transformation programmes focus heavily on replacing legacy infrastructure while leaving the organisational structures built around those systems largely unchanged.

“With any transformation, it’s not just the technology,” he says. “Technology is a great enabler and often a trigger for change, but real transformation also requires clarity around who owns the customer experience across the organisation. If that accountability remains fragmented across different teams, then installing a new platform doesn’t necessarily improve how the bank actually serves the customer.”

The result is that banks can adopt modern technology while continuing to operate with workflows and decision structures designed for a different technological era.

Why transformation programmes stall inside banking organisations

The persistence of organisational silos remains one of the most significant barriers to effective transformation. Customer journeys that appear straightforward from the outside often involve multiple internal processes across different departments.

When a bank evaluates a lending request, for example, the process may involve gathering financial documentation, verifying compliance requirements, analysing financial statements, conducting risk assessments and obtaining approvals from multiple levels of the organisation. Each step may be managed by different teams and supported by separate systems.

Jung’s perspective on this challenge reflects experience on both sides of the industry. He began his career as a software engineer within a bank before moving into technology transformation roles and later joined nCino after previously working with the platform as a client. That background exposed him directly to the operational complexity banks face when attempting to modernise technology environments while maintaining regulatory oversight and risk management controls.

“A lot of white-collar work, not just in banking but across industries, has historically been organised around tasks,” Jung says. “Those tasks were created because technology couldn’t automate certain steps in the past. As technology evolves and AI becomes capable of handling more of those activities, organisations need to ask whether the workflow itself still makes sense.”

In many organisations, roles and processes were originally designed to compensate for technological limitations that no longer exist.

“Process is normally the gap between what people can do and what technology of today can do,” Jung says. “As technology evolves, that gap becomes smaller. If organisations continue to operate with the same processes even though technology can now automate large portions of the work, the workflow becomes unnecessarily complex.”

In practice this means entire layers of operational activity may persist even when technology has the capability to streamline them.

“A large portion of work in banking today exists because technology previously couldn’t perform those tasks,” Jung says. “As automation becomes more capable, employees can spend less time on repetitive activities and more time helping customers navigate complex financial decisions.”

Replacing legacy systems therefore addresses only one part of the transformation challenge. Banks must also reconsider how responsibilities are organised and how decisions move through the organisation.

“In many cases the challenge is not implementing the technology,” Jung says. “The challenge is redesigning the operating model around that technology so that the workflow itself becomes faster and more efficient.”

Cloud platforms enable lifecycle-based banking architecture

Cloud infrastructure has become a central component of banking modernisation strategies. Many institutions are migrating technology environments to cloud platforms to improve scalability, resilience and system integration.

However, Jung emphasises that the benefits of cloud architecture depend on how banks redesign their operational workflows.

“Often when banks move to the cloud, the programme becomes focused on the technology migration,” he says. “But what really matters is how the platform architecture supports the processes around it. If the workflow itself remains fragmented, the technology alone will not change the customer experience.”

One of the areas where nCino focuses its platform architecture is addressing fragmentation across the lending lifecycle. The platform connects onboarding, loan origination, credit analysis and portfolio management within a single operational environment so that information gathered during one stage of the relationship remains available across subsequent stages.

“When you’re being onboarded or originated as a customer of a bank, the information you provide and the services you receive continue to be relevant throughout the relationship lifecycle,” Jung says. “From the customer’s perspective that is a single experience, but internally those processes are often managed by different teams and supported by different systems.”

This fragmentation can limit a bank’s ability to maintain a consistent understanding of the customer relationship.

“Having onboarding, origination and portfolio management on the same platform creates a shared data model and consistent context across the lifecycle,” Jung explains. “That consistency becomes increasingly important as you embed automation and AI into the workflow because those systems rely on accurate, real-time information.”

Integrated platform architecture therefore allows banks to maintain continuity across the customer lifecycle while reducing operational complexity.

Artificial intelligence is transforming how banks analyse information

Artificial intelligence has quickly become one of the most widely discussed technologies in financial services. Yet Jung believes the most effective applications focus on improving how banks structure and analyse information rather than replacing human decision-making.

“It’s easy to get swept up in the excitement around AI and start applying it everywhere,” he says. “But the starting point should always be the outcome you want to improve. Are you trying to reduce the time it takes to make a credit decision, process documents faster or gain better visibility into portfolio risk?”

In lending operations, AI is already helping banks analyse financial documentation and extract relevant information.

“A lot of banking work involves gathering information and making sense of large volumes of data from different sources,” Jung says. “Large language models are particularly effective at gathering and structuring large amounts of information quickly, whether that information comes from financial statements, reports or other data sources, so that humans can review that information more efficiently and make better-informed decisions.”

AI systems can analyse financial documents, extract relevant data and summarise information that analysts would otherwise review manually. This allows bankers to focus on interpretation and judgement rather than basic information processing.

However, Jung emphasises that these technologies support rather than replace human decision-making.

“The final decision in terms of what we provide as a bank to the customer is still human-led,” he says. “AI helps gather and structure the information, but people remain responsible for evaluating the risk and ensuring that decisions align with regulatory requirements and the bank’s policies.”

In that sense, AI functions as an augmentation tool that improves the speed and clarity of decision-making without removing human accountability from the lending process.

Data governance determines the effectiveness of AI

As artificial intelligence becomes more embedded in banking operations, the quality and governance of data become increasingly important.

“Clean data is really important,” Jung says. “Like any technology system, it’s garbage in, garbage out. If the data being used by AI models isn’t reliable, the results those models produce will also be unreliable.”

Rather than attempting to rebuild enterprise-wide data architectures at once, Jung suggests that banks focus on improving the data required to support specific operational outcomes.

“You don’t necessarily need every piece of information about a customer to deliver a particular service,” he says. “If you start with the outcome you want to achieve and identify the data needed to support that outcome, you can improve data quality incrementally.”

Operational improvements can also translate directly into financial outcomes for banks.

“If a bank can provide a faster credit decision, particularly for products such as mortgages or commercial loans, it increases the likelihood that the customer proceeds with that bank,” Jung explains. “Once the loan settles on the balance sheet, interest income begins earlier, so operational efficiency can translate into financial outcomes.”

However, Jung cautions that technology improvements alone do not guarantee productivity gains.

“If processes become faster but the organisation continues operating in exactly the same way, the productivity gains may not materialise,” he says. “People may still be performing tasks that no longer add value simply because the workflow hasn’t been redesigned.”

Leadership involvement therefore becomes a critical factor in transformation programmes.

“The organisations that have been most successful tend to have strong engagement from the CEO and senior leadership,” Jung says. “When leadership is directly involved, it becomes easier to make the organisational decisions required to support the technology.”

Aligning platforms, data and operating models

The banking industry now has access to technologies that were unavailable even a decade ago. Cloud infrastructure, artificial intelligence and advanced analytics are widely accessible across the sector.
Yet the institutions that benefit most from these technologies will be those that redesign their operating models to take advantage of them.

Jung’s perspective highlights that transformation depends on aligning platform architecture, data governance and organisational structures with the customer lifecycle. Platforms such as nCino are designed to support this shift by integrating lending workflows, customer data and decision processes within a single operational environment.

Technology investment alone, Jung says, does not transform banking.

Meaningful transformation occurs when institutions redesign how decisions move through the organisation, ensure that information flows consistently across the customer lifecycle and deploy technology platforms that support faster, more informed decision-making across the enterprise.