Philippine Bank CxOs Say AI Is Delivering, Just Not Where It Matters Most (Yet)

Banks nowadays often talk about AI ever since it became this shiny new thing a few years back, but in a closed-door room in Manila, the conversation took a direction that many in the industry rarely voice out loud.

Something that we have not heard about.

Everyone in that room agreed that AI has become unavoidable, deservingly so, right? Well, not quite.

Inside that room, these CxOs also agree that almost every banker admitted that most of what they are doing today barely scratches the surface.

The truth is that banks might be embracing AI, but they are not dreaming with it. More like doing it for the sake of just following the trend.

But trend, does not last, or does it?

The room was a mix of traditional players, digital challengers, and leaders who have lived through several waves of banking transformation. Within minutes, it became clear that AI has already seeped into almost every corner of their organisations.

A Question That Set the Tone Early

As they began unpacking what their AI programmes had actually achieved, a question emerged that would quietly frame the rest of the conversation.

Moderator Vincent Fong, Chief Editor at Fintech News Network, laid it out plainly:

From that point onward, every story shared, every challenge raised, and every aspiration explored kept circling back to that same idea.

Banks had already unlocked meaningful gains. Teams were working faster. Processes were smoother. Decision-making had become richer and more precise.

But Vincent’s question pushed the room to confront a deeper truth: the next phase of banking growth won’t come from running more AI pilots or layering new tools onto old journeys.

It will come from how deeply institutions are willing to re-examine the design of those journeys altogether.

Some have been running machine learning and RPA quietly for years. Others now rely on Copilot for their daily workflows. Many have internal LLM layers that sit on top of policy manuals, product documentation, compliance guidelines, and customer journeys to make staff more effective.

Their fraud teams now have new AI-driven tools, risk teams have introduced predictive scoring models, and the operations teams use them to extract data from forms, reconcile documents, and triage cases.

Even frontliners have begun adopting AI-powered knowledge assistants that can help answer customer queries with far better consistency.

In other words, the AI, has become part of the everyday machinery of pretty much every Philippine banking and/or financial company.

This was also true at UNO Digital Bank. When the conversation turned to productivity, it was Manish Bhai, UNO Bank’s CEO, who shared a moment of unexpected candour.

Many were seeing the same effect inside their organisations.

Tasks that used to take an hour were now done in minutes. Drafts that once needed multiple rounds of checking could be produced with sharper accuracy on the first try. Even regulatory documents, once notoriously time-consuming, were moving faster with fewer revisions.

The gains were real, not theoretical. Yet as everyone shared their wins, a more important truth began to surface.

AI Has Become Deeply Embedded, But Mostly Behind the Scenes

As the conversation continued, the group gradually realised that almost every AI success story they mentioned had one thing in common.

All of it sat behind the scenes.

It made life easier for staff, for risk managers, for compliance teams, for operations. But the customers, they barely saw it.

Mike Singh, President, Tendo by Tonik, took the mic and finally voiced a perspective that had been sitting under the surface of the conversation

He wasn’t dismissing the work being done.

In fact, several participants had later pointed out that many internal AI improvements (whether in fraud detection, operations, risk, or compliance) do translate into faster turnaround times and fewer errors, which ultimately benefit customers.

Mike’s point was slightly different. These improvements, while meaningful, still sit mostly in the background. Customers rarely feel them in a direct or tangible way.

That realisation reframed the conversation.

The group acknowledged that much of the current AI effort is focused on internal efficiency, not on addressing the day-to-day financial stress, planning challenges, or anxieties that Filipinos face when managing their money.

They had not explored how AI might prevent a missed payment before it happens, guide someone toward healthier financial habits, or help a gig worker with uneven income get a fairer assessment for credit.

These are the kinds of outcomes customers immediately recognise—yet they are often the last to be prioritised.

The discussion wasn’t about a lack of concern for the customer. It was about recognising the gap between what AI is capable of and what customers currently experience.

Jerry Ngo, CEO of EastWest Bank, added a useful lens.

It wasn’t a call for secrecy but rather was a call for trust and subtlety.

Customers want banking to feel smoother, more intuitive, and less of a chore.

These customers want faster resolutions and smarter recommendations. They don’t need to know which AI model is making it possible. They just want to feel supported, not scrutinised.

The Hard Part of Cleaning Decades of Data Debt

What surprised no one was the topic that came next. Every bank in the room admitted that their biggest obstacle remains data.

The problem is not a lack of information. It is the condition in which everything has been stored.

Over the years, customer profiles have been duplicated, scattered across various systems. Some were even left incomplete altogether.

Much of this sits on core banking platforms built many moons ago, and several of these systems still carry critical workloads up to this day.

As onboarding journeys evolved, they added new forms and processes, leaving behind layers of unstructured records that never aligned with the ones before.

By the time the room laid out the full picture, the scale of the challenge became impossible to ignore.

One banker noted that a single customer could appear under multiple identities depending on which part of the bank you asked.

Another pointed out that some analytical models were built on data sets never intended to support AI. Piece by piece, the group painted a portrait of an industry working with information that was abundant yet uneven.

Lito Villanueva, EVP and Chief Innovation and Inclusion Officer at RCBC, captured the issue in a line that drew instant agreement.

Many banks have already begun the long, meticulous process of fixing this. Some are rewriting entire onboarding flows to create a consistent identity foundation.

Several are reshaping their architecture so information moves in real time and can support AI-driven decision-making.

A few even described the journey as renovating a house while still living in it. Something like where the operations cannot stop, but waiting another five years is not an option either.

This part of the conversation was neither glamorous nor theoretical, but it was a reminder that the most meaningful breakthroughs in AI will not come from the models themselves, but from the foundations banks choose to rebuild beneath them.

AI Culture Is Emerging as the Biggest Differentiator

Midway through the session, a new theme began to take shape.

It became increasingly clear that technology alone is not what separates early adopters from those still finding their footing. The real gap is cultural.

Several banks spoke about building internal AI labs where teams can experiment freely without worrying about slowing down operations.

Some have carved out dedicated time each week for employees to explore new tools and rethink existing workflows. A few monitor AI usage at the leadership level to keep their initiatives coherent, while others have set up central governance councils to prevent projects from scattering across the organisation.

All of these efforts point to the same realisation. AI is changing how organisations think, create, and make decisions.

Teams learn to draft with more confidence. Context becomes richer and easier to access. Information moves faster across departments.

People are now starting to treat AI not as a novelty but as an everyday partner in their work.

Matthew Chen, CEO of OneConnect Financial Technology, captured this shift in very human terms.

He was not suggesting that people are becoming less important. In fact, he was stating the opposite.

AI clears the mechanical work that often weighs down frontliners, allowing these “people” to focus on empathy, connection, and meaningful guidance.

The banks that recognise this cultural dimension are the ones pulling ahead. They see AI not as a set of tools to deploy but as a new way of operating, and that mindset is proving to be their strongest advantage.

When the Fear Lifts, Banks Start Imagining What AI Could Really Do

As the conversation wound down, Gigi Puno, CTO, GoTyme Bank, introduced a thought experiment that instantly shifted the room’s energy. It was a simple question wrapped in a futuristic scenario.

From that point, the limitations of older institutions became far easier to see.

Instead of wrestling with layers of legacy processes, a new bank could begin with a clean slate. Onboarding could be built to unify identity, biometrics, transaction patterns, income signals and communication preferences into a single, living customer profile from the very beginning.

Risk assessment could evolve as well.

A lending engine might evaluate a borrower not only by documents but by behavioural consistency, payment habits and contextual markers that offer a more dynamic picture of creditworthiness.

Even customer support could feel remarkably intuitive, as if every staff member already understood the customer’s situation the moment they reached out.

Matthew Chen of OneConnect Financial Technology pushed the vision further with something more tangible.

He asked to picture a branch experience where the system recognises you the moment you walk in. The bank already knows what you called about the day before.

It has the full financial context behind your concern and guides you to the right person without unnecessary steps.

To the customer, the sophistication remains invisible. What they feel is care, continuity and attention.

The room agreed that none of this belongs in the realm of science fiction. The building blocks already exist. The real gap is no longer capability but imagination.

And yet, for all its sophistication, the customer never sees the machinery behind it. What they feel instead is care, continuity, and the sense that their bank finally understands them.

As the group reflected on this imagined future, Vincent Fong’s early question resurfaced almost naturally:

Because the vision they were now describing wasn’t about better tools or faster workflows. It wasn’t about adding another model or automating another process either.

It meant reimagining banks by embedding AI at the foundation rather than weaving it in after the fact.

Every challenge raised earlier in the session suddenly showed up not as roadblocks, but as the very things holding back that future.

And every success they had celebrated felt like early hints of what was possible if banks were willing to dream a little bigger.

By the time the conversation closed, one thing had become clear:

The next leap for Philippine banking won’t be about whether AI is adopted. It will be about how boldly institutions are willing to redesign themselves around it.

The technology is ready. The building blocks exist.

What’s left is imagination. And the willingness to turn it into practice.

For the unfiltered version of everything raised in that room, take a moment to dive into the full discussion by watching the Are Banks Thinking Big Enough About AI? | Philippines AI CxO Roundtable, and let the conversation unfold.

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