EP968: Globally Scalable, Locally Compliant

Joaquin de Valenzuela, nCino's MD EMEA, reflects on his first year highlighting the platform's impact, bankers save 8–10 hours weekly through AI tools. nCino serves over 2,700 customers globally, with banks originating more than $3 trillion in loans on the platform. Cloud-native architecture enables continuous innovation and automatic compliance updates. Across EMEA, priorities differ: European banks focus on efficiency and compliance, Middle Eastern banks on growth, and African institutions on financial inclusion. nCino's competitive edge lies in its vast banking dataset, unified data model, and a three-phase AI roadmap from staff productivity tools to agentic AI and ultimately autonomous banking systems. The biggest industry challenge remains balancing rapid innovation with the security and stability that banking demands.

Transcript

SPEAKER_01 0:00

Right now, like as you listen to this, there are billions of dollars moving through the global financial system. And the wild part is it's running on code that is literally older than the people managing it.

SPEAKER_00 0:09

Oh yeah. It's a massive issue.

SPEAKER_01 0:11

Aaron Powell Right. I mean upgrading your phone takes, what, five minutes? But upgrading a global bank's core software system, that is like trying to replace a patient's nervous system while they are running a marathon.

SPEAKER_00 0:23

Aaron Powell That's a perfect way to put it. The friction between old money and new tech is incredibly severe right now. And the patient cannot stop, they can't stumble, and they certainly cannot afford a system failure.

SPEAKER_01 0:34

Trevor Burrus Because that nervous system is carrying life savings, active mortgages, the operating capital of millions of businesses.

SPEAKER_00 0:41

Exactly. The stakes are as high as they get.

SPEAKER_01 0:43

Which brings us to the core of what we are unpacking today. Welcome to the deep dive. We are looking at this really in-depth November 2025 discussion from the IBSI FinTech Journal. It features Joaquin de Balenzuela.

SPEAKER_00 0:58

Yeah, the managing director for EMEA at the Encino.

SPEAKER_01 1:00

Right. And there is this image in that piece that just perfectly captures the reality of this whole transition. It's a photograph of this vintage glowing glass light bulb, but the filament inside spells out Fentech. I love that image. It's so good. And surrounding this analog bulb is this dense interconnected web of like digital icons representing global finance and data processing.

SPEAKER_00 1:26

It perfectly illustrates the paradox that banks are facing right now. You have this traditional, deeply conservative institution, the light bulb, trying to operate inside a hyperconnected, high-speed digital web.

SPEAKER_01 1:37

Exactly.

SPEAKER_00 1:38

But to really understand how they're bridging that gap, you have to look at the people engineering the transition. Like DeVelanzuela stepping into the EMA leadership role for Encino in late 2024. That is a massive signal to the industry. Trevor Burrus, Jr.

SPEAKER_01 1:49

Because of his background, right?

SPEAKER_00 1:50

Yeah, he came from a role as chief digital officer at Temnals. And before that, he headed financial services for Salesforce across EMEA and Latin America. Heavy hitting experience.

SPEAKER_01 2:12

And for context, Encino is based out of Wilmington, North Carolina. And they have essentially built this unified cloud native platform specifically for intelligent banking.

SPEAKER_00 2:22

Aaron Powell And the sheer scale here is just wild.

SPEAKER_01 2:26

Yeah. They're serving over 2,700 financial institutions globally. And those banks are using this single platform to originate over $3 trillion in loans.

SPEAKER_00 2:36

Aaron Powell Three trillion. I mean, that is a number so large it almost becomes abstract, right?

SPEAKER_01 2:40

Trevor Burrus It really does. It's hard to even picture. Trevor Burrus, Jr.

SPEAKER_00 2:42

But Divelanzuela frames this scale around the idea of intelligent banking fueling dreams and building communities. And when you attach that sentiment to $3 trillion, it it stops being corporate rhetoric. Trevor Burrus, Jr.

SPEAKER_01 2:53

It becomes highly literal.

SPEAKER_00 2:54

Exactly. This isn't just, you know, enterprise saws sitting on a server somewhere. This software is the fundamental plumbing of the real economy. It dictates the specific algorithm that decides if a local bakery gets a lifeline during a slow season or how fast a young family gets approved for their first home.

SPEAKER_01 3:12

Okay, let's unpack this right here. Because putting that much of the global economy on one platform raises massive red flags for me.

SPEAKER_00 3:20

Oh, sure. The centralization aspect.

SPEAKER_01 3:22

Right. If 2,700 banks are putting their core operations onto Ancino, aren't we just trading the friction of legacy systems for massive vendor lock-in? Like, why wouldn't a modern bank just use a microservices approach?

SPEAKER_00 3:35

Aaron Powell What do you mean by that exactly?

SPEAKER_01 3:37

Aaron Powell You know, string together specialized apps, have one really nimble app for mortgages, a different one for compliance, another for small business lending, wouldn't that be safer?

SPEAKER_00 3:47

Well, banks actually tried that approach for the better part of a decade.

SPEAKER_01 3:50

Oh, really?

SPEAKER_00 3:51

Yeah. And it created an absolute nightmare of fragmented data silos. Think about it. If your mortgage app doesn't natively speak to your compliance app or your core ledger, your data is just tracked.

SPEAKER_01 4:03

Ah, I see.

SPEAKER_00 4:04

The argument De Valenzuela makes for a unified cloud native architecture is what he calls the virtuous cycle. When you have a unified platform, the data flows freely across all lines of business.

SPEAKER_01 4:16

Because it's all in one ecosystem.

SPEAKER_00 4:18

Right. And because it is cloud native, banks aren't waiting for physical server upgrades to get new features.

SPEAKER_01 4:24

So instead of a bank's IT department spending like six months installing an update locally on their own hardware, Osino innovates on their end and the improvement is pushed out universally.

SPEAKER_00 4:35

Universally and instantaneously. That's the key. When an update or a new compliance module is deployed, it hits the entire customer base simultaneously. Wow. Every single enhancement benefits the global community of banks using the platform in real time.

SPEAKER_01 4:49

Okay, but how do you push a universal update to a global community that has radically different needs? Like De Valenzuela manages EMEA. That is a massive, highly diverse slice of the planet.

SPEAKER_00 4:59

It really is.

SPEAKER_01 4:59

A universal update sounds great until you realize a bank in Frankfurt operates completely differently than a bank in Dubai.

SPEAKER_00 5:06

That's a great point. And the piece breaks this down beautifully. The regional priorities across EMEA are wildly different.

SPEAKER_01 5:13

Aaron Powell Give me an example.

SPEAKER_00 5:15

Well, in Europe, the focus is entirely on efficiency, straight through processing, and navigating this notoriously dense regulatory landscape. It is all about strict optimization.

SPEAKER_01 5:27

Aaron Powell Right, heavy compliance.

SPEAKER_00 5:29

Yeah, exactly. But in the Middle East, the dynamic shifts completely. The banking sector there is hyper-focused on aggressive market expansion and capturing rapid wealth generation.

SPEAKER_01 5:39

Okay, so totally different goals. And what about Africa?

SPEAKER_00 5:42

In Africa, the primary driver is financial inclusion. They're building innovative digital channels to bring vast unbanked populations into the formal economy.

SPEAKER_01 5:51

It sounds like a universal power adapter.

SPEAKER_00 5:53

How so?

SPEAKER_01 5:54

You know, you carry this one block in your suitcase, and whether you plug it into a socket in London, Tokyo, or Johannesburg, it works. The local voltage is different, the wall shape is different, the economic reality is different, but the adapter dynamically adjusts and delivers a unified stream of power.

SPEAKER_00 6:09

That's a really good way to look at it.

SPEAKER_01 6:11

It's basically globally scalable, but locally compliant.

SPEAKER_00 6:16

It's a strong analogy, but I'd add a crucial caveat.

SPEAKER_01 6:19

Okay.

SPEAKER_00 6:20

A standard adapter just passively passes electricity. This platform actively normalizes the data flowing through it.

SPEAKER_01 6:28

Interesting.

SPEAKER_00 6:28

Because despite those varying regional goals, the underlying mechanical need for every bank is exactly the same. They all have to manage the client credit lifecycle.

SPEAKER_01 6:38

Right. The basics don't change.

SPEAKER_00 6:40

Exactly. They all prospect, onboard, originate loans, manage portfolios, and generate compliance reports. By unifying those core mechanics in the cloud, you create the foundation for the next phase of the revolution.

SPEAKER_01 6:53

The cloud gathered and structured the data.

SPEAKER_00 6:55

Right. And now artificial intelligence is weaponizing it.

SPEAKER_01 6:58

And the timeline for that weaponization is moving incredibly fast. The banking tech space is full of buzzwords right now: embedded finance, open banking, hyperpersonalization.

SPEAKER_00 7:07

So many buzzwords.

SPEAKER_01 7:08

But De Valenzuela is adamant that AI is the undisputed heavyweight champion here. And its moment is not five years from now, it is right now.

SPEAKER_00 7:15

Aaron Powell The urgency is very real. But um, so is the danger. There is a massive paradox lurking in this AI gold rush.

SPEAKER_01 7:25

What's that?

SPEAKER_00 7:25

The threat of generic AI. I mean, generic off-the-shelf AI models promise incredible operational efficiency, right?

SPEAKER_01 7:32

Sure.

SPEAKER_00 7:33

But if every major bank buys the exact same generic AI to optimize their risk assessments and their loan approvals, they are all utilizing the exact same logic. Trevor Burrus, Jr.

SPEAKER_01 7:44

Right. They just commoditize themselves.

SPEAKER_00 7:46

Exactly.

SPEAKER_01 7:46

Here's where it gets really interesting, because we see this in everyday life. If I use ChatGPT to write my resume and you use it to write your resume, they both sound perfectly professional, but they sound identical. Aaron Powell Yep.

SPEAKER_00 7:57

The personality is gone.

SPEAKER_01 7:58

The distinct edge is completely washed out. So if a bank uses generic AI, how do they avoid stripping away their unique institutional risk appetite and just turning into a sterile, identical robot?

SPEAKER_00 8:08

Aaron Powell That is the multi-trillion dollar question. If everyone has the same algorithm, the competitive advantage drops to zero.

SPEAKER_01 8:15

Aaron Powell So what's the fix?

SPEAKER_00 8:16

Aaron Powell Well, what's fascinating here is Encino's strategy to prevent this commoditization. It relies on what they call their collective intelligence layer. And they build this using three specific advantages.

SPEAKER_01 8:26

Okay, what's the first one?

SPEAKER_00 8:28

First, they have the largest banking data set in the industry, backed by 14 years of moving global customers to the cloud.

SPEAKER_01 8:34

14 years of data is massive.

SPEAKER_00 8:35

Aaron Ross Powell Huge. Second is operations data value. They aren't just training models on generic financial theory, they are training them on actual human workflows.

SPEAKER_01 8:45

Aaron Powell So like how people actually do the job.

SPEAKER_00 8:49

Right. And the complex exceptions that define real-world banking. Because banking isn't just about applying a rigid rule, right? Trevor Burrus, Jr.

SPEAKER_01 8:56

Right. It's about a human relationship manager knowing when to make an exception to the rule for a high value client.

SPEAKER_00 9:01

Aaron Powell Exactly. And that leads to the third advantage: the unified data model. How do you take unstructured workflow data from a bank in London and a bank in Nairobi and make it universally instructive without linking sensitive information?

SPEAKER_01 9:15

Aaron Powell That sounds nearly impossible.

SPEAKER_00 9:17

Aaron Powell But because Encino has spent over a decade migrating banks onto this single platform, they have incredibly clean, normalized data across all lines of business. Oh wow. So when you combine the historical depth, the operational workflow knowledge, and the normalized data, this collective intelligence layer eliminates 90% of the undifferentiated heavy lifting for the bank.

SPEAKER_01 9:40

So by offloading the 90% of banking that is just standardized, boring paperwork, the bank can take its human capital and focus it entirely on the 10% that makes them unique?

SPEAKER_00 9:50

Precisely. There's specific market strategy and human relationships.

SPEAKER_01 9:54

That makes total sense.

SPEAKER_00 9:55

And we are seeing the tangible results of that shift right now. Internally, Encino has integrated AI into every facet of their own organization. Every single team uses prompts and AI tools daily to build products.

SPEAKER_01 10:08

Okay, but what about externally? What about the actual banks?

SPEAKER_00 10:10

Externally, the impact on the bankers using the platform is staggering. Bankers are saving eight to ten hours every single week through these AI tools and process automation.

SPEAKER_01 10:18

Wait, really? Eight to ten hours? An entire workday handed back to them every single week.

SPEAKER_00 10:24

Every single week.

SPEAKER_01 10:25

But how does that actually manifest on the floor of the bank? Like what are these AI tools actually doing to save that much time?

SPEAKER_00 10:32

DeVell Anzuela outlines a three-phase evolution of this AI adoption. Phase one, which is where we have been recently, is about supercharging the staff.

SPEAKER_01 10:41

Okay.

SPEAKER_00 10:42

This involves targeted, predictive, and generative AI tools built on bank-specific policies. It is essentially intelligence customized to make the individual worker faster at their immediate task.

SPEAKER_01 10:53

It sounds like we're moving from AI as a calculator to AI as a co-pilot. In phase one, you ask the AI for a specific number or summary and it gives it to you.

SPEAKER_00 11:02

Exactly. But phase two is a much deeper integration.

SPEAKER_01 11:04

Which is happening now.

SPEAKER_00 11:05

Yes. Phase two is what is rolling out right now, and it is a fundamental paradigm shift. This phase is about supercharging strategy through agentic AI.

SPEAKER_01 11:14

Agentic AI.

SPEAKER_00 11:15

Right. We are moving beyond an AI that just generates text. Agentic AI involves intelligent agents that handle complete multi-step workflows through natural conversations.

SPEAKER_01 11:24

Explain the mechanics of that. How does an AI agent securely execute a multi-step workflow inside a highly regulated bank?

SPEAKER_00 11:33

Well, agentic AI doesn't just read data, it acts as a secure middleware. Let's say a banker types a natural language request to initiate a commercial loan.

SPEAKER_01 11:42

Just types it in like a chat.

SPEAKER_00 11:44

The AI parses that request, authenticates the banker's credentials via API gateways, queries the bank's legacy mainframe for the client's history, interfaces with external credit bureaus, and autonomously populates the complex risk assessment fields.

SPEAKER_01 11:59

That is insane.

SPEAKER_00 12:00

It moves the human from being a data entry processor to being an orchestrator of intelligent systems.

SPEAKER_01 12:05

It's like you tell the AI to fly the plane to London and it handles the flaps, the throttle, and the radio communications with air traffic control.

SPEAKER_00 12:12

That's exactly what it's doing.

SPEAKER_01 12:13

And that inevitably points to phase three. If phase two is the copilot, phase three must be about supercharging the bank's DNA with fully autonomous systems.

SPEAKER_00 12:22

You hit the nail on the head. That is the future state. Phase three leverages this AI native platform to build autonomous systems that execute a bank's unique strategy at massive scale.

SPEAKER_01 12:34

Wow.

SPEAKER_00 12:35

It is effectively creating an AI-powered version of the bank itself, operating within the strict guardrails set by the institution.

SPEAKER_01 12:42

Aaron Powell So what does this all mean for the human being sitting in the branch office? If phase two and phase three are giving a banker ten hours a week back and the AI is flying the plane, does the human element of banking just slowly disappear? Do they just go golf?

SPEAKER_00 12:57

No, it doesn't disappear, it elevates. The piece makes a strong argument that giving bankers that time back allows them to focus purely on growth and service. Okay. When a relationship manager isn't buried in data matching and compliance checklists, they can actually sit down with a small business owner, understand the nuance of their long-term vision, and structure a highly creative, personalized financial solution.

SPEAKER_01 13:18

So the technology absorbs the administrative burden so the human can focus on the relationship. I understand the theory, but let's look at the gritty reality of implementation here. How do you actually get massive, old school, deeply risk-averse financial institutions to adopt autonomous agentic AI?

SPEAKER_00 13:37

That's the real challenge. And they will rely on a massive partner ecosystem to do it. Encino uses a network of system integrators.

SPEAKER_01 13:46

Oh, okay.

SPEAKER_00 13:46

These partners take the core AI platform and configure it to meet the highly specific needs of the institution, whether they are dealing in SME, commercial, or corporate banking. Right. Because the platform is built to be flexible, this configuration phase is where banks lock in their differentiation while adhering to local regulatory frameworks. The partners basically use the platform as a transformation accelerator.

SPEAKER_01 14:09

Let me push back on that though, because using system integrators is notoriously slow and expensive in the enterprise software world.

SPEAKER_00 14:15

Sure, traditionally.

SPEAKER_01 14:16

Doesn't passing the configuration off to a third-party consulting firm completely kill the speed of light advantage of the cloud? How is this not just the same old consulting blow we've seen for decades?

SPEAKER_00 14:26

It is a totally fair question. But there is a massive difference between legacy customization and modern configuration.

SPEAKER_01 14:34

What's the difference?

SPEAKER_00 14:35

In the old days, integrators spent years writing millions of lines of custom code for a bank. And that instantly became obsolete legacy tech the moment it was deployed.

SPEAKER_01 14:45

Right, because it was hard-coded for that specific moment.

SPEAKER_00 14:48

Exactly. With a unified cloud platform, the integrators aren't writing custom-based code. They're toggling configurations, establishing API gateways, and mapping the bank's unique workflows onto a constantly updating foundation.

SPEAKER_01 15:02

Ah, so the core code remains standard and upgradable.

SPEAKER_00 15:05

Yes. The platform continues to evolve underneath them.

SPEAKER_01 15:08

That's brilliant. And looking forward, the product evolution here seems focused on three main pillars: deeper AI integration for automated decision making, expanded API connectivity, to let these cloud native agents seamlessly handshake with external fintech ecosystems and enhanced analytics.

SPEAKER_00 15:25

Right, turning operational data into actual competitive intelligence.

SPEAKER_01 15:28

But achieving all of that requires navigating the ultimate bank tech challenge. The fundamental friction in this industry is the absolute conflict between the speed of innovation and the non-negotiable demand for stability and security.

SPEAKER_00 15:42

If we connect this to the bigger picture, technology wants to move fast and iterate. But financial regulation moves at the speed of government and for a very good reason.

SPEAKER_01 15:52

Yeah, the old Silicon Valley motto, move fast and break things, is catastrophic in banking. If you break a photo sharing app, people are annoyed. If you break a bank's core system, people lose their life savings. Exactly. So how can a platform promise fully autonomous phase three AI systems when the demand for security is absolute?

SPEAKER_00 16:12

Well, legacy providers typically force banks to make a painful choice. You can either move fast or you can be secure. But you cannot be both. Right. The regulatory complexity across different global markets makes simultaneous speed and security incredibly difficult. The true value of a platform like this, and the reason it gains such massive market share, is that it acts as a shock absorber between those two conflicting forces.

SPEAKER_01 16:34

A shock absorber, I like that.

SPEAKER_00 16:36

It delivers continuous breakneck innovation through its cloud architecture, providing the speed. But because that architecture is built on a unified data model refined over 14 years of strict compliance experience, it maintains ironclad reliability.

SPEAKER_01 16:50

It is a fascinating balancing act. To bring this all together, we started by looking at a $3 trillion cloud engine and the sheer weight of the global economy resting on it. We navigated the vastly different realities of EMEA from strict European optimization to African financial inclusion and saw how one platform actively normalizes the data for all of them.

SPEAKER_00 17:13

It's a massive undertaking.

SPEAKER_01 17:15

And we dug into the real threat of generic AI commoditizing the banking sector and how normalized data creates a collective intelligence layer. And then we traced the evolution from basic AI tools that save bankers 10 hours a week all the way to the bleeding edge of agentic AI acting as secure middleware to execute multi-step workflows autonomously.

SPEAKER_00 17:34

And for you listening, the practical reality of this technological shift is happening right now. The next time you apply for a loan or open a business account or tap approve on a financial app, consider the invisible, highly complex AI ecosystem working behind the scenes.

SPEAKER_01 17:50

It's wild to think about.

SPEAKER_00 17:51

Your request is likely being parsed, authenticated, and processed by an intelligent agent, saving a human worker hours of administrative burden so they can actually focus on the human impact of your financial goals.

SPEAKER_01 18:02

It fundamentally changes how you view the digital infrastructure we take for granted, but it also leaves me with a lingering, slightly provocative thought to explore on your own.

SPEAKER_00 18:11

Oh, let's hear it.

SPEAKER_01 18:12

If phase three is fully realized, if we truly reach a point where autonomous, highly advanced AI systems are perfectly executing unique bank strategies at a massive scale, what happens when two of these AI-powered banks end up directly competing against each other for your business? Oh wow. Right. Does the global banking industry eventually just become a silent, microscopic war of algorithms, constantly optimizing and outmaneuvering one another in milliseconds? And if the patient running the marathon suddenly has a purely artificial nervous system running the show, what happens to the messy, intuitive, relationship driven foundation that the entire financial sector was built on in the first place? Something to keep in mind the next time you plug into the financial grid.