EP1006: No-code, white-label digital lending solutions

This interview highlights a successful partnership between Bluering and Housing Bank in Jordan, which resulted in a prestigious award for the rapid deployment of a corporate lending platform. CEO Fares Kobeissi explains that modern financial institutions require comprehensive digital solutions that automate the entire credit lifecycle while remaining flexible enough to integrate with existing systems. A key focus of the discussion is the importance of no-code technology, which allows banks to customize workflows and adapt to market changes without extensive technical overhead. The source also emphasizes the role of artificial intelligence and high-quality data in improving credit decisioning, reducing risks, and enhancing the user experience. Looking ahead, the company aims to innovate through open banking and specialized software designed for the microfinance sector. Ultimately, the text argues that banks must replace outdated legacy systems with agile, automated platforms to stay competitive in an evolving financial landscape.

Transcript

SPEAKER_00 0:00

Usually a medical diagnosis is, you know, pretty precise. Like if you break your arm, an x-ray shows a very clear, jagged white line.

SPEAKER_01 0:07

Right, exactly.

SPEAKER_00 0:08

The doctor just points at it and says, there's the problem. But uh if you want to understand why a billion-dollar corporate loan goes bad or why a local bakery can't get the capital to expand, that X-ray machine is just fundamentally broken. Trevor Burrus, Jr.

SPEAKER_01 0:21

Yeah, totally broken.

SPEAKER_00 0:22

Aaron Ross Powell When you look under the hood at how financial institutions actually decide to lend money, the mechanics are surprisingly murky and rigid and frankly slow.

SPEAKER_01 0:33

Aaron Powell Oh, it is a notoriously clunky infrastructure. I mean, we are talking about legacy technology that has, in many cases, been patched together over literally decades.

SPEAKER_00 0:42

Wow, decades. Yeah.

SPEAKER_01 0:44

And because credit is the absolute lifeblood of global finance, you know, that sluggishness creates a massive bottleneck for the entire economy.

SPEAKER_00 0:52

Aaron Powell, which is exactly what we are unpacking today. Welcome to the deep dive. We're looking at the invisible infrastructure of borrowing, and we're drawing from this highly revealing April 2025 interview in the IBS Sci FinTech Journal.

SPEAKER_01 1:04

Right, the interview with Ferris Cobesi.

SPEAKER_00 1:06

Yes. The chairman and CEO of a financial technology company called Blue Ring. And our mission for this deep dive is to look at this company not just as a software provider, but as a real case study for a massive paradigm shift. We want to understand how the deeply entrenched processes of borrowing money are being completely rewired by automation and artificial intelligence. And most importantly, why that matters to you, whether you're navigating a massive corporate merger or just trying to secure a small personal loan.

SPEAKER_01 1:39

And you know, the catalyst for this specific conversation is a rather unusual industry milestone that's mentioned in the source. Blue Ring recently won the award for most innovative use of process automation at the Global FinTech Innovation Awards. Now, obviously, industry awards happen every day.

SPEAKER_00 1:54

Sure, they do.

SPEAKER_01 1:55

But the reason they won is what really gives us a window into this technological shift. They completely overhauled the corporate lending platform for Jordan's housing bank.

SPEAKER_00 2:04

Which is a huge deal.

SPEAKER_01 2:05

Exactly. One of the most sophisticated financial institutions in the Middle East, and they executed the entire project in about nine months.

SPEAKER_00 2:12

Okay, let's unpack this. Let's unpack the mechanics of that because, you know, on the surface, nine months might sound like a long time to install some software. Trevor Burrus, Jr.

SPEAKER_01 2:20

Right. To the average person, sure. Trevor Burrus, Jr.

SPEAKER_00 2:22

But in the context of global banking, overhauling a major institution's entire credit system in under a year is practically light speed. It's like successfully swapping out the engine of a commercial airplane while it is in mid-flight.

SPEAKER_01 2:38

Aaron Powell It really is. I mean, to understand the friction here, you really have to look at what digital lending actually entails. It is arguably the single most complex process to automate in the entire banking sector.

SPEAKER_00 2:49

Aaron Powell The most complex.

SPEAKER_01 2:51

Well, absolutely. Because when a major bank issues a corporate loan, it is not a linear event at all. That single application triggers actions across multiple heavily siloed departments. Okay. You have origination, risk assessment, compliance, legal credit administration, and uh finally disbursement.

SPEAKER_00 3:09

And none of those departments naturally talk to each other.

SPEAKER_01 3:12

Exactly. They exist in completely different worlds. Each department has totally unique workflows. They require totally different reporting structures to satisfy different regulatory bodies.

SPEAKER_00 3:22

Oh, right. The regulators.

SPEAKER_01 3:24

Yeah. They have separate compliance checklists. And crucially, they all have completely different integration needs with the bank's older legacy core software.

SPEAKER_00 3:33

Aaron Powell That sounds like a nightmare.

SPEAKER_01 3:35

It is. Trying to get all those divergent departments speaking the exact same digital language, migrating decades of historical data and training the users all without breaking the bank's daily operations usually takes years.

SPEAKER_00 3:48

Years. Wow.

SPEAKER_01 3:49

Yeah. So a nine-month implementation is a severe disruption to the accepted timeline of banking technology.

SPEAKER_00 3:55

Which obviously forces us to ask how did they bypass that architectural bottleneck? And according to Kabisi in the interview, the key to this speed is something called no code technology.

SPEAKER_01 4:06

Right. No code.

SPEAKER_00 4:07

Now, I have to pause and push back on this premise because when the average person hears the phrase no code, they think of like simple drag and drop website builders.

SPEAKER_01 4:16

Sure, like the consumer stuff.

SPEAKER_00 4:17

Yeah. You know, the tools you use to set up a quick online portfolio or a neighborhood bakery site, it implies something lightweight and inherently limited. How does a massive financial institution dealing with billions of dollars rely on a no-code solution for its literal backbone?

SPEAKER_01 4:34

It sounds totally counterintuitive, I know. Yeah. But what's fascinating here is that no code in the realm of enterprise financial software means something entirely different than building a simple consumer website. Okay. Blueing's products are entirely no code. And we aren't just talking about basic data entry forms here. We are talking about their complex corporate workflows, their risk rating systems, and even their IFRS9 solutions.

SPEAKER_00 4:58

Okay, let's quickly define that for the listener. Uh IFRS9 is the International Financial Reporting Standard 9.

SPEAKER_01 5:04

Right.

SPEAKER_00 5:04

It's essentially a highly complex, very math-heavy accounting standard that dictates exactly how banks have to calculate and report their expected credit losses. Like it's serious heavy-duty compliance.

SPEAKER_01 5:15

Precisely. It requires intense mathematical modeling. So in this context, no code isn't about making the software simple. It is entirely about agility.

SPEAKER_00 5:24

Agility, right.

SPEAKER_01 5:26

Historically, if a bank wanted to change its credit policy, let's say the central bank suddenly issues a new regulation, or the bank decides to adjust its risk appetite because the commercial real estate market is getting shaky.

SPEAKER_00 5:39

Which happens all the time.

SPEAKER_01 5:40

Right. How do they adapt? Historically, they would have to hire software engineers to physically go in and rewrite the underlying code of their lending platform.

SPEAKER_00 5:49

It's like realizing your living room is too cold. But instead of just turning a dial on a thermostat, you have to tear down the physical walls of your house, reroute the internal wiring, pour new concrete, and just hope you didn't accidentally cut the plumbing in the process.

SPEAKER_01 6:02

That is the perfect analogy. Legacy code is exactly like a house made of solid concrete. Any change requires jackhammers and months of labor. And after the engineer has finished rewriting the code, the bank still has to run massive, highly expensive testing phases to ensure the new code didn't break the compliance reporting in, say, the legal department.

SPEAKER_00 6:21

Right, because of the silos.

SPEAKER_01 6:23

Exactly. But with an enterprise no code platform, the system is more like a house made of perfectly engineered Lego bricks. I love that. The underlying architecture is already built and tested. The system is parameterizable.

SPEAKER_00 6:37

Meaning the bank's own risk managers and business analysts can just snap the pieces into different configurations.

SPEAKER_01 6:42

Exactly.

SPEAKER_00 6:43

They can customize how the software behaves, adapt to new credit policies, and change reporting structures through a visual user interface. They never actually touch the underlying code.

SPEAKER_01 6:54

Right. And that eliminates the need for an army of developers every single time the market shifts. It drastically reduces the total cost of ownership for the bank.

SPEAKER_00 7:03

That makes total sense.

SPEAKER_01 7:03

And mechanically, that is exactly how you accelerate deployment time exponentially. That parameterization is how you achieve a nine-month overhaul at an institution like Jordan's Housing Bank.

SPEAKER_00 7:14

And this is why you, the listener, should actually care about back office banking software. Because markets evolve rapidly. Customer expectations change overnight. If a bank system is made of concrete, it can't adapt to you. If a new economic crisis hits and the government announces a new type of relief loan, a hard-coded bank might take six months just to process the application.

SPEAKER_01 7:36

Right. By which point it's too late.

SPEAKER_00 7:38

Exactly. Yeah. No code agility means banks can actually keep pace with the real economy.

unknown 7:43

Trevor Burrus, Jr.

SPEAKER_01 7:43

But you know, an agile engine is completely useless if you put the wrong fuel into it.

SPEAKER_00 7:48

Ooh, good point.

SPEAKER_01 7:49

And in the world of credit decisioning, that fuel is data. The most perfectly designed, rapidly deployed workflow won't help you if the information it is processing is flawed, outdated, or incomplete. Trevor Burrus, Jr.

SPEAKER_00 8:02

Right. If you build the fastest pipeline in the world, but you pump sludge through it, you still just have sludge.

SPEAKER_01 8:08

Exactly.

SPEAKER_00 8:09

Which brings us to the actual data these systems are analyzing. And the source material gets very specific about how Blue Ring approaches this, and it involves a deep dive into artificial intelligence.

SPEAKER_01 8:19

Yeah, this is key.

SPEAKER_00 8:20

Kobisi explains that their systems gather data on borrowers across all possible channels, but they don't just look at structured data. Here's where it gets really interesting.

SPEAKER_01 8:29

Aaron Powell Right, because structured data is what we traditionally associate with thinking. It is the neat, highly organized spreadsheets. It's your debt-to-income ratio, your tax returns, your historical payment record. It's data that fits perfectly into rows and columns.

SPEAKER_00 8:45

It's essentially the resume. It gives you the bullet points of a borrower's financial history.

SPEAKER_01 8:49

Exactly, the resume.

SPEAKER_00 8:50

But Blue Ring's technology also heavily processes unstructured data. If structured data is the resume, analyzing unstructured data is like reading the borrower's entire biography.

SPEAKER_01 9:00

I like that framing.

SPEAKER_00 9:01

We are talking about text documents, emails, market reports, news articles, maybe even behavioral patterns. But practically speaking, how does a system actually make sense of that? Like how does an algorithm read an email and decide if someone is good for a loan?

SPEAKER_01 9:17

Well, this is where artificial intelligence moves from just being a buzzword to a highly functional tool. Okay. The system uses AI, specifically natural language processing and machine learning algorithms, to do the heavy lifting that human analysts simply cannot do at scale.

SPEAKER_00 9:32

Because there's just too much of it.

SPEAKER_01 9:34

Right. The volume is insane. For example, let's say a corporate borrower submits their pristine quarterly spreadsheet, the structured data. Okay. A human analyst looks at it and it seems totally fine. But the AI is simultaneously scanning unstructured data, like recent supply chain news reports or logistics emails.

SPEAKER_00 9:54

And it spots a correlation.

SPEAKER_01 9:56

Exactly. The AI might notice a consistent 15% delay in shipping times from the borrower's primary vendor over the last three weeks. Oh wow. Or it might flag a subtle shift in the tone of industry reports regarding that specific sector. It synthesizes that unstructured text and flags a severe cash flow risk before that risk ever actually shows up on a quarterly balance sheet.

SPEAKER_00 10:18

Aaron Powell That is wild. So it's essentially predicting the future state of the borrower's wallet.

SPEAKER_01 10:22

Yeah, pretty much.

SPEAKER_00 10:24

And the ultimate goal here, the primary metric that banks really care about, is what Kobisi refers to as NPL's non-performing loans. These are loans where the borrower has simply stopped making payments.

SPEAKER_01 10:34

Right, the bad loans.

SPEAKER_00 10:35

By using AI to constantly ingest and analyze that unstructured biography, the system maximizes the accuracy of risk identification, which directly decreases those non-performing loans.

SPEAKER_01 10:49

And this isn't just some experimental theory. The source points out a major validation of this technology. Blue Ring's risk rating platform is actually the underlying engine for SP global rating models.

SPEAKER_00 11:01

Wait. SP global. As in the massive financial intelligence company that essentially grades the health of global markets.

SPEAKER_01 11:08

The very same.

SPEAKER_00 11:09

That is a serious endorsement.

SPEAKER_01 11:11

It is the absolute gold standard. By serving as the engine for those models, Blue Ring is pairing its state-of-the-art credit automation software with world-class rating methodologies. Right. The AI's ability to digest unstructured data and spot those hidden correlations is trusted at the very highest levels of global finance.

SPEAKER_00 11:28

Okay, I understand the efficiency here, but I really have to play devil's advocate for a second. Whenever we start talking about an AI vacuuming up unstructured data, reading emails, scanning news reports, analyzing behavior to determine if I am trustworthy enough to get a loan that sounds, frankly, incredibly invasive.

SPEAKER_01 11:47

Yeah, I get that.

SPEAKER_00 11:48

Credit data is already deeply personal. If this system is building a comprehensive financial biography, how on earth is that data actually protected?

SPEAKER_01 11:56

It's a vital concern. And honestly, it's a structural challenge for the entire industry. Cobisi acknowledges directly that credit-related data is universally recognized as one of the most sensitive types of data in existence.

SPEAKER_00 12:08

I'd imagine so.

SPEAKER_01 12:09

Global frameworks like the GDPR laws in Europe govern this information with extreme strictness. If a bank accidentally leaks this data, the penalties and the loss of public trust are just catastrophic.

SPEAKER_00 12:22

So how do they handle the infrastructure to prevent that? Because putting all this sensitive unstructured stuff on a public cloud seems super risky.

SPEAKER_01 12:28

That's precisely the point. To handle this sensitivity, Bluring does not force banks into a generic multi-tenant public cloud environment. Instead, their solutions are intentionally designed to be deployed either entirely on-premise, meaning the software physically lives on the bank's own internal servers. Trevor Burrus, Jr.

SPEAKER_00 12:46

Like in their own building.

SPEAKER_01 12:47

Exactly. Or within the bank's highly secured, heavily encrypted private cloud.

SPEAKER_00 12:53

Aaron Ross Powell So the data never actually leaves the bank's physical or digital walls. Right. The AI is doing its unstructured reading inside a locked vault, basically, adhering entirely to the specific security policies of the individual bank and the local central bank regulations.

SPEAKER_01 13:09

Aaron Powell Correct. They completely isolate the intelligence engine within the bank's existing security perimeter.

SPEAKER_00 13:15

Trevor Burrus Okay, so we have this picture of a highly secure, AI-driven, no-code engine that is overhauling massive institutions like Jordan's Housing Bank. And while Cobesi mentions that they design solutions for SMEs, which are small and medium enterprises, it's easy to assume this level of high-end SP validated technology is still just a luxury for the big players.

SPEAKER_01 13:37

Right. That's the common assumption.

SPEAKER_00 13:38

Does this actually trickle down?

SPEAKER_01 13:39

Aaron Powell If we connect this to the bigger picture, if we look at the full scope of their product line, we see a really deliberate effort to push this technology across the entire financial spectrum.

SPEAKER_00 13:51

Okay, how so?

SPEAKER_01 13:52

Well, they design solutions for financial institutions, non-bank financial institutions, traditional retail banking, and perhaps most interestingly, the microfinance sector.

SPEAKER_00 14:01

Aaron Powell Microfinance is just fascinating. For some context for you, microfinance institutions provide very small loans, sometimes just fifty or a hundred dollars, to individuals who completely lack access to traditional banking.

SPEAKER_01 14:15

Right.

SPEAKER_00 14:15

This is a booming sector, particularly in developing nations and emerging markets, where that small amount of capital can buy, you know, the seeds for a farm or the sewing machine to start a local tailoring business.

SPEAKER_01 14:27

And operating a microfinance institution is mechanically very different from running a corporate bank.

SPEAKER_00 14:32

I would assume so.

SPEAKER_01 14:33

Yeah. The data structures are entirely unconventional. I mean, a rural farmer doesn't have a W-2 tax form or a structured credit history. Right. Their unstructured data might be things like mobile phone top-up histories or local weather patterns. Furthermore, the volume of transactions is massive, but the dollar amount of each transaction is tiny.

SPEAKER_00 14:53

Which brings up a massive friction point. The unit economics.

SPEAKER_01 14:57

Exactly.

SPEAKER_00 14:58

If a farmer needs a $50 loan, the administrative cost of assessing their risk, processing the paperwork, and disproofing the funds cannot be forty dollars.

SPEAKER_01 15:08

Right. The math just doesn't work.

SPEAKER_00 15:10

Traditional legacy systems are just way too expensive to run at that scale. Which is why my favorite detail in this interview isn't actually a software feature, it's a business decision.

SPEAKER_01 15:20

The pricing model.

SPEAKER_00 15:21

Exactly. Blue Reading actively adjusts its own pricing scheme specifically for microfinance institutions.

SPEAKER_01 15:27

Yeah, it's brilliant.

SPEAKER_00 15:28

They deliberately change their revenue model to ensure this high-end AI-driven technology is actually affordable for microlenders. They aren't just stripping features away to make a cheap version. They are fundamentally altering the economics of their software to democratize access to capital.

SPEAKER_01 15:44

It is a really profound example of how technological efficiency directly enables financial inclusion. By drastically lowering the cost of origination and risk assessment through automation, the unit economics suddenly makes sense.

SPEAKER_00 15:58

It's viable.

SPEAKER_01 15:59

Exactly. Capital can finally flow into markets, communities, and asset classes that traditional banks previously considered, well, far too expensive or mathematically risky to serve.

SPEAKER_00 16:09

That is so cool. So we've mapped the current landscape. We've gone from the macro scale of corporate overhauls in Jordan to the micro scale of affordable lending in emerging markets. But the underlying tech landscape isn't static. If this parameterizable AI-driven system is solving so many current bottlenecks, what is breaking next? Why are they already building the next iteration?

SPEAKER_01 16:32

Well, because the environment around the software is evolving just as fast, Cabesi notes that they operate on a strict cycle, continuously upgrading their systems every two years.

SPEAKER_00 16:40

Every two years, wow.

SPEAKER_01 16:41

Yeah. They are currently developing what they call version seven of their solutions.

SPEAKER_00 16:45

Aaron Powell And the features listed for version seven are honestly extensive. Yeah. They are integrating new AI functionality at multiple layers of the software, rolling out a brand new user experience, developing a dedicated mobile app for the bank's users, and introducing this concept called database independency.

SPEAKER_01 17:04

Right.

SPEAKER_00 17:05

Let's pause there for a second, mechanically. What does database independency actually mean for a bank?

SPEAKER_01 17:10

So in traditional software development, an application is often hard-coded to work with one specific type of underlying database, say an Oracle database or a Microsoft SQL server. Right. If a bank decides they want to switch their database vendor to save money or improve performance, they are trapped. They would have to rewrite the entire lending application to communicate with the new database.

SPEAKER_00 17:32

Which goes back to the jackhammer analogy.

SPEAKER_01 17:34

Exactly. But database independency means the Blue Ring software sits above that layer. The bank can swap out the foundational database infrastructure beneath it without rewriting the application, completely eliminating vendor lock-in.

SPEAKER_00 17:46

Wow. So it's just another layer of agility? But stepping back, this raises an important question. What are the broader industry forces demanding this rapid two-year upgrade cycle? Cobasi identifies two specific drivers for the immediate future. Right. The first, unsurprisingly, is the relentless evolution of AI. The predictive capabilities of these algorithms are advancing so rapidly that if your lending platform isn't constantly updating its AI layers, you are actively falling behind in risk management.

SPEAKER_01 18:18

It's an arms race, really.

SPEAKER_00 18:20

It is. But the second driver he mentions is a massive structural shift. Open banking.

SPEAKER_01 18:25

Oh, open banking is arguably the most transformative shift in financial data architecture of the decade.

SPEAKER_00 18:30

Absolutely. And I'll try to explain this without sounding like a textbook. Historically, your financial data, your transaction history, your direct deposits, your bill payments has been trapped inside a vault at your specific bank.

SPEAKER_01 18:40

Right, locked away.

SPEAKER_00 18:41

Open banking shatters that vault. It is a regulatory and technological framework that requires banks to allow secure third-party access to your data through APIs, application, programming interfaces. So with your permission, a totally different financial app or competing lender can securely plug into your bank, read your data, and offer you a better loan rate, your data actually becomes portable.

SPEAKER_01 19:05

Exactly. And Cobisi points out that open banking is currently being widely adopted across several countries in the Middle East. For a fintech company focused on credit risk, this represents a monumental opportunity.

SPEAKER_00 19:17

Oh, for sure.

SPEAKER_01 19:18

Because suddenly that pool of fuel we talked about earlier, the structured and unstructured data, is vast, interconnected, and instantly accessible. Right. When data moves fluidly between institutions via APIs, an AI-driven lending platform can synthesize a borrower's complete financial reality with just unprecedented speed and accuracy.

SPEAKER_00 19:40

And to facilitate that speed, Blue Ring is also preparing to launch a SOS offering software as a service, which will allow these systems to be deployed in cloud native environments even faster for institutions that don't require that strict on-premise lockdown.

SPEAKER_01 19:54

Right.

SPEAKER_00 19:54

The stakes here for the banks themselves are completely existential. Kobisi summarizes the industry's reality with a quote that feels almost like an ultimatum.

SPEAKER_01 20:03

Yeah, it's pretty direct.

SPEAKER_00 20:04

He says Legacy credit systems can no longer support the evolving needs of modern banks. Institutions must adopt AI-driven end-to-end digital lending solutions to remain competitive and future ready.

SPEAKER_01 20:18

It is a stark reality. I mean, lending is a core revenue driver for financial institutions.

SPEAKER_00 20:23

It's a whole business.

SPEAKER_01 20:24

Right. If a bank's engine is built of that rigid concrete, relying on manual data entry and human analysts trying to spot correlations in a sea of unstructured data, they simply will not survive. Yeah. They won't be able to process the data fast enough to capitalize on open banking, and they won't be accurate enough to avoid massive losses when the market inevitably shifts.

SPEAKER_00 20:45

So what does this all mean? Synthesizing everything we've covered today, we started by looking at a we explored how AI acts as an incredibly powerful reading engine, trusted by SP Global, to scan our unstructured financial biographies and spot risks before they happen.

SPEAKER_01 21:00

Yeah, it's amazing.

SPEAKER_00 21:02

And we saw how adjusting the core business model can take that sophisticated analysis and make it economically viable for microfinance in emerging markets.

SPEAKER_01 21:11

Which is so important.

SPEAKER_00 21:12

It really is. While terms like IFRS9 compliance and database independency sound like totally dry back office jargon, they're actually the foundational mechanics of our society's invisible infrastructure.

SPEAKER_01 21:25

Absolutely.

SPEAKER_00 21:25

It's the plumbing. The efficiency of that software directly dictates whether a local entrepreneur gets the funds to hire five new people or whether a community can access the capital required to build.

SPEAKER_01 21:36

It is the invisible infrastructure of opportunity.

SPEAKER_00 21:39

Yeah.

SPEAKER_01 21:39

But as we look toward version seven and the widespread adoption of open banking, the nature of that infrastructure is fundamentally shifting, which leaves us with a pretty profound realization to ponder.

SPEAKER_00 21:50

Okay, what's that?

SPEAKER_01 21:51

As artificial intelligence solidifies its role as the ultimate gatekeeper of credit, capable of reading vast oceans of our unstructured data to mathematically determine our trustworthiness, the dynamic between a borrower and a lender changes completely. Oh, wow. In an era where open banking ensures all your financial data is fluid and connected, we really have to ask ourselves: what story is your digital footprint actually telling the algorithms? Right. And more importantly, are we ready to navigate a world where an AI system might map our habits, understand our risks, and calculate our financial reliability far better than we know ourselves?

SPEAKER_00 22:28

Oh man. That is a haunting and totally fascinating thought to end on. If you're swapping up the engine of the airplane mid flight, you really have to hope the new AI autopilot has a clear map.

SPEAKER_01 22:39

Exactly.

SPEAKER_00 22:40

Thank you for joining us on this deep dive into the rewiring of global finance. Keep questioning the invisible systems around you, and we'll see you next time.