Credit scoring fair and efficient with founder and CEO Ron Benegbi (Canada)

Speaker A: Foreign. Voice of fintech.

Speaker B: Welcome to Voice of Fintech, a podcast mapping out the Swiss and global fintech scene. Connecting fintech enthusiasts with startups, incubators, accelerators, business angels and VCs and incumbents interested in partnerships. Voice of Fintech will help you navigate the fintech ecosystem. Here you can listen to the startup founder stories, what investors and incumbents are looking for when dealing with startups, and find out more about resources provided by incubators and accelerators. My name is Rudy Falad and I'll be hosting this podcast. Hello and welcome to Voice of fintech. Today we're going to talk to Ron. And we're going to talk to Ron because he's a founder of Uplink, which is a another solution, uh, that helps you to, uh, assess better credit worthiness of people, is based in the US we'll find out more about this. So credit scoring, better credit scoring than what we have today. And how do we do it? Why do we do it? Let's find out more from Ron. Can you tell us about yourself? What led you to become an entrepreneur? Why aren't you working in the corporate? What's exciting about being an entrepreneur?

Speaker A: You know, it's funny you say that. I've always said that if I'm someone who's unhirable, meaning, you know, I can get hired by a corporate bank and within 30 days they probably fire me. I guess it's just a DNA thing. Watching my parents basically growing up in a small business environment, watching them both build successfully and non successfully small businesses all my life has really embedded in, in myself the DNA of, you know, wanting to go out there, work hard, roll up the sleeves, do what I need to do to basically be my own boss and basically build a company from scratch.

Speaker B: All right, sounds great. Now that's a good point. Unhirable. Therefore, you need to hire yourself or you build the business that would hire you. That's right. So that's one reason that led you to start your own business, a personal one. But let's maybe then jump forward and talk about what is Uplink, what's the problem that you're solving or what's the opportunity that you are capitalizing on? Are you a kind of a pain reliever or uh, are you a delight startup?

Speaker A: No. So we're a pain reliever. What we do is we work with, we're focused on small business lending. And what we do is we provide a very sophisticated solution. Working with, let's say, banks who lend money to small business, that helps them better evaluate risk and ultimately the credit worthiness of a small business owner. And, you know, the problem we're solving is small business owners, they have trouble accessing capital, at least fair capital, affordable capital. And this isn't anything new, but it's been an ongoing problem for many, many years with respect to trying to access this fair and affordable capital with banks. And what we've done is we've created a solution that gives the bank an opportunity to better assess the credit worthiness of a small business owner, but still staying within their comfort zone. And it's been, you know, you know, it's been looked at very, very favorably by a number of banks all over the world. And we're excited about the impact that we have been making and we can possibly make going forward with respect to small business lending.

Speaker B: I see. All right, so let's just make sure that we all understand it. So you're providing solutions to banks to assess the credit worthiness, of SMEs, SMBs, et cetera. Right. Okay. So, uh, I mean, it's been difficult to assess credit worthiness in many countries. Right. And in America, you have a system for consumer credit. In other countries, it's kind of like either or, you know, I'm based in Switzerland. If you're on the list of people who didn't pay something, then your life is over. But there is no, uh, you know, grading in terms of how good or bad your credit is. It's either that you are a problem or you're not. But for, uh, SMEs, there are financials out there. Maybe they are worse than for publicly traded companies in terms of quality or access, etc. But there is information about you. So there have been solutions out there, you know, done in Bread street even, and people like that who were telling you, maybe not about lending or credit worthiness, but can I deal with these? With this business? Is it a, uh, vendor that I can trust? Right. So when you compare it to these existing solutions, where do you stand out? What's the angle and where exactly is the problem other than, yes, we need to assess it and maybe the process right now it's cumbersome and manual.

Speaker A: Yeah, for sure. Well, you're right in saying there are solutions out there. I mean, every bank we work with, whether the bank is in Japan, whether it's in the US Whether it's in Chile, has what they call their own internal scoring system. So every bank has created a set of rules and guidelines about how to score an applicant. And what I'll tell you, Rudy, is pretty Much every bank we work with also uses a third party of some kind. So you mentioned Dun and Bradstreet. Absolutely. Credit reporting agencies like Equifax, TransUnion, Experian, all of that is there. But what makes us different? Well, for one, we don't try to replace anything a bank is doing today. We tell them whatever you're doing, however you're assessing the risk, great, keep doing what you're doing. What we do is we bring two very important things to the table that the banks don't necessarily have or are, uh, able to utilize today. And the first is data. We have access to a lot of data. And what I mean by that is our technology is connected to over 10,000 unique data sources in 150 countries worldwide. So on average we would manage anywhere between 1 to 2 billion different data series in a local market, I.e. switzerland, Canada, Australia, et cetera. And we're able to present that to the bank in a highly crystallized way where they can understand what this data means and use the data in conjunction with what they're doing today. And when I say in conjunction, it's primarily what we call alternative data. So of course, every small business has, a small business has their own sort of financials that they're self reporting into an accounting system. Banks, like, like I said earlier, have their own internal scorecards. They use third party, so they use data from those sources. And we're bringing all of this new data to the bank in a very crystallized way where we can overlay that with their existing data and then leverage all of that into a much more effective and much more accurate scoring system. And the results we've seen so far to date, just in proof of concepts that we've done with banks, are increasing, let's say loan approval rates anywhere between 40 to 70% of previous and things like increasing net credit margin into the tens of millions, in some cases hundreds of millions of dollars for a bank. So that's what we bring to the table. And like I said, it's not a way to replace what a bank is doing today. It simply complements what they are doing today. So the banks have been fairly receptive to this approach.

Speaker B: All right, understood. Now what kind of data points are we talking about? Can you give us some examples?

Speaker A: Yeah. So I mean, again, traditional data points, you mentioned it earlier. Things like financials, of course there are tax statements, there's, you know, credit statements, things like that, credit bureaus. But the types of data we bring, the alternative data are things like government statistics, department of Labor, Department of Commerce, Department of Trade. We look at economic and market oriented data points. So you know, currency markets, commodity markets, if it's a retail environment, we'll look at things like cell phone data, we'll look know traffic data and we look at different data points that ultimately tie into what we call the ecosystem of that small business, making sure it's relevant to that business. Because every small business is unique. And then we understand through the history of what we've done over the last X number of years, we've which data attributes weigh and value differently in terms of the scoring and are ultimately more predictive. And ultimately that translates into how we help the bank score specific credit application. So it's a lot of data that while a bank can access access it themselves, they don't necessarily know what to do with it when they get it. And that's what we bring to the table.

Speaker B: All right, so how long have you been providing these solutions? So because you mentioned the approval rates, if I were a bit fastitious I could say, well you can approve anybody, you don't need an algorithm for this. But what you want to do is approve them without compromising on the credit risk worthiness of your portfolio. Right. So you want people to be to paying you in the end, paying you back. So what are the results after I assume a few years of using these algorithms, how predict how good were your predictions so far?

Speaker A: So it's a very unique approach what we've done Rudy. And, and if you recall earlier I said we, we don't try to change what a bank is doing. We work within their process and part of that process is their existing credit comfort zone. So the credit rules they have, the policies and procedures they have around the evaluation of credit and they of course share that information with us. So to answer your question, how long have we been doing it? Well, we're a bit of an unusual story and I don't want to use the word unique because I believe every startup and every fintech startup is unique in its own right. God bless us all. It's, you know, we're all trying to build unique solutions but it is an unusual story. And what I mean by that is we actually took a technology that my co founder had developed in a different company, uh, technology that had been in market for over 15 years and maybe most importantly a uh, technology that had enabled lenders to lend over US $1.5 trillion in new loans during that 15 year window. And what we did was we repurposed it into a modern day Fintech that we call uplink. So Uplink as a company is relatively young. I mean as a company we're, we're four years old. So we're not that, we're not that old. But as a technology we're now, you know, the technology has now been in market for almost close to 20 years. So we're able to establish a great amount of credibility as a fintech startup walking through the door on day one. And that gives us an advantage that, you know, most startups don't have. And that's why I tell you it's an uh, unusual story. So I hope that answered your questions. But to give you an example of some of the numbers we've seen, we've seen approval rates, like I said, increase by as much as 40, 50, 70%. We've seen losses decrease anywhere between, you know, 100 to 400%. And ultimately we've seen credit margin increases by as much as 50 to 60%. So you know, initially the results have been very, very powerful. We've started because we are a, uh, relatively newer company with proof of concepts with different banks in different regions all over the world. We work through both a direct model and through a partnership model where we have partners all over the world bringing our solution to different banks. And that's enabled us to ultimately conduct a lot of these POCs and a number of them are now in the process of converting to full scale live production environments starting in 2026. So I hope that answers your question.

Speaker B: Yeah, yeah, great. So it's not about increasing the approval rate only, but it's also about cutting the losses down, the credit losses down. You also see improvements in net interest margin. Right. So these are the results. Uh, it looks like the algorithm works. Obviously when we talk about algorithms these days, we also say that sometimes they're kind of self learning. Right. This is not rule based, traditional AI anymore, things like this. So how do you improve? If you just reflect on your journey, you say, okay, we have all these data points, we look at them, we test them, I guess which ones are uh, of a more predictive value than others? Maybe there are some data points we think would have a predictive value, then we can find out that they don't. Can you give us some examples? How do you go about this? Of course, it's all about fine tuning all the time. Right. So are you working on this in real time or almost in real time or is it embedded in your solution or how does it work?

Speaker A: Yeah, I mean that's a great, that's A great point. It's a great question. And again, if I go back to what I said just a few minutes ago, the advantage and what has gotten a lot of these banks excited about working with us is the fact that while we are uh, a newer company and banks today, banks are very conservative. It doesn't matter whether you're a bank in Switzerland or in Japan or in the US Banks by their nature are very, very conservative. So as a new fintech startup going into a bank, the issue of credibility is always first and forefront. So where we've had this advantage is the fact that while we are a new company, our technology has almost 20 years of real life, real world experience. So it's not that we've hired some really smart people, PhDs, put them in a basement, built some AI, this, AI that model, and come out after a couple years with a, uh, PR narrative around that. That's, that's not who we are. Maybe it's somebody else, but it's not our story. Our story is the fact that we've been able to take a technology that has real world experience, that has seen different programs in different markets under different market conditions and it has learned over almost a 20 year window, which is a fair amount of time and. Excuse me, and is able to take that and now correlate that to a more predictive methodology that candidly, very few others can offer a bank. So it comes down to experience, it comes down to the different data points that we've been able to leverage, uh, almost over 20 years and ultimately uh, it comes down to being able to prove to the specific bank we're talking about. And that's why we always do, Rudy, a proof of concept because we always go into every relationship on day one and we say we're not asking you to believe us today, regardless of our track record, we don't think you should believe us. What we'd like to do is spend the next four to six months in, in this proof of concept environment, letting us prove ourselves to you that what we're saying can actually come to fruition and is actually true. And that approach has given these very conservative and you know, risk averse financial institutions the comfort level they need to get excited about working with a relatively younger company.

Speaker B: All right, I hear you, but just please give me a couple of examples of the data point that works and maybe another one that didn't work as well. Yeah, so I, from um, you know, predictive perspective.

Speaker A: Yeah, I mean there's, there isn't a one size fits all. I'LL use one specific data point that my co founder, who I, uh, compare him to John, if you've ever seen the movie A Beautiful Mind about John Nash. Yeah, I mean, he is really the reincarnation of John Nash. He is really an elite mathematician. He uses actually a very unique data reference point that he's seen over the last 10 or so years have a predictive element to it that very few organizations would understand. And actually, when he told me this initially, I, I kind of, I actually laughed because as a layman it's like, you got to be kidding me. And what that data point is, is for last number of years they have now we have started pulling information directly through an API with NASA. So think about NASA. We've linked our tech directly to a NASA API that has all of these satellites running around the world taking these basically real time images and snapshots on heat signals. And we've been able to utilize that data point to really predict the operational efficiency and ultimately how long a business is actually in operation at a moment in time. Because, you know, when banks lend to small business, they're not lending just to startups, they're lending to chemical manufacturers, are lending to, you know, all different types of businesses. And you know, a number of banks had indicated that, oh yeah, these banks are saying they're running seven days a week, you know, 24 hours a day. And this type of information has proven to be very useful. Now, on its own, could we just use that to come up with a predictive algorithm that truly is accurate in terms of representing the right type of financial performance for business over a certain amount of time? Absolutely. Not on its own, it's not strong enough. But in the context of, um, overlaying other data points and billions of data series, it presents a very meaningful value. So that's just one example. And candidly, I'm sharing that example with you because my partner has this passion for this one data attribute and he shares it with just about every bank we speak to. And every time he shares it, I have this smile on my face because I go, wow, it's incredible. Uh, but he, he's able to quantify that ultimately. And again, it's part of the puzzle. It's not the entire puzzle.

Speaker B: Okay, all right, great stuff. So let's now turn over to you. So how do you make money as a business?

Speaker A: So, you know, we have a very simple business model. It's actually something a bank is very familiar with because they live it day to day. It's actually very similar to that of a credit, credit bureau. So it's transaction oriented, it's pay on, pay on use. So every time a bank wants a score or wants an assessment, they, they, they do an API call to our system and we charge them a transaction fee on it. Very similar. In fact, it's the exact same structure as what a typical credit bureau or credit agency charges a bank. So it's something that they're very familiar with, it's something that they're very comfortable with because again, they're very risk averse. They don't like a lot of new things entering their environment. So we've tried to create an approach that works within a banking environment. Uh, so that's our business model. Simple.

Speaker B: All right, absolutely. So that's clear. And we, uh, talked about banks being your clients. What kind of banks are we talking about?

Speaker A: Awesome. Um, you know, we think our sweet spot would be sort of what we would call certainly in the US and Canada, a regional or a super regional bank. But candidly, we've seen some of the larger banks as well, you know, be interested. So what I would tell you, Rudy, is, uh, you know, on the low side, it's a bank that would have, let's say, minimum 15 billion in terms of assets under management anywhere up to some of the banks we're working with are actually in the trillions. And that's unusual because there aren't too many banks like that. But as an example, we just finished a proof of concept with one of the 10 largest banks in the world. They're based out of Japan. And we did that through a partnership that we have globally with Visa, who brought us into that relationship. We went through the POC very successfully. We're now in phase two of a working environment with the bank and hopefully we'll get into a production oriented relationship with them. But like I said, it's anywhere from a smaller bank to a very large bank and anything in between. We believe our sweet spot are sort of the, the mid sized banks, anywhere between kind of 100 to 300 billion. But we've seen banks of all sizes, like I said, smaller than that and larger than that. Get excited about working with us.

Speaker B: All right, wonderful. And you, um, kind of mentioned that you've been around with this technology for decades. Ah. Even though the company is in its current form, you know, uh, active in for four years or so.

Speaker A: Yeah.

Speaker B: But when you come back to the beginning of your venture, how did you get started? How did you get your first 100 clients or.

Speaker A: Well, yeah, yeah, yeah. Well, I'll Tell you how I got started in Uplink. You know, I actually grew up as an immigrant. I'm actually based in Canada. So we're actually a Canadian incorporated company with a very large, large footprint in the US of course, but we have two main offices, one in Toronto and the other in Atlanta. And, and uh, I tell you this because I actually my, my family and I came to Canada many, many years ago in the. I know that's hard to believe Rudy, because I sound so young and by the way, I appreciate you saying that, but we were poor. My dad was baking bread at night. He went to a Bank in 1973 to apply for a small business loan. And the banker told them, Look, Mr. M. Bennett, you don't qualify for how we as a bank lend to small business. However, I see something in you and I believe in people and I'm going to give you $5,000. My dad was able to take that money, $5,000 in 1973, start a very small business which eventually grew and evolved into a larger business. And I really became the springboarder, the opportunity our family needed to be successful in, in a brand new country. And I share that with you because going back to your question, it was early 2021 and I knew what I wanted to do was find a solution that I could take to any small business lender, whether it be a bank, a credit union, an alternative online lender, but any organization that lends to small business and allow them to look beyond traditional data. Right, you talked about that earlier. Credit scores, credit bureau information, traditional financials, but allow them to really dig into the, the true, the true environment of that small business and bring that to the lender in this very holistic way. And I wasn't able to find anything like that. And I knew to build something like that from scratch would be very, very expensive, would take a lot of time and when we went into a bank we wouldn't have the credibility. So that's when I got lucky and I met my co founder and he had organically built this solution over a long period of time and we were able to take that and repurpose it and bring it to these banks. So we had that element of credibility on day one that most fintech startups do not have that advantage of. And it's still even with that element of credibility because we're selling into a bank and because involves credit decisioning, even though we don't make the decision. And credit decisioning ties into the regulatory environment, it's a very long sales cycle that's very painful, regardless of how wonderful you are. So, you know, that's given us a big advantage. That's taken time. But over the last two years, I would tell you our business has really taken off. We're currently working with four of the top 20 US banks. We're currently working with three of the top six Canadian, uh, banks, Canadian lenders. We have, which is highly unusual, exclusive and preferred contractual relationships globally with Both Visa and MasterCard around the world as each one's respective small business scoring technology. And what that means in English is they bring us into their banking customers and they say, this is our partner. And if you're looking for better underwriting solutions for small business, we stand behind them. And that's incredibly powerful because I mentioned earlier, we just concluded a, uh, POC with one of the 10 largest banks in the world based out of Japan. Well, we did that through Visa, and there's no way on earth we could have had that opportunity by just cold calling or knocking on their doors. So through Visa, we've had opportunities for end mastercard for projects in markets like Japan, Malaysia, Chile, Colombia, soon to be Mexico, and of course we have our own coverage in North America. So I hope, I'm sorry if I was a little long winded, but it's, you know, when you're selling to a bank and you're selling something that really impacts credit decisioning and ties into the regulatory environment, you better be prepared, you better have some tough mental resolve to you, you better be patient, you better have capital and just be prepared to wait because, uh, it's a long sales.

Speaker B: Absolutely. I've heard that from many founders.

Speaker A: Yes, that's true.

Speaker B: All right, so before we go, what are the next milestones ahead of you? Whether this year or next year?

Speaker A: Yeah, I mean, our milestones right now where we've gotten to the point of maturity where we can very easily crystallize our milestones. Rudy. So we have a number of POCs that have recently concluded, or they're on the verge of concluding, some are on the verge of starting. And really our milestones starting for 26, because we're pretty much there, right, is to have two to three live production. So. So POCs are not live, they're historical back tests against data. But our next milestone is really to have two to three live production paying customers in market in 2026. We're well on our way to achieving that. But we feel that if we can get to that milestone of 2 to 3, we should be by the end of 2026. So call it a year from now, plus or minus, in a position to have a run rate where we as a company are now profitable, which, which would be the ideal state for the business.

Speaker B: Absolutely. So, great stuff. So, before we go, I just wanted to ask you a very simple question. How can people reach out to you? And, uh, what kind of people would you like to hear from most?

Speaker A: Yeah, well, first of all, I've always told people this. I'm the easiest person in the world to get a hold of, by the way. I'm. I'm very easy to reach. First of all, there's only one ron Benegby on LinkedIn in the world. So if you Google my name, you're going to get us. You can certainly reach us through our website, which is uplink co, not.com.co so up l I n q.co or you can just reach out to me on LinkedIn. Like I said, I'm, um, the only Ron Benegby in the world. So I think you'll, you'll have an easy time finding me and send me a message. I'm online LinkedIn all the time and in terms of the types of people that, you know, want to reach out to us. So anywhere from a lender. So if you lend money to small business to a potential partner. So if you are, uh, some sort of technology that sells into a lender who lends to small business and you're looking for a way to add value to your current offering, or you're looking for a way to help your customer underwrite more successfully small business. We're very open to the partnership model, as we've done with both Visa and MasterCard. So I would tell you again, if you're a potential customer, potential partner, or if you're just interested in credit scoring on its own, you know, we'd love to have a conversation with, with you because it's something we're very passionate about and we care deeply about. Not just to make a few bucks, but also to really, really help small business and really help lenders extend as much working capital into local communities around the world as possible.

Speaker B: Great stuff, Ron. So thank you so much and good luck to you and Uplink.

Speaker A: Well, thank you for having me, Rudy. It's been a pleasure and, uh, I look forward to, you know, engaging with you down the road. Thank you.

Speaker B: Thank you for listening to Voice of Fintech podcast. If you haven't already, check out also voiceofintech.com where you will find all the episodes and additional resources related to the podcast. You can Also subscribe to Voice of Fintech on Apple Podcasts, Spotify, Google, or any other podcast app that you like. If you have any suggestions on the topics or guests or how to make this podcast better for you, please email us at infoors of fintech.com Happy to hear from you. Thank you.