Victor Swezey 0:02
Hello, and welcome to a special edition of the buzz, a bank automation news podcast. Today is August 2 2023. My name is Victor Swezey. And I’m the editorial intern at Bank Automation News. Today is the last episode of our global startup cities series, where we have taken you to some of the most innovative tech hubs around the world to give you a look at these startup cultures and the markets they serve. Along the way, we’ve talked to FinTech founders, from the cities about the products they’re bringing to market. On this final episode, we’re bringing you back to Toronto to get a look inside Canada’s startup capital just over the border. We’ll be talking about the immigrant experience in Toronto, the collaborative ethos shared by Canadian founders, and some of the resources that have grown in the city to support them. Joining me today is the co founder of uplinq a startup using AI and alternative datasets to help financial institutions lend to small and medium sized businesses. Please welcome Ron Benegbi.Ron Benegbi 1:12
Yeah, sure, a so first of all, Victor, thanks so much for having me excited to be here. Like you said, I’m founder and CEO of uplinq in a sentence, we are a credit decisioning support technology for small business lenders. So in English, what that means is we provide institutions that lend money to small business, a lot of data and a lot of insight to help support their evaluation process and their credit adjudication process. And ultimately, though, the decision is still stays with the, with the lender, but we we support them. So a little bit about me. I’m Cyril founder, fifth startup, by the way, I’ve been told it’s my last startup, so very excited about that. But really, more importantly, as I’m an immigrant, and my family migrated to Canada in the early 70s, we were poor. We had no money. My dad was baking bread at night, to put food on the table for our family. And he went to a bank in 1973. And I know I’m dating myself a little bit, because I look exceptionally young. I was around in 73. And he asked the banker for a small business loan. And the banker told them Look, Mr. Bernanke, you really don’t qualify for how the bank lends to small business. However, I believe in people. And here’s $5,000. And my dad was able to take $5,000.19 73 start a small business, which turned into a medium sized business over time. And that really became the springboard the backbone for our family’s lives and in a new country. And I, I share that because that that really correlates directly to your question. I’ve grown up in a small business family, my successes, and my failures have come as a small business owner. So it uplink, our mission is to work with lenders and through the use of data to the use of science. And some pretty sophisticated techniques, provide them the information they need to help them extend additional working capital into the hands of small business. So in other words, say yes, when they were initially going to say no. So it is a very personal and meaningful story for me, Victor, I mean, small businesses always been underserved in financial services, no one would argue that, but if you look at the impact that COVID had on small business owners all over the world. And now if you look at the impact that, you know, the economy’s having, and we’re in this sort of uncertain times, whether some days we’re in a recession, other days, we’re not access to fair and ethical credit, has never been more difficult for a small business owner to obtain. So if we can just help turn a few nose into yeses, we would really be serving our purposes.Victor Swezey 4:19
Let’s dive in maybe on a on a technical level, a little more into how uplinks credit decisioning process actually works, we’d love to hear more about what kind of alternative data sources you use, maybe some of your most unique types of categories of data that you pull from, and you know, any use cases and ways that AI and machine learning might be involved in your credit decisioning process. I think our listeners would be really interested in that as well.Ron Benegbi 4:43
In terms of alternative data. Here’s how I would I would I would talk about this, you know for years and going back to when my dad was applying for a loan lenders would evaluate a small business the same way. Give me your For financial records, let me pull some type of credit score on you. And then from that I’ll make a credit decision. Well, that’s a very antiquated way of thinking about credit, especially in today’s day and age where the profile or the DNA of the small business owner has changed significantly over the last few years. So, you know, a lot of new small businesses have cropped up, a lot of these small businesses are sort of, you know, sort of in the gig economy, so to speak, they don’t have established financials or credit reports, and ultimately, they’re gonna, they’re set up for failure. So when we talk about alternative data, what we present to a lender is, we allow them to evaluate the entire ecosystem of the business itself. And look at all that information in context, meaning environmental data, community data, market information, data, all of these different types of data sources, in combination with traditional financials and credit scores. I’m not, you know, I’m not trying to downgrade or poopoo credit scores. But if you look at them in concert with all of these other macro and micro economic types of data sources, then you as a lender have a much better perspective on the true health of the business. So, you know, you ask the question, well, like so what are you talking about? Well, it can be things like cell phone data, it can be traffic information, it could be information from governmental sources, like, you know, the US Bureau of Labor, or the Census Bureau or Department of Housing or Department of Commerce and an on and on and on. I mean, in some cases, we actually use data that we acquire from a NASA feed of looking at satellite imageries sure, because there are all kinds of small business operators out there, it’s not just tech. So it’s, what we do is we tap into all of these sources, but we don’t just dump it on a lender, because at the end of the day lender won’t know what to do with it. We crystallize it for them, we leverage the years of experience and insights that we’ve garnered from the programs our customers have utilized over that time. And ultimately, we make a recommendation and we provide it the recommendation in a very, very detailed manner as to why we think this is a good or a bad loan. And ultimately, though that decision does stay stay with the lender. So that’s a little bit about what we’re doing and how we do it. I hope I answered your few questions. But if I missed one, just fired over? No,
Victor Swezey 8:05
absolutely. I really appreciate that. And, you know, you really piqued my interest with some with the traffic data and the NASA Data. Can you tell me a little bit more specific use case for how that might be relevant in?
Ron Benegbi 8:19
Yeah, I mean, if you if you Well, if you look at traffic data, so let’s say you’re a restaurant. Well, that’s really, really important. If we can get information about traffic flow and patterns in your specific neighborhood. That’s a really important piece of information to determine what, you know, potential future performance could look like beyond just again, traditional financials and Bureau scores. If you look at like things like I use satellite imagery, people love that. So I’ll give you a use case. So let’s say you’re a manufacturer, and you’re applying for a loan with a bank. And you’re telling the bank, listen, we run seven days a week, we’re running night shifts, because this is where we’re manufacturing this widget, whatever the widget is, well, if we have access to satellite imagery, that can then capture sort of heat patterns and heat signals over your location. And we noticed that on the weekend, it’s like there’s nothing there. But during the week, at during these hours, we’re getting different types of readings. Well, we know that they’re fibbing or they’re stretching the truth a little bit. So those are the kinds of things that the system can look at and intelligently and this is where, you know, leveraging different AI techniques helps us develop models that ultimately attenuate directly to the lender, but also specifically to the applicant itself. And that’s something that is a true point of differentiation for us against others.
Victor Swezey 9:58
And tell me about Some of the banks that you that you partner with who are some of the lenders that you use your data to advise,
Ron Benegbi 10:06
right now where we are with our business is we are in heavy proof of concept mode, with a number of banks all over the world. And we typically take that approach first, because it’s a pretty big deal when you’re going to a lender, and even though we’re not making the decision for them, you’re talking about potentially transforming their loan book, in which case, you’ve got risk, you’ve got compliance, you’ve got it security, you’ve got the business itself, all have to kind of look at this. So you know, the, the proof of concept or POC approach, like try before you buy, has resonated very well. So right now we’re working with two of the large to the top five banks in Canada, we’re working with to top 20 small business lenders in the US, we’re working with one in Mexico, we’re working with a couple in Africa, and I’m hoping to be able to share that, you know, by as early as you know, next month, we can add Hong Kong and India to that list as well. So, you know, it’s it’s, it’s a global approach in terms of we can help anyone who’s lending the small business, and anyone who wants to make some type of meaningful impact on their loan book,
Victor Swezey 11:30
in the spirit of comparing Canada and the US. Maybe if we could zoom out a little bit and compare the startup cultures in Toronto to to, you know, some of the other startup hubs around the world, maybe take Silicon Valley in the US and London? What makes Toronto unique?
Ron Benegbi 11:49
Yeah, well, you know, it’s hard for me to answer that just because I’m, I don’t know what the startup culture in Silicon Valley is like, or it isn’t Israel, or it is in London, but, you know, as far as Toronto goes, you know, I can I can talk to that it’s, it’s certainly what I feel, is a tight knit community where anyone kind of in this community is open to helping one another, there’s sort of a pay it forward mentality here that I’d like to think exists within Toronto. Yeah, I mean, the community itself has grown substantially over the years, especially in FinTech and especially with the organizations that support technology here, in Toronto. So I would tell you that, you know, you can, if you want to, you could probably attend some sort of tech event, whether virtually or in person, just about every night of the week, here in Toronto, there’s always something going on, and being a pretty large Metropolis onto its own, you’ve got some, you’ve got some great entrepreneurs in here. And, and, and a big reason for that is because, you know, Toronto has always been known as fairly diverse, and multicultural, and you have a lot of different ethnicities and immigrants like myself, and my family, who have come at one point from a different country. And you know, many of them have decided to, you know, go into the startup world. So it’s great, because we get to meet different different people from different cultures, different perspectives, and they certainly bring that added element to the entrepreneurial world. And I can tell you, it’s exciting. Like I’ve, I’ve made a lot of friends just being in the community. Not necessarily by working with these companies, but just like I said, bumping into them in advance, whether it be in person, or you know, you’re at as sort of a zoom seminar and you see them in you know, people start talking and then you, you reach out. So overall, I would tell you that look, it’s a it’s a great place to be. It’s a big city, but it feels like it in many ways it feels like a small town and that that’s how I would describe Toronto in my in my from my view.
Victor Swezey 14:20
Can you tell us a little bit about maybe how Toronto became the startup hub that it is now?
Ron Benegbi 14:26
Yeah, I mean, I would tell you that I think Toronto really started to take shape as a tech hub in the kind of early to mid 2000s. I will tell you that. A big a big jumping stone is an organization called Mars. And no, it’s not the planet and it’s not the chocolate bar company. Mars is an innovation ecosystem. I like to think of it as almost as a platform to which it It has four different tracks, like different types of startups, like clean tech, digital health, enterprise software, and fintech. And it supports these ventures through different programs that originally were government funded both federally and provincially. But over time, as you know, government funded funding naturally declined or has gotten more difficult to obtain corporate sponsorship really stepped in. So I think Mars has played a critical role in the in the ecosystem, and has grown has helped grow and develop that ecosystem over time. There are other organizations that have also played a big role. The one, the one that really resonates with me is an organization called Tech to start by an individual named Alex Norman, probably sort of Mr. Tech Canada, if I would describe Alex but it started off as a kind of a small community gathering, trying to help a few startups and all of a sudden tech to has grown into Montreal, you know, Montreal tech, and Vancouver tech. And really, it’s a, it’s a community for all startups in Canada, it’s a it’s a Canadian community, and they host a bunch of different events, both in person and online. Newsletters go out a couple times a week, you know, a lot of a lot of a lot of information has garnered from them. And then accordingly, you know, there’s a lot of, there’s some really good media focus specifically in Toronto, probably the most prominent one is organization called beta kit, which everyone kind of defers to as the sort of the go to go to source for information on all things tech in Canada. And then there are a few technology writers as well that are very well known. So, you know, over time, it has really, really grown. And as more venture capital dollars, started to enter the ecosystem, both from Canadian firms as well as US firms. And I can tell you, there are a lot of US firms who invest in Canadian companies and Toronto based companies. And I’m proud to say that most of our investors that are actually American, really helped the community grow and flourish and become what I believe is a top 20 tech community globally, as ranked by different startup reports out there. So I hope that answers your questions. I’m sure there are a lot of other great communities out there as well.
Victor Swezey 17:56
Definitely, definitely. And that’s really exciting to see. And, you know, looking forward, I guess, with with, with all that momentum, what are some fintechs that you think we should be watching coming out of Toronto?
Ron Benegbi 18:08
Yeah, I mean, there’s a lot of I think there’s just a lot of great companies, there’s, there’s one that you know, pops into my head, called lat Li, they’re, they’re sort of a hybrid FinTech kind of Prop tech. But they’re doing some really exciting things with respect to real estate, and trying to help you, you as a potential homeowner, get access to your first home. And I think that is a really, really big problem. It’s certainly a huge problem in Toronto. And I can tell you, as a father of like, she’s not a millennial, she’s a Gen Zed. It’s just really, really hard to like, buy your first home. And, and I’m pretty sure that other markets here in Canada, they’re experiencing the same thing. So they’re doing some really exciting and creative things around how they use financing to help these individuals get access to real estate that they can own. There’s also a really interesting company, sort of in the FinTech InsurTech space called walnut, which is doing some really cool things around embedded insurance and insurance again, is another problematic area where you know, rates are kind of like rates and access to fair and market market value policies are, are tough to get especially for startups and especially for fintechs. So, you know, so that companies wall not so those are the two that kind of dropped off by head but certainly there’s there’s quite a few and, you know, we’re all kind of trying to take it one day at a time. I’m in grind it out. So, you know, hopefully many, many will succeed.
Victor Swezey 20:08
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