Episode #238: Mark Straub, Smile Identity, “Face Recognition Is, In A Sense, Deep Technology”

Episode #238: Mark Straub, Smile Identity, “Face Recognition Is, In A Sense, Deep Technology”

 

 

 

 

Guest: Mark Straub is co-founder and CEO of Smile Identity, a universal KYC enrollment and authentication solution for enterprises in mobile-first economies.

Date Recorded: 6/25/2020

Run-Time: 1:04:22

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Summary: In episode 238 we welcome our guest, Mark Straub, co-founder and CEO of Smile Identity. In today’s episode, we’re talking user authentication and biometric checks.

We kick off the episode with some observations from Mark about the evolution of startup fundraising as he’s traveled the world. We get into some of the efforts that have been in place by organizations in emerging economies to prevent fraud, and some of the issues that have resulted as a byproduct.

We discuss the backstory of Smile Identity and the current use case for Smile Identity’s technology. We dive into the nuts and bolts of challenges faced by face recognition technology, and the thought and work involved to implement and train algorithms for biometric checks.

All this and more in episode 238 with Mark Straub.

Links from the Episode:

  • 0:40 – Intro
  • 1:26 – Welcome to our guest, Mark Straub
  • 6:17 – The early part of Mark’s career
  • 9:20 – Overview of frontier markets
  • 13:44 – Genesis of the idea that became Smile Identity
  • 22:47 – Moving from idea to implementation
  • 30:10 – Where the tech giants stand with facial recognition
  • 38:02 – How people at the margin are getting impacted
  • 41:18 – Business model
  • 43:56 – How the pandemic is impacting Smile Identity’s business
  • 47:40 – The economic opportunity in Africa
  • 51:39 – Planning a trip to Africa
  • 54:55 – Most memorable moment in building the company
  • 56:59 – Outlook for the company
  • 59:27 – Most memorable investment
  • 1:02:54 – How to connect with Mark: @smileidentity, smileidentity.com, LinkedIn, @markStraub, kenya@smileidentity.com, Ghana@smileidentity.com

 

Transcript of Episode 238:

Welcome Message: Welcome to “Meb Faber Show” where the focus is on helping you grow and preserve your wealth. Join us as we discuss the craft of investing and uncover new and profitable ideas, all to help you grow wealthier and wiser. Better-investing starts here.

Disclaimer: Meb Faber is the co-founder and Chief Investment Officer at Cambria Investment Management. Due to industry regulations, he will not discuss any of Cambria’s funds on this podcast. All opinions expressed by podcast participants are solely their own opinions and do not reflect the opinion of Cambria Investment Management or its affiliates. For more information, visit cambriainvestments.com

Meb: Hey, podcast listeners. Super fun show for you today. Our guest is co-founder and CEO of Smile Identity, an African-focused identity validation solution for companies in mobile-first economies. In today’s episode, we’re talking user authentication and biometric checks. We kick off the episode with some observations on startup fundraising in emerging markets as our guest has travelled, and lived, and invested all over the world. We chat preventing fraud in emerging markets and discuss the backstory of Smile Identity. We then dive into the challenge and pitfalls many of these big tech companies like Amazon and Microsoft are experiencing with facial recognition technology offerings. We then chat how best to train algorithms for biometric checks using computer vision and machine learning, and some of the pitfalls there. Please enjoy this episode with Smile identity’s Mark Straub. Mark, welcome to the show.

Mark: Thanks, Meb. It’s great to be here.

Meb: Tell our audience where he is.

Mark: So, I’m actually at home, probably like most of your listeners. And I live in Oakland, California, although I’ve travelled quite a bit in the last few years building my company. Basically, the last four months, I’ve been stuck here, sheltering in place and working obviously through Zoom, and Slack, and all the other tools that we’ve got as a team. It’s been an interesting way of working. And what we found as a team actually is that not that much has changed in terms of what we do day to day. I just have to get up pretty early.

Meb: Yeah. Well, you have the honour of being a fellow Wahoo and I’ve never bought an expensive pair of sneakers. I’m not a sneakerhead. And I actually did an interview a few years ago, kind of, admonishing people that were investing in these very expensive Yeezys and shoes. But I saw, since you’re a fellow Virginia grad, Nike released a special champs edition Dunk shoe and, of course, they sold out immediately, that are orange and blue, Virginia colours from being basketball champs. And I’m really tempted to buy my first pair of shoes off StockX, but they’re like $250 and I’m a cheap bastard. So, I can’t quite bring myself to that. What, your UVA? What did you study?

Mark: I was 2004. Actually, I just wanna touch on that shoe thing for a second. Because it’s funny. I’m not a big consumer goods guy. I don’t buy that many things. My wife can tell you when there’s packages that arrive, it’s always for her. However, I have recently found an e-commerce passion, which is I buy these shoes made in Kenya as I’m sure we’ll get into… I do a lot of work in Africa, travel quite a bit across the continent, at least prior to COVID. And one of the things, I mean, Nike is a great, sort of, example. You know, Nike is all about great athletes and champions.

And if you think about the world of marathoning and running, which, you know, I’m a runner and many of the athletes that win most of the world’s top marathons and long-distance running, they’re often Kenyan and there’s a culture and a community around running in Kenya that’s huge. But for a long time, there was no, sort of, Kenyan brands or Kenyan products. You had Kenyans coming out, signing deals with Nike or Reebok, whoever. So, a couple of entrepreneurs, I think an American and a Kenyan, got together and built this new shoe company called Enda and it’s an awesome running shoe. It’s actually the only shoe I wear now besides flip flops. And they come in very bright, colourful varieties and styles. So, definitely check them out.

Meb: How do you spell that?

Mark: It’s called Enda, E-N-D-A.

Meb: Okay.

Mark: They’re now starting to get, of course, endorsements from all the Kenyan runners. I think Lupita Nyong’o also has a pair. So, anyway, yeah, it’s a cool company, great product. I think they cost about 100 bucks. That’s really good value.

Meb: Cool. Oh, I love it. You also have the somewhat dubious honour of being, I think, the only podcast guest in 200-plus episodes that we’ve lived in the same apartment. Is that right? We didn’t cross over at the same time we weren’t roommates.

Mark: That’s right.

Meb: Didn’t we cohabitate in the same place in San Francisco?

Mark: Occasionally, in the middle of the night, I would see the ghost of Meb Faber getting something from the refrigerator.

Meb: Were you upstairs or downstairs?

Mark: I was upstairs. Yeah. I think I had the corner unit without much light, but yeah. It was good. It was good while it lasted.

Meb: I managed to… the podcast listeners have heard this, but I time my move to San Francisco at the worst possible time in the history, which was directly after the dot-com bust. And like many broke 20-something-year-olds, I inhabited, I think what was a two-bedroom apartment with at least four people in it. And I lived in a converted, I don’t even know how to describe it, basement, but…

Mark: Yes. I remember that basement.

Meb: …but was…

Mark: It was low ceiling. I’ll put it that way. I don’t remember exactly how tall you are but probably wasn’t that comfortable.

Meb: It was a big room, but my favourite part of this was that I had installed… listeners, give me credit for this because this was early 2000. I installed voice-activated lights but the problem with where I installed them when they were, kind of, around the corner. So, I was in bed and you could program them to be any phrases you want. But I came with a default. It was like, “Lights off.” And so, when I’d be going to bed, I’d be screaming, “Lights off.” And then anytime my roommates wanted to mess with me, they would just wait till I was asleep and just scream, “Lights on,” and my lights would come the middle of night. Anyway, I miss San Francisco.

Mark: I have a toddler. I have a toddler now who would absolutely love that game. When we’re trying to get ready for bed, we do lights on, lights off with, like, hand gestures. He’s starting to learn the words and sometimes he’ll just sit there and he’ll want to turn the light on and off, on and off, five minutes. I’m sure he would have enjoyed that, playing that game with you.

Meb: Yeah. Okay. So, we’re gonna talk all the things emerging markets, Africa, startups. But let’s talk a little bit about your background because I think it informs your trajectory into what you’re doing now. Tell me a little bit about your path. You spent quite a bit of time in some top-tier big-name venture capital beginnings, right?

Mark: Yeah, that’s right. So, probably, like a lot of young finance grads in, like, the early 2000s, I came to San Francisco to do investment banking. I got trained on Wall Street, but I really wanted to work more closely with technology companies. So, I came out to San Francisco after UVA and worked in technology investment banking. And I guess, one thing that’s been common in my career is I’ve always wanted to get closer and closer to the actual value creation. So, investment banking was a bit too abstract for me and I didn’t feel terribly great about making models 1% more efficient for very, very large convertible debt financing in the public markets. Not that there’s anything wrong with that, but it just didn’t speak to me.

So, I actually quit my job in investment banking. I went overseas in like 2006. I worked in microfinance in India, kinda, just around the time when Kiva was getting started and Muhammad Yunus got the Nobel Prize. So microfinance was this growing movement at the time. It was primarily, basically, satchels of cash, motorcycles, and people tracking payments in Excel and handbooks. And I learned as much as I could about that, but I got to the point where I felt like what I really needed to get back into was technology if I was gonna try to make a difference.

So, I came back to the Valley and actually ended up joining a large venture capital firm and worked in venture capital first focused on the U.S. at that firm, and then eventually in another firm that I helped get started that was focused on emerging markets. So, I managed a fund called Khosla Impact for about five years along with a small team. And that was Vinod Khosla’s personal money we are investing in companies that were focused on emerging markets and particularly consumers in the bottom half of the world’s economic pyramid, so, affordable financial services, affordable healthcare education, energy as well.

My career has, kind of, span both Silicon Valley and technology businesses. I was fortunate enough to be part of teams that invested in companies like AdMoband Tesla and Twitter, but also, I’ve spent a lot of time investing in businesses doing pay-as-you-go solar energy for rural parts of Africa and India. And yeah, I guess this is, sort of, the duality of my interests. I believe in capitalism, I believe in technology, but I also think that some of the most exciting applications of those engines are at frontiers of the economic world.

So, yeah, that’s, kinda, been my path. And now, I’m the co-founder of my own company that’s focused on making it easy for people to prove their identity and get access to a modern visual lifestyle. And we are focused on sub-Saharan Africa and also believe that our solutions eventually will be useful beyond Africa in other frontier and emerging markets.

Meb: Yeah. I wanna drill down deep into a Smile here in a second. As you spend a lot of time in these frontier emerging markets, any general observations over that time you were travelling around the world, working with entrepreneurs and all sorts of different geographies, in various stages, economic development, any general observations, kinda, commentary on the status of what that world looks like and any just insights in general?

Mark: Yeah. I mean, I think the biggest, and maybe this is obvious for some people, but I don’t think it’s obvious to most Americans. We tend to be somewhat insular and our politics the last few years has reflected that to some extent, but I think the biggest insight to me was that all around the world, regardless of where people come from, fundamentally, they really want the same things. They want to live a better life than their parents did, they want their children to have a better life than they did. They want access to the modern lifestyle that we take for granted.
So, yes, that’s things like Instagram, and Twitter, and Facebook, but it’s also things like clean water, reliable electricity, and convenience, and good consumer products and services. So, I think that’s one thing that, regardless of where you go, how different either the cities look or the people look, fundamentally, people really do want a lot of the same things.

I think the other thing that was, kind of, interesting meeting with entrepreneurs, because they were my lens and my bias was often meeting with entrepreneurs, when I was writing checks, I would be one of the few guys that was going to places like Kampala, Uganda or Lagos, Nigeria and meeting with entrepreneurs, having coffee, and then, in some cases, writing cheques to support new businesses. And I think the big thing that I noticed that was different is that, of course, the skills, and the drive, and the passion were there the same as they are, let’s say, in the Valley, but the supporting ecosystems weren’t.

So, if you wanted to get started and build something, getting that first $25,000, at least a few years ago, in a place like Lagos, or a place like Nairobi, or even a while ago in places like Bangalore, was really hard and you might have to give 50% of your company up to your uncle or some other businessman that you may have come across who happen to have an extra 25k. So, that’s supporting ecosystem is just radically different than what you see in the Valley. And the penalties for failure have been social ostracization. So, that’s why in many cases, the people who did become entrepreneurs in some of these markets, at least the ones who were trying to build more than a small business just were incredibly passionate and driven to do this because it wasn’t easy.

Now, that started to change. So, we’ve seen Y Combinator has been pretty active in Africa, particularly in Nigeria. There’s a variety of other either seed funds, incubators, accelerators that have gotten some level of scale or traction. And so, it’s much easier these days, if you are a young aspiring entrepreneur or, let’s say, in Lagos or in Bangalore, or maybe at Jakarta to get started, get maybe your first 25, 50 or 150k, build something, demonstrate some value. And then there are now, kind of, a class of seed and early-stage funds that can support these entrepreneurs. Still nothing like Silicon Valley in terms of the support system, but it’s rapidly changing.

Meb: Thinking about entrepreneurs all around the world is you find that enterprising, brilliant people exist everywhere and we spend a lot of time preaching the global investing message on the public equity side, but I think this 21st century will be fun to watch a lot of the, sort of, private development in the U.S. with early-stage get its exposure elsewhere around the rest of world. I’m excited to watch.

Mark: I was gonna say one last thing on that is that capitalism wants a yield, right? Capital wants yield. And I think, as you’ve seen the world integrate, of course, we’ve seen the challenges of integration, things like public health challenges and some of the challenges with data privacy and data protection, which we can get into a little bit later, but one of the benefits of the integration of the world has been that capital can more easily flow to the places where it’s needed and where it’s appropriately used, and the beneficiaries of that have largely been entrepreneurs in emerging markets. So, that is one trend, I think one globalism trend that I am certainly excited and buy and believe in.

Meb: So, you’re, kinda, doing the VC angle, spend a lot of time with these entrepreneurs. What was the origin story genesis of the idea that became Smile?

Mark: Yeah. So, Smile Identity was started because I kept hearing the same story from a variety of entrepreneurs that I would either get to know or that, in some cases, I invested in and in particular, in a couple of markets in sub-Saharan Africa. And the story was one of either we have to put a lot of barriers up to slow down and prevent fraud. And those barriers would be manual checks, they might be agents who have to go visit a business, might be paperwork. Those barriers would inherently slow down the ability of companies, like lenders or digital banking startups, to be able to quickly acquire new customers or new merchants and onboard them.

The counterpoint to that was that if they didn’t put those barriers up, inevitably, what you’d see is probably would be low for some period of time, but then one person would get through, they’d share that news in Facebook or WhatsApp, and all of a sudden you’d see fraud rates for particular product go through the roof, 3% fraud becomes 9%, 9 becomes 25, and all of a sudden your loan portfolio is upside down.

And so, this common set of frustrations was something I would hear about with the financial services companies that I was looking at. And a lot of the companies that I ended up investing and working with we’re in financial services. They moved from being, originally, the original microfinance businesses that I talked about which were largely paper-based based and cash-based to, in the modern era now, this is all digital financial services or what we call FinTech. In Africa, it’s primarily things like mobile money accounts that are tied to your phone number, or even mobile banking accounts where you can move money fairly seamlessly between your phone number between a bank, between some other third-party wallet location.

So, these services have grown dramatically and they’ve solved a real pain point which is being able to move money quickly in small fractions at low costs between consumers, between businesses, across borders, between different currencies. And yet the fraud problem and the underlying regulations technology to make these things all work seamlessly and safely without money laundering or without terrorism finance, in many cases, it wasn’t there. And so, companies would have to build this stuff internally and everyone would build it a little bit differently.

And the aha moment for me was I actually was going back and forth between Mumbai and Nairobi. So, we had portfolio of investments in India, we had a portfolio in Kenya. And about somewhere around 10 years ago, the Indian government began building and rolling out this system called Aadhaar, which means foundation in Hindi. Aadhaar became this national ID system and it was the largest national ID system I think outside of China to use biometrics. Aadhaar was unique because it created a unique ID number for each person that was deduplicated based on your biometrics. So, in theory, you couldn’t really get two Aadhaar numbers.

Now, there’s some criticism of Aadhaar and there’s definitely problems with Aadhaar but it was a huge step, a giant leap forward in terms of being able to uniquely identify a consumer. And as a result, the regulations in India, which had previously prevented the transfer for cash out from mobile money accounts, changed. And all of a sudden, you had all of these companies, this, sort of, wave of new FinTechs that were growing up very quickly because they could now easily move money in and out of digital form and back into fiat, and the regulations would allow them to do that. Previously, the banking regulations didn’t allow that, but Aadhaar allowed the banks and the federal regulators, especially the national regulators, to get covered with this idea of cash in, cash out in and out of digital form.

Meanwhile, across the Arabian Sea in Kenya, mobile money had already been a thing for 10 years, but some of those same fundamental protocols for confirming identity were missing. At least they weren’t in digital form. They were, kind of, still manual. People looking at photos of physical ID cards. And as I began working more and more with entrepreneurs in places like Nigeria, South Africa, Ghana, I saw the same kinds of issues – KYC, fraud, and lots of manual processes to try to stop bad actors from getting into the system and ruining it for everyone else.

So, I started thinking about, “Well, what if you built, not the national ID system because fundamentally, that’s the purview of the state, but what if you worked with the existing national ID systems that are being built, and there’s many new ones being built across Africa, and build a set of protocols on top of that, that would allow people to authenticate whether this is a real ID and whether the person who is presenting this credential is actually the owner of it?” And that was the beginnings of Smile Identity. We basically set out to replicate what became known as IndiaStack.

So, IndiaStack is things like a universal payment interface, a digital signature, a digital ID confirmation, or an eKYC, you know, your customer basically. We tried to replicate that in Africa and created something called like an Africa Stack. But the way we do that is we didn’t want to replicate all of the costs and issues that the Indian system had. So, we didn’t wanna spend $1 billion. We didn’t want to be entirely reliant and dependent on government. We wanted to be compatible with government and we didn’t want to have people have special hardware.

So, the Indian system was built on using things like Iris scans and fingerprint readers. And once you’re dealing with physical hardware, you have a whole host of problems from costs to try to secure that hardware and making sure that the agents aren’t abusing that hardware or stealing biometrics. So, we needed something that was gonna work with whatever smartphone you’ve got in your pocket. And this just happened to coincide with fairly substantial advances in face recognition technology and the creation of these national ID systems, which often had a photo on file for a user.

So, Smile Identity really does two things. One is we allow our customers, which are primarily financial services applications, and now increasingly banks and telecoms, to provide us with an ID number from their consumer. We query that ID number against an ID authority. For example, the national ID authority, or the passport authority, or the driver’s license authority in a given country. And they can also provide us with a selfie or an image. And we can also compare that selfie or image against the photo that exists associated with that ID number again, inside the ID of authority.

So, by comparing the ID number against what exists in the national ID system and a selfie against the photo that exists again inside the national ID system, we can one, confirm this as a real ID, we can return some of the basic information on this ID like, what is the name of this person, what is the date of birth of this person, and we can also compare the selfie provided against the photo on file to see is the person who’s presenting this credential actually the person who owns it? Is this person the same person that exists inside the source of truth, which is the national ID system?

So, the combination of those two pillars became Smile Identity. And the nice thing about this is that if you are an application developer, you don’t have to have any kinda special hardware, you don’t even have to have an iPhone or an advanced smartphone for you to use our system. You can integrate our system via an SDK, the software developer kit, into an Android application or into an iOS application or where you can use us with an API. And that means that if you are building a new digital bank in Kenya, or in Ghana, or Nigeria, you can onboard customers quickly, you can get a biometric check on that customer against the source of truth. And the customer never has to walk into a bank branch, they don’t have to have any kind of special physical hardware other than any, kind of, the standard-issue smartphone. And I can do a stronger KYC check on your consumer in Lagos than someone can do on a consumer in Los Angeles.

So, this is basically the story of Smile Identity and the value proposition that we offer to this new class of financial services companies and digital financial services companies that are growing right now across Africa.

Meb: A lot of questions. I imagine that the off-the-shelf technology has come a long way since I was making fake ID in North Carolina. As a high school student, North Carolina had like the most easily fakable driver’s licenses on the planet.

Mark: I used to use New Jersey. Did you ever try New Jersey? That’s what we used.

Meb: No, but the beauty of the North Carolina one is you could print it out. I had a Mac, whatever Mac this would have been in the ’90s, I had a laser printer and you could print it out on packaging tape so that you actually didn’t have to make a fake ID. You could just overlay it on your own ID. So, you could just rip it off, nothing generates more entrepreneur, sort of, ideas than an 18-year-old wanting to drink Bud Light. So, okay.

Mark: Yes.

Meb: So you come up with the idea and it’s a pretty big step from saying, “Hey, I wanna take on something that is not that different than what India has spent billions dollars on.” Walk us through the process. Did you try to license or buy some off-the-shelf software, was this something you had to gather a team and build and walk through the actual last few years of actually putting this thing together?

Mark: There was really two disciplines in the company that we had to develop. And one of them was one that I already had which was a lot of relationships I had experience working in regulated sectors and in the financial sector in different African markets. And so, that piece I, kind of, had already, and I can talk a little bit more about that, but it’s not just my relationships. It’s also now the team that I’ve built.

So, we have a 15-person team. Most of those folks are in Africa. And each of the people who joined our team comes with some relevant experience, largely from other financial companies they worked with. So, we have basically one of the early employees of a company called Flutterwave, which has raised quite a bit of money and they’re now becoming one of the largest payment companies in Africa. So, one of their earliest employees joined us and is leading our effort in Nigeria.

One of the people who built a large lending company in Africa called JUMO became our early director of operations. And he had previously set up integrations with telcos and government institutions in five markets across Africa. So, he brought his Rolodex and his experience. And so, this network of relationships experience and know-how, trusted relationships, was one half of the equation. But, you’re right, the other half is this technology piece. And face recognition is, in a sense, it’s deep technology. This is not something you code up over a weekend.

So, we started with, and I don’t know how technical your listeners would wanna get on this. I’ll do my best. I’m a finance guy actually. My team is largely experienced engineers with a bunch of different disciplines and patents. I’ve become a student of computer vision, mostly through observation and understanding through them. But computer vision starts with what is the training set that you’re working with, and then, of course, what are the algorithms you can use to make decisions, and then how do you train those algorithms with your training set?

And there are now a number of open-source face recognition algorithms that have been developed either by private industry and made public or in academia. Those algorithms are, sort of, you can think of them as just as a vehicle, but you still need to train them and you need to train them specifically for the purpose that you have. So, we put together an ensemble of algorithms. This is basically when you have four or five algorithms that all are working and looking at the same data but they each produced their own results. And then you come up with an analytics engine that combines the different results to give you a final output. And that’s often how the basics of machine learning start.

So, you typically start with an ensemble, where you have multiple algorithms who are essentially competing with each other. Think of it almost like a Supreme Court, and you see what the votes are on that Supreme Court, and then you come up with a final result. Over time, we kept adding to that ensemble with additional algorithms and more and more data until such time as we trained individual algorithms to beat the ensemble because we had trained those algorithms with our own proprietary data and essentially, a substantial amount of proprietary data.

So, now, we’ve been at this for three and a half years. We have over 2 million face images that we’ve been able to train on. And our face images reflect our population, the targeted population that we’re working with. So, our dataset is 99% African faces. That is really important for getting accuracy in your algorithms. And we can talk a little bit about face recognition, because it’s certainly a hot topic right now and for good reason. But at a minimum, we started with the premise that we can’t just take an algorithm and use it off the shelf. We have to retrain it and reweigh it for our target population. And it’s taken us years and years to do that.

And I would say certainly, the first 18 months was fraught with issues, but we’re largely testing this with one large customer at the time, two large customers. One that was helping us collect data and the other one that was really helping us confirm the accuracy of it. And then there’s additional step that you have to take because you can’t just, sort of, rely on machine results. You actually have to see how many machines are performing against ground truth.

So, we’ve built a human review team. We actually have two human review teams. One in South Africa, one in Tanzania, and those human review teams look at the machine results of any given comparison between a selfie that’s provided by a user and a photo that’s on file, let’s say, inside of government institution. And that human review team will look at what the machine said and basically agree or disagree with it, and then put a rationale as to why they disagree, if they disagree.

Once you do this hundreds of thousands of times which we have, you create what’s called supervised learning. So, your machines now can learn from their mistakes and the humans are acting essentially as teachers teaching the machines where they’re making errors and how to correct them. And by doing this hundreds of thousands of times with millions and millions of images, you can start to get to something pretty special.

So, we are very excited by the accuracy we now have in our system. We’ve put it head to head with some of the largest face recognition companies in the United States, including a couple of the ones that have been recently in the press for questions about the accuracy of their technology, and our face recognition technology, our algorithm, as compared to some of those other large companies, is outperforming in an accuracy of face-to-face comparisons for front-facing African faces. And this is pretty meaningful because, historically, the accuracy of face recognition technologies for people of colour has notoriously been poor and especially poor for women of colour.

So, we’ve been working on this problem and not just myself, but actually a fairly diverse machine learning team, my cofounder, our systems architect from India, and our machine learning engineer from Rwanda, who’s actually a PhD candidate at Carnegie Mellon Rwanda. And that’s been one of the most exciting parts of our journey has been developing this technology for the very specific purpose of helping people get access to whatever digital financial services that they’re trying to get.

Meb: So, you’re allowed to say the names, IBM, Amazon, Microsoft, all the big tech giants, there’s been a lot of news flow about the problems with their offerings. I think the relationship in the United States with various government and police organizations that’s drawn a lot of criticism and probably rightfully so a lot of interest. Maybe talk about in general, any of the… you hinted at some of the parts of the challenges and it seems very obvious that if you train your algorithms on one population and then expect it to do well on another one that it wouldn’t be accurate. And then, not surprisingly, the ones that’s not gonna be accurate for tend to be the ones that historically, it’s been the most problematic.

Any of the thoughts, any other, kinda, general observations on what’s going on here summer 2020 for those companies? Are they just gonna, kinda, give up, they’re just gonna have to buy you? What’s the status?

Mark: We’re not necessarily interested in being sold to those companies, but there’s three problems here. And I think it’s high time this conversation happens nationally. I’m surprised it’s taken this long, but I guess people are now starting to pay attention to the way the technology is being used on them, which is one of the key problems here. So, the three problem I see with face recognition that’s offered by the large tech companies that you just described. The first is the use case.

The key difference between what we’re building and what those companies have built and sold is we are not building surveillance technology. The way our face recognition works is we train on images of front-facing profile faces. That is to say that for ours to get an accurate read, we want the user to show us their face forward-facing, chin up, eyes looking forward. And that is, kind of, the standard profile that we build around. If the user is looking off in a different direction, if their face is too far away, if it’s at an angle, we have some built-in real-time feedback mechanisms that we run in our SDK and soon we’ll be running in a web application to give the user feedback in real-time, to tell them, “Move your face into the circle, please move forward, you need more lights, please smile.”

So, these kinds of cues help us capture a good image of a user looking forward. And, of course, to do this well, you really need the user’s consent, right? We’re not trying to do this without their consent. We’re not trying to take images of people who are unaware. And in fact, our policies with our partners, all the financial services companies we work with, the telecoms, we require them to get consent from their users before they use our technology. That’s part of the whole purpose for the technology is to give people access to things that they want, not to use this technology on them to surveil them. So, that is the first problem.

And I think given the second and third problems we’ll talk about, which is the accuracy of the machines and the underlying dataset, if you’re trying to sell something for surveillance use case and you have problems with the accuracy, you really shouldn’t be doing what you’re doing. I think this is one of my biggest concerns about the big companies that are selling face recognition technology is the use case.

The second problem, now, we get into bias. So, we talked about the bias of the dataset, and this is certainly one of the biggest problems. So, if you train on a population that is reflective of the United States, you’re gonna inherently have a dataset that is still Eurocentric. It’s still gonna be white ethnocentric. And so, you are going to have more shots on goal, even if you’re doing supervised learning, get more training and more shots on goal for those white faces than you are for minority faces. And as a result, your accuracy will suffer as you get into those other population sets. And you can spend a lot of time training on this and many of these companies are, but inevitably, you’re gonna be limited by the nature of your dataset that you’re training on.

The last piece which is I think the piece that doesn’t get talked about as much, although there’s been a lot of hot conversation about this recently in a computer vision chat on Twitter, it’s turned into a full-scale debate, and for good reason. And this is the bias in the entire value chain or the entire image processing chain. And I’m talking here about the phone camera sensor, the face position detection and alignment algorithms, face recognition as well. Camera sensors, skin exposure settings are derived from aperture, which is the amount of light that’s allowed to travel through a lens. ISO speed, which is the camera sensitivity of lights, shutter speed, how long light is permitted to hit a camera sensor. All these things are adjusted by OEMs and by device manufacturers to default towards enhancing the capture of light-skin. Historically, that’s been the audience.

Now, we’ve actually seen some of this change has been really interesting. There’s a story that you can find on the internet about how a Chinese company that is one of the largest handset manufacturers and distributors in Africa has actually started adjusting these internal sensors and these device settings to have more accurate and more robust images of faces of colour. And it’s actually been part of their success in Africa. The company is called Transsion. They produce handsets that under the brands like Tecno and Infinix, but the underlying parts of the value chain have bias built into them.

In addition to that, if you look at the way that these face recognition algorithms get measured by NIST, which is the U.S. government body that essentially puts ratings on accuracy, the NIST dataset itself may not actually be reflective of minority populations. That dataset itself may be closer to the U.S. population. So, maybe you perform well on the NIST score. Maybe you actually outperform the other companies on the NIST dataset, or maybe you perform well on another dataset called Labeled Faces in the Wild, which is a large dataset of celebrities that’s been labelled.

The problem is if those datasets, which are essentially the measuring stick for accuracy, if those datasets are biased, or if a camera capture systems are biased towards giving you less data on African faces or faces of colour and richer data on white faces, you’re dealing with a situation where the frame of reference is biased and every house you build to perform well against that frame of reference has a crooked foundation.

So, there are lots of issues with face recognition and there’s lots of issues with the approaches that these big companies have been taking. We are trying to do something fundamentally different. Number one, it’s about consent, and people getting access to things that they want not being surveilled. Number two, we build a dataset that is specific to our target population. So, we’re working in Africa. Our dataset is 99% African. And number three is how do you deal with these inherent biases in the entire technical value chain? And we’re actually doing some stuff with our SDK to basically do enhanced exposure, to try to get more lumens on a face. We actually turn on the front-facing light as bright as we can get it also to get a reflection of somebody’s face to get more of a reflection, more lumens, essentially off their skin and get a better image.

So, we do a lot of little things to try to get better selfies. We actually wrote a blog post about this, Taking Great Selfies in Africa, on our Medium account. And it’s about how do you address all these inequities in the technology from the very beginning and not allow your technology to be used for purposes that it is inherently going to produce unethical outcomes for?

Meb: Yeah. It’s 2020. We find our self in a time where it’s so challenging with news flow. One of the positives of this coronavirus, by the way, I read recently that people are just, like, throwing their hands up with the major networks and started watching local news again, which we actually found ourselves looking at when they are reopening the beaches and everything else and local news is so pleasantly quirky, but in general, it seems a lot more along the lines of what you would think traditional journalism is without a lot of it. But it’s funny because if you didn’t mention a lot of the points and foundations of what you’re talking about, you wouldn’t assume that a lot of these structures are a little bit faulty and it’s been fun to watch because I remember getting updates from you when you’re like, “Man, we are so excited. We just hit 100,000 in our dataset.” And now you’re at a million, which is amazing.

Mark: More than 2 million. Look, I think for a lot of people who… and this is I think certainly true in the United States. Of course, I spend a lot of my time thinking about places outside the United States. I think a lot about what’s going on with my teams in Nairobi, and in Lagos, and in Cape Town, and then Kigali, but for many folks in the United States who… and if they’re listening to this podcast, they probably done pretty well for themselves. The traditional structures are working well. If you’re in an investment firm, if you’re making money, you’ve got a job that you’re able to work from home, plug it into Slack, plug into Zoom, plug into the podcast and to, kinda, go about your life during coronavirus, the world seems like it might be crazy outside of your house, but I guess you seem, kind of, fine and secure inside of your home.

But what I think coronavirus has pulled back the curtains on as these lockdowns have gone on and as the caseloads have risen is that inevitably, people who are at the margins always get hit the hardest. And whether that is people who are economically at the margins because they’re not earning as much money, or they don’t have the same job security, or they have to work the front line because that’s the jobs that they’ve got, or people for whom the system was not really built to serve. Coronavirus has, kinda, pulled back the curtain on that.

And we see all the underlying inequities in the system. And I think maybe this is what we really needed to have a reckoning about how do we fix problems with our social contract? How do we make the technology products that we use less biased and more ethical? How do we have a conversation about what is the appropriate use of technology in policing and in civil society? How do we have a conversation about the difference between people who can work entirely remotely and digitally, and what safeguards they might need versus the people who are in the front lines and have to work driving buses, and delivering food, and checking out groceries? Is there a different social safety net that we really owe it to those people who are taking on the risk for the rest of us?

So, yeah. I mean, I think we’re obviously seeing a lot of stressful things every day in the news in the United States and it’s hard to process, but I think it’s probably part of a necessary conversation that we need to have both about economic inequities, but also about technology inequities and how technology is used to either perpetuate the existing structures we have or whether it can be changed to improve the current situations.

Meb: Yeah. I have an article which may or may not be out by the time this publishes about at least within the U.S. some policy ideas, which is normally an area I don’t spend a whole lot of time on. It’s hard, it’s challenging. I don’t know. No easy answers in a lot of these ideas, but seems hopefully, I’m an optimist at my core. So, hopefully, things are moving in the right direction.

Mark: We have to understand reality before we can fix it. And I talk a lot about this. Inside Smile Identity, we have this term we call secrets of truth. So, we try to find the root cause of any problem. We try to find the root truth of any identity query. So, if we get a question, is this the real person, is this who they say they are, we have easily built machines to try to solve this, we have humans who work on this. But I think part of fixing things it starts with, let’s actually get a real look at what reality is for people. And maybe we needed a little truth-seeking here in the U.S.

Meb: Tell me a little bit about the business model, what’s the, sort of, ideal partner, you’re working with banks, what’s the plans for expansion? Tell me all the good stuff. How do you guys plan on making money?

Mark: Yeah. So, the way we make money is on a per-query basis. So, going back to your reference of checking an ID, so you could buy a Bud Light, our partners pay us on a pay-by-the-drink basis or per API call basis. So, they typically top up their wallet with us. And once they’ve topped up their wallet, as they begin to make API calls, we debit their account. And those calls can range from at the low end, probably, about 25 cents, at the high end, something approaching $1.

And the price really depends on what type of ID are we validating and what other elements might we be validating in that transaction? Are we doing just a pure ID validation? Are we doing a face recognition comparison? Are we doing a deduplication? Are we checking other information like a phone number?

So, this suite of services is available via API. We charge per API. And our customer base today is about 30 customers from across the five markets that we operate in. We started largely with FinTechs. So, companies that were unbundling traditional banking services and creating pure-play versions of the best in class savings app, the best in class brokerage app. If you’re a Nigerian and you wanna buy Tesla stock, how do you do that easily? Well, it used to be very hard, but now, there are two companies in Nigeria that allow you to do that and both of them are working with us.

And then now, we’ve expanded into other things beyond just Fintech startups. So, we have three banks that are working with us in Kenya and Nigeria. We have a couple of telecoms that are working with us to roll out mobile money services or mobile money wallets. And we have a couple of companies in areas like social welfare payments.

So, companies that are actually solving problems for their governments by doing either the equivalent of government checks to vulnerable people, and they do ID validation before they give that money out, whether that’s sent to a mobile phone or whether it’s handed out essentially. So, a variety of use cases, we are now starting to see some testing for telemedicine use cases. So, validating somebody is who they say they are before they get to use their insurance coverage and we’ve also seen some usage in ride-sharing. So, confirming that a driver is who they say they are either before they are allowed to drive on your platform, we’re having them have to authenticate their face periodically to confirm that the licensed and insured driver is actually the ones still driving the motorcycle. A lot of different use cases and example customers. One of the things about identity is it really cuts across every industry and every use case

Meb: What’s been the impact for your business and then be the emerging markets you work in with the pandemic of this year?

Mark: So, this is a big question that everybody in the team, we have 15 people in our team across all of our markets, and we all were holding our breath to see how coronavirus would impact Africa. The positive was that Africa had a bit of a head start because many of the cities are not as tightly connected to the U.S, and Europe and Asia as the global North is, kinda, connected to each other. And so, they had some time, some warnings in a sense, to be able to shut down borders and quarantine and prevent the spread of coronavirus. And so, there has certainly reduced the spread. There’s only been about 330,000 cases in Africa. About a third of them are in South Africa and only I think about 9,000 deaths so far in the entire continent of Africa, which has over a billion people.

There’s also a demographic difference which I think is interesting, your listeners may not realize it, the average age in Africa is actually 19 and less than 3% of the population of Sub-Saharan Africa is over 65. So, a much younger demographic overall. Now, this is some reasons for that other diseases and health conditions that people die from in Africa, but the coronavirus seems to be, to some extent, this seems like Africa has the demographics working in its favour.

The big question a lot of people were wondering was what’s the economic impact gonna be? So, you have all of these very stringent quarantines, which a lot of the governments put in place, particularly in South Africa, you also have a lot of people who need to work every day to eat. And so, we definitely seen economic impacts and certainly, people are suffering, but the governments did also begin to lift their curfews and their quarantines earlier than we’ve saw in the U.S. and certainly in some parts of the U.S. and Europe.

So, in the beginning of May, we saw Nigeria begin to open up. South Africa has begun to open up. They’re still on what they call level-three lockdown, which is where there are some businesses that are open, but things like people going into your house to clean house, that’s still not allowed. Kenya is, kind of, semi-open. So, there’s a curfew at night, but other than that, people can go about their business during the day. Of course, mostly, people are avoiding bars and restaurants, but the takeout business is thriving. I think you’re seeing a lot of the same things in Africa we saw in the U.S. It’s just, kind of, there was a bit of a delayed reaction. And then now, maybe the V-shaped recovery is starting.

But the most interesting thing that’s different has been that mobile money, which was already used in a variety of African markets, has dramatically accelerated. So, in Kenya, the government’s working with the largest telecom basically waived all transaction fees for anything less than about $10. That means that there’s lots of microtransactions happening now in digital form that are essentially free. Of course, the telecom missed out on some revenue, the government misses out on some tax revenue, but the benefit is that people can go about their economic business without increasing the risk of handling cash and potentially transmitting the virus.

So, it’s been an interesting story. It’s a little bit different from the U.S. and Europe, somewhat delayed different demographics. And this digital currency situation is giving some boost to economic activity without some of the risks that would come typically with using cash.

Meb: Yeah. We’re chatting with another African FinTech, Chipper Cash, I believe next week or the following week as well. You guys seem to be doing great.

Mark: Yeah. Chipper Cash is actually one of our customers.

Meb: Well, there you go. One of the tailwinds for Africa, in general, might be that at least it seems like this virus disproportionately affects older people and Africa is demographically speaking a much younger continent than most of the rest. We did a tweet last year on public markets. And I gotta be careful what I say here, because the way the SEC and FINRA works, but let’s just say there’s only currently one ETF out that focuses on Africa, which is, sort of, insane and it’s tiny. And even then, almost all of it is concentrated in South Africa.

But if you were to look at a lot of the characteristics of the companies currently super-high return on equity, high dividend yield, super low price-earnings ratios, but 15% of world population, at least 5% of GDP, but 1% of world market cap. And it doesn’t take a lot of common sense to think that if that 1% becomes even 2, 3, 4, 5, 6, 7, 8, 9, 10, this could very well be a massive boom over, not just this decade, but the rest of this century too. We’re very bullish.

Mark: Yeah. I think by 2050, something like more than half the world’s population is projected to be in Africa. That’s adding something like a billion people over the next 30 years. And, of course, that’s just projections, but it’s only 30 years away and you have the bulk of the population just now entering their prime working years. And so, for the next 20 to 30 years, they’re gonna be creating value, creating products, delivering services, and also wanting to consume services. And some of that’s gonna be global things.

Netflix has become hugely popular in Africa, of course, not surprisingly during COVID, but there’s also demand for more local content, local services, you know, locally relevant services. And if you think about PayPal, you mentioned Chipper Cash earlier. PayPal never really managed to win in Africa. They were always focused on bank accounts and emails, which just wasn’t the most relevant consumer tools for a continent where the phone became the primary computing and transacting device. And companies like Chipper Cash are really providing that similar value proposition of very convenient money transfer but in an entirely locally-relevant way.

So, instead of moving cash from one email to another, you might be moving cash from one phone number to another and doing it from one digital currency to another, or moving cash from fiat to crypto, crypto to fiat, fiat to mobile minutes, mobile minutes to your bank account. Your bank account may be to paying off one of the services that you use, like your Netflix or your DSTV, so you can watch Premier League. So, this really requires local integrations, local compliance, local understanding.

And I totally agree with your premise that this is gonna be where there’s gonna be opportunity in the future. I think the smart money, the venture capital money, has already started to move into Africa. So, something like $2 billion of venture capital went into Africa last year, and that was double-digit growth over any previous year. And over 50% of that went into FinTech or financial services, which is the core infrastructure for any kind of business that comes next, basically every kind of commerce.

So, the Cambrian explosion, I think is just starting. And the people who are paying attention are making their bets now. And they’re betting on services that are locally relevant, locally compliant, and building locally-adapted products and services for what are fundamentally universal value propositions. There’s also more local African capital formation, more PE funds and VC funds with offices across Africa, more African teams that have experienced large rounds now actually being funded, and some entrepreneurs who are even on their second or third venture-backed company. It’s a really exciting time for the continent and we’re certainly excited to be a part of it.

Meb: Awesome. All right, I’m gonna make you put on a different hat. I’ve never been to Africa. I have a, let’s say, a three weeks, what’s a good initial first [inaudible 00:51:49]. What would be my itinerary? I need to make a few stops. Where do I go?

Mark: Three weeks is great. I wouldn’t do anything less. In fact, I would try to do more if you can. Man, I’m sure someone on my team is gonna get upset with me. We have people on our team from six different African countries. So, someone on my team is going to feel left out. Look, Africa is such a massive continent. It depends on the, kind of, experience you’re looking to have.

If you’re going on a fact-finding mission, and you really want to try to understand African business, and the African consumer base, and the next generation of entrepreneurs, you need to make a stop in Nigeria. It’s the largest country in terms of both population and GDP, try to do some meetings and Lagos, maybe talk to the folks at Ingressive. They set up meetings for investors. Many actually have their own fund as well, but they can help you get a sense of the intensity, the excitement, the creativity of businesses and entrepreneurs. They’re both those who are tech-enabled but spend some time in the markets. Go visit computer village and see the people who are just working to survive and make ends meet on the street, entrepreneurs who are doing it out of necessity.

If you’re looking for something more like a vacation, maybe a little slower, more relaxing, perhaps a more familiar landing coming from the West Coast and coming from California, certainly South Africa and the Western Cape. There’s a great wine country, good entertainment scene, the surfing, and at least pre-COVID and hopefully, post-COVID, there is a great restaurant and bar scene in Cape Town in the Western Cape. I love that area. We have a team there. I moved my family down there for a while last year, shortly after my son was born.

If you’re looking for a full experience, something that people often associate with a trip to Africa is, of course, a Safari. South Africa has great wildlife and game parks, but so does East Africa. And I would certainly be remiss if I didn’t tell you, you’ve got to go to Kenya. And if you go to Nairobi, Kenya, you don’t even have to leave the city. The Nairobi National Park as a full-scale national park with amazing wildlife that’s actually inside the city limits, giraffe, lions, zebra, the whole thing. And if you’re there during the great migration, you can actually head south across the Maasai Mara and the Serengeti. There’s something like 1 million or 2 million wildebeest that move south from Kenya and Tanzania.

But while you’re in Nairobi, again, make time to meet with some of the great entrepreneurs for there. Familiarize yourself with mobile money, get an M-Pesa account. See what all the fuss is about. Nairobi is really where the mobile money revolution started. And it’s created a lot of other companies and innovations on top of it, but there’s just so much to see. It’s such a massive continent. People underestimate how big it is though if you’re looking for, you know, a three-week highlight tour, those are my favourites.

Meb: Maybe I’ll create it to four to six weeks. We’ll see. My first roommate, one of my best friends out of UVA is from Burkina Faso.

Mark: Oh wow.

Meb: I’m sure I’d be remiss. If you’re listening, what’s up Simon? If Simon, who is building a bank, if I didn’t come spend some time with him or let him tour me around too. So, it may end up being a whole quarter. We’ll see.

Mark: If you speak French, you got to go to francophone and get a great opportunity to go try to test out your French.

Meb: Amazing. Man, you’ve done a lot of travelling with building this company and all sorts of different countries. It’s hard enough to try to do a startup and just one you’re working in a lot. What has been the most memorable moment of, sort of, the Smile journey the past couple of years?

Mark: One of the highlights for me, and we were actually planning to do this again this year until the coronavirus hit, but one of the highlights for me was we had this all hands offsite we did last year around this time in Tanzania. We decided afterwards we had so much fun, we wanted to do it annually. In fact, I would like to do it semi-annually or even more frequently, but we got our whole team together from across four different countries where we have different team members and we spent some time both having fun. We went to Zanzibar, we swam the ocean, we saw turtles, we cooked prawns together with one of our customers. We also had some serious conversations about our products, our customers, their needs, what’s changing in Africa, and our vision for the future of our company.

And I think what was really important for me was to have this whole team together, to build closer relationships, to bring this really diverse group of people. And I don’t mean just diverse, we’re from different countries, but different experiences, different skill sets. In our team, we have people who are machine learning, PhD candidates, we have product designers, we have support engineers, finance guys like me, a sales team, and we all learned a lot from each other. It was really educational and fun.

And we also got a chance to really focus on solidifying our values as a team, the things that bring us together no matter where we’re from, no matter what part of the company we work on. So, that was really fun, and I can’t wait to do that again. And hopefully, we’re gonna make that, kind of, a quarterly or semi-annually part of our company even as we continue to grow and expand across Africa and hopefully beyond.

Meb: What’s the horizon look like for you guys, the next one, three, five years? What, sort of, the main things you hope to accomplish, the, sort of, big opportunities, what’s y’alls big goals in next few years?

Mark: When you think about what we do and the problem that we’re focused on, it’s about solving trust on the internet, which is a universal thing. So, today, we’re in five markets across Africa, but our ambition is to be in 54 and it will take some time to do that, but over the next year or so, we’ll get from 5 to 15. And we’ll also be expanding our offering from consumer verification to business verification, what we call KYB. And this is more than just confirming or verifying a business registration number or tax ID, which we can do today in Nigeria. It’s about doing a stronger due diligence on a company.

So, we will be able to do identity verification on the directors, we’ll be able to see who the beneficial owners are, we’ll be able to see if there’s a change of control or change restructuring of the business, and then also checking for things like sanction screening, politically-exposed persons, whether or not the names of the directors or beneficial owners show up on those lists, and finally local compliance. So, is this business compliant from a tax standpoint? Do they have a tax clearance certificate?

So, these additional services we’ll start by offering in Nigeria this year and then we’ll expand them to our other markets as we develop them and as we learn. But beyond Africa, we’ve actually begun receiving inbound interest from other parts of the world. So, we have interest from the Middle East, we’ve gotten demand from South Asia and Southeast Asia.

And I expect that if you caught up with us in two or three years’ time, you’d see that we’re not just an African company, we’ve actually built an identity platform for a broader set of emerging markets, where there are common needs and common challenges, heterogeneous device environments, challenging network connectivity issues, but high growth and fundamentally a new generation of internet users who are coming online primarily with mobile devices and are interested not just being entertained, but actually in transacting and yet need to be verified, need to be trusted in order to interact and transact with the broader global economy. So that’s the horizon that we see for ourselves over the course of the next several years. And we think it’s a very exciting one.

Meb: We also ask people what’s been their most memorable, personal investment. Anything come to mind, good, bad in between?

Mark: There’s a company in Kenya called Kopo. And it was an investment that I made in my prior firm, but it became personal for me. I actually made it in 2011. It was the first investment I made in Africa. The name of the company actually comes a West African creole word for money. But Kopo Kopo was the first company to do merchant onboarding for Safaricom’s M-Pesa. And that may sound like, kind of, a boring business topic because payments is somewhat esoteric and boring and I think it’s about licenses and regulation. But think about what Square did for payments in the United States, essentially making it easy for any small business to accept digital payments so that a coffee shop or an artist could get started quickly and accept card payments over a weekend without really any infrastructure.

And Kopo Kopo essentially did the same thing and they started around the same time. So, there were lots of parallels, but instead of cards, they made it easy for any small business in Kenya to start accepting mobile money. And you might ask, “Well, wasn’t that like the point of mobile money, didn’t M-Pesa already exist?” Yes, but M-Pesa wasn’t used that way at the time. It was a consumer peer-to-peer payment tool. So, trying to use it to buy something in a retail shop would be like, it would have been like walking into McDonald’s and telling the clerk you wanted to pay them personally with PayPal. It just didn’t work. That wasn’t something that was functional. There just wasn’t really a business offering for M-Pesa. And Kopo Kopo created that.

And then on top of that also, similar to Square, they built a lending business which has really become their growth driver. So, because they could see them, merchants’ transactions every day, and because they were actually processing the payments on behalf of the merchant, they were in a very privileged position and could make loans to those merchants that banks couldn’t because they had a better visibility into that merchant’s business.

And even now during COVID, the company is actually still making loans and they’re able to survive and thrive because they have a superior dataset. They can see all the transactions coming from a merchant. So, they know if you’re a merchant and you’ve shut down your business, that you’re not making any revenue. They also know if you’re a merchant and you still got a business may be doing takeout for certain hours during the day. And so, of course, it’s challenging, it’s a different environment than they’ve ever seen before, but they’ve been doing this for a decade and they’ve built a superior dataset and really a trusted relationship with their thousands and thousands of merchants because they’ve been doing this for so long.

And if there’s anything I’ve learned from doing business in Africa, it’s just how important trust is. I’m really honoured to now have associations with companies like Kopo Kopo, who have built trust with their customer basis over a decade. And there’s many other companies like that across Africa that we’re working with that have also done a phenomenal job of building, kind of, best in class or best of breed products in their categories, building trust with their consumer basis. And we’re helping them to start trusting more consumers and onboarding more consumers. So, the Kopo Kopo experience I think set the stage for me personally and I’m still very proud to be involved with that company.

Meb: Well said. Mark, people wanna follow your story, what you guys are up to, partner with you, all that good stuff, where do they go?

Mark: Check us out on Twitter, Smile Identity. You can go to our website smileidentity.com. Our Twitter handle is @smileidentity, and you can also find us on LinkedIn again, Smile Identity. It’s spelled like it sounds. My name is Mark Straub. You can find me on Twitter as well @Markstraub. I’m the CEO and co-founder. My team, which is spread across the globe, is always excited to connect with people who wanna work with us. So, if you happen to find yourself in South Africa, or in Nigeria, or Ghana, if you’re doing business in one of those markets may be Kenya, you can also reach out to our team and each of those countries there’s email handle, so, kenya@smileidentity.com, ghana@smileidentity.com and so forth.

Meb: Awesome, Mark. This has been super interesting, a lot of fun. I’m definitely gonna reach back out when the world gets back to normal and we can travel and you can be my tour guide as well.

Mark: I’d love that.

Meb: Thanks so much for joining us today.

Mark: Thank you so much, Meb. This has been great.

Meb: Podcast listeners, we’ll post show notes to today’s conversation at mebfaber.com/podcasts. If you love the show, if you hate it, shoot us feedback@themebfabershow.com. We love to read the reviews. Please review us on iTunes and subscribe to the show anywhere good podcasts are found. My current favourite is Breaker. Thanks for listening, friends, and good investing.