Disrupting Traditional Banking

Welcome to the keynote from Cornell. I’m your host, Lutz Finger. I’m a faculty member at the Johnson School, where I teach several topics, mostly AI and product, several courses. I actually recently launched as well a course on product and AI.

It’s a public course. It’s a certificate program. If you want 100 hours of German English from me. But in that course, I actually replaced myself with a bot, with a virtual copy of myself, so that the course stays up to date.

Well, beyond academia, I’m a startup founder and CEO of a Gen AI platform for e-commerce. Essentially, I’m doing SEO in the new world of large language models, and SEO stands for search engine optimization. Now, in the new world of large language models, you won’t have search, so it’s something else. Let’s not call it SEO, so get rid of the S.

And as many of you who follow around the show knows, AI is transforming the world. It’s transforming many industries. And one of those industries is the finance industry, and it’s fintech. And that’s the reason why it’s my very special honor to welcome today’s guest, Colin Walsh.

Colin and I, we met quite a while back, and he is the founder as well as on the board of Varo Bank. I have to disclaimer, I’m an early investor in Varo Bank. Like, Colin’s vision is stunning. We will have quite a fun discussion.

So let’s dig right into it. Let’s talk AI and fintech. But before we do this, quick question to all of you out there. Like usual, write down.

Your questions. Write down what you want to chat with us. I will funnel this through. If you put in your name, don’t be surprised when I say your name.

If you want to be anonymous, write that down. Obviously, don’t leave private information on it. It’s a live show. Okay.

With this one, let’s talk fintech. Colin, welcome to the show. Well, it’s so great to be on your show and to be part of another class with you, because I think we’ve got a lot of questions. I’ve done this once before.

Three times, actually. Has it been three times? Okay. All right.

Well, that’s great. And I’m thrilled. Thank you, everyone, for taking the time to participate in this session today. So one of the things I normally start off, who is Colin, right?

So like, who are you? But before we do this, what’s on the map behind you? So this is a map of Rome. But it’s for many centuries, actually millennia, how Rome got built out.

And it’s sort of a layered piece of art. But it reminds me every day that Rome was not built in a day. But however, with AI, that may change. And so you might actually be able to create entire cities in very short periods of time.

But we’ll talk about kind of how the power of AI is really unlocking so much in terms of better access and reducing risk and allowing populations to enter into the financial system to find a pathway towards wealth and prosperity. And so we’ll talk a lot more about that and some of the specific things that we’re doing at Voro Bank. Very cool. So I’ll give a background to you.

But like, who’s Colin? Like, except that you are sitting in front of a picture from Rome and you are coming live to us from San Francisco. From San Francisco. But I actually did live in Italy at one point.

So it’s close to my heart. But yeah. So a little bit of my background. So I graduated Cornell a long time ago.

I’m not going to disclose my year. And then I went into financial services. So I had a career that spanned quite a long time, over two decades, working for some of the leading financial services companies in the world, including GE Capital, American Express twice. I was at Wells Fargo.

I was at Lloyd’s Banking Group in London during the financial crisis, before, during, and after the financial crisis. So I had many years of experience leading businesses, launching products, turning around businesses, buying businesses. But really, there was a feeling that I had about 10 years ago when I started Voro that the system was just not working for so many people out there that were struggling to make ends meet. They were looking to get ahead.

But the sort of legacy way of doing things really continued to exclude large populations of people. And so my vision was to create a bank that was built on the latest technologies that could do things in much more innovative ways to be able to provide access to free banking services, which is something that, you know, for people who don’t have a lot of wealth or income, the banks, the only way they make money off of these customers is through fees and charges. Right. Right.

So that’s part of the exclusion. And then building better tools for credit access as well. Let me actually, like, and by the way, this is like, Colin, you are the typical go-rat person, right? I mean, like, the Johnson School that is behind this keynote is known for, like, their strengths in, like, education for finance.

And Cornell is one of the best schools for… Yeah. …computer science and therefore software AI. So the combination you bring to the table today is really Johnson and Cornell all over.

But can you explain a little bit more about today’s situation for the underserved population? Because I think many people in this audience, they don’t even know that part of this country in which we are living here doesn’t have access to banking. Yeah. So I would say that the U.S.

is largely banked. I mean, there’s probably about 7% of the population that don’t have some form of a bank account. So compared to other parts of the world, I would say the U.S. population is largely banked.

And there’s also… 7%. Like, I think it’s amazing. But it’s 7%, right?

These are small. People that don’t actually formally operate inside the banking system. They might be using cash. They’re using prepaid cards.

Other things that… You know, they’re using… You know, they may be transferring money through money transfer apps. But for the large…

But 93% of people have some access to financial services. Also, the U.S. is a very concentrated system from a supply perspective. You’ve got banks and financial institutions competing at the local level, the regional level, the national level, online, digital.

But the problem is that unless you have a very low cost, very efficient, sophisticated platform to help people, you tend to… You have to have a very low cost, very efficient platform to help people, you tend to offer not very good services. Like, I like to say that people are underserved and overcharged in the U.S. And so, as I mentioned earlier, many people who don’t have a lot of wealth or income are experiencing an abundance of fees and charges.

Many of them are not terribly transparent. They have difficult time sort of building credit if they’re new to credit, or they’ve damaged their credit. They have a very difficult time getting access to transparent, affordable credit. They don’t necessarily have access to tools to help them build savings and greater financial resiliency.

And so really that was what VAR was all about, is how do you bring all of these things, elements together in a digital platform to be able to help? And this is the shocking statistic, is that there’s 180 million people in the United States that are just living paycheck to paycheck, like they’re just trying to get by. And with all of the economic stress and economic uncertainty and political uncertainty that exists in this country, that is a real burden for many people because they’re feeling this acute sort of financial stress and anxiety. And so being able to alleviate that.

It also, and I know, let’s, from your background, you know, you also have experience in healthcare and health tech and, you know, the financial pressures lead to health issues as well. And so, so VARO’s mission is really around how to be the bank for all of us, how to be able to provide free banking services, quality banking services, but also being able to help put money back in our customers’ pockets. And we’ll talk about how we do that. Actually, and let’s stay for the moment at the actual problem at this situation, because, I am one of the things.

So as I met you, I don’t know, like how many years is this? Long time ago. Long time ago, right? Like you had started the journey.

One of the things which I found as a storyline, very fascinating, which I think you should tell here is if people, those over 100 million people who live paycheck to paycheck, they get a paycheck. If they don’t have a bank account, they go to the bank and let’s just say the paycheck is $1,000, then the bank charges them a tremendous fee to actually. Oh yeah, minimum balance fees, or if they’re running short before the next paycheck arrives, they’re paying punitive overdraft fees. You know, they’re getting zero interest if they’re able to put some money aside for savings.

If they are able to get access to a loan, it’s probably at an extraordinarily high rate. And if they miss a payment, they’re charged fees on those missed payments. So on average, our customers, we save them hundreds of dollars of fees a year, which for people who really are trying to make every dollar work for them, that’s very meaningful. And then you add in the other sort of services that we’re providing to help build much more control and resiliency.

Now, before you dig into Vyro, why is it that the banks, before Vyro came to the scene, weren’t supplying? I mean, it’s a huge market, right? Why didn’t banks give that market a service? Or charge them so much that this was actually bad, right?

I mean, there’s two reasons. And the one I’ll focus on is more the economic incentives, that it was really difficult for the banks to make money off of consumers that transact. And so the banks are using more of a low-value transaction system, which is a very, very frequently, but more low-dollar transactions. So they’re not necessarily high-value transactions.

They’re perceived as higher credit risk because the banks are using backward-looking systems that are trying to look at credit performance as opposed to actually monitoring real-time cash flow and activities with the banking system. And again, it requires much more sophisticated tooling and modeling capabilities to do that today. And also, vulnerable populations are more susceptible to fraud, scams, and other things. And so there’s high cost of fraud and dispute processing and other things.

And so the economic incentives made it very difficult for, and to this day, make it difficult for incumbent institutions to effectively serve this large population of consumers. And so this is where technology comes in. Now, there’s other issues at play too, but that’s, yeah. Yeah.

This is so cool because whenever we talk about AI and technology, then the typical product viewer from a background, right? I’m a product manager. I come out from the Google school. Then it’s like, okay, what’s the user flow?

What’s the user’s problem? And we have that here. People are overcharged. They have a little money and that little money, a high percentage is actually going away for fees.

Mm-hmm. And that happens because their risk is too high and the cost of doing business is too high. So this is a perfect situation where technology can play a role. And this is the moment as you entered with a vision and I listened to you and I was like, gee, this guy is amazing.

So tell us a little bit like how did you pitch initially Varo Bank in terms of the technology? Yeah. Well, and this is where it became… You first started with kind of what are the risks?

Yeah. There are一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定一定 and making sure that none of those activities are happening and having systems to be able to detect anomalies and to understand how our customers are using the platform. Again, we’ve touched on a couple of times lending and being able to provide access to consumers who maybe the banks would not underwrite or feel are a good credit risk. So using advanced machine learning capabilities to be able to make loans available to folks.

And then also thinking about personalization how do you get to know your customer and be able to serve up things that are relevant to their lives and making sure you’re kind of there meeting them where they are and whether it’s through how you acquire customers and making sure you’re finding the right people, how you prove them, how you then manage those relationships over time. And that also speaks to how you service these customers because one of the things we learned early on is we were providing voice support for everybody but it was getting very expensive. And so then we, we moved more towards a chat, sort of a live person chat but then we started to build an AI chat bot that is now containing over 55% of our inbound inquiries and it’s allowed us to reduce our operational costs by about 60%. So I mean, it’s like, but get it, but continuing to develop that.

And as you mentioned, let’s kind of thinking about that journey. So first it starts with the use cases but then it’s really thinking about the platform itself and how do you think about your organization, your organization and the infrastructure investments you’re making that are gonna help, make smarter decisions faster, that are gonna try to identify causal links, the accuracy of the decision-making. Also as a regulated institution, it’s very important to have explainability of your models and how you incorporate explainability into the development pipelines. And that also requires a lot of work, thinking about instrumentation of the platform.

So if I start trying to, go ahead. Because we covered a lot of ground. So let me just bring this together. You had two problems, right?

The problem number one, the operational cost for traditional banks were too high to serve underserved. And the risk to do it was too high, which added to the cost, right? And I think, you just talked through so many good things that I wanna actually- We could do a class on each one of these. Exactly.

Like each of them- And I’ll add a third actually, is personalization too. Oh, personalization, yes. So you don’t have a terribly personalized experience. So you have high costs, what’s perceived as high risk, and then a lack of a real kind of- Personalization.

Personal experience for the consumer. And all of this is actually a technology or machine learning play. And it’s funny, Brent, Brent and Clark actually already like ask a question, and I can only encourage everybody else to ask, because he was saying that how to deal with financial crimes and advisory offerings, and you actually addressed this already in the answer. You are using AI to manage risk, to look at fraud, to reduce the risk for lending.

And you talked about the, personally, you talked about the, personally, you talked about the personalization and the operational part. And all of those are actually features in something which makes you like a very, very successful FinTech case. But it’s not that there is one AI that solves it all. You build an AI every time.

And it’s an ensemble of models. I mean, we have so many, and I was just about to kind of geek out on the platform when you stopped me because I can get into- Sorry, yeah. No, that’s fine. But like, there’s a lot in terms of what you have to build, you know, in terms of inference and model, and sorry, the monitoring side.

I mean, it’s really important that as you build out these tools and how you think about the model development pipelines and the feature platforms, and as you were just talking about, you know, there’s many features that go into being able to actually provide these outcomes through the model development effort. And so, a lot of, you know, going back to your earlier question. So, as we were approaching this, really thinking about how to design a platform from the onset that would meet some of these objectives of, you know, better, faster decision making, explainability, so that you can actually take your models to the regulators and have them understand them. I mean, these are all critical components that ultimately get us to the outcome of efficiency, lowering costs, making better credit decisions, having a more personalized experience for our consumers.

And this is like some of our audience is doing fundraising. Cornell has a very strong startup community, as you know. Right? We just said there’s a problem.

The problem is not as described. We said there is technology which can solve it. Then you went on into the details, as you said. You geeked up and kind of like you can say, I can solve this.

You’re just getting me going a little bit. Yes, I know. I know. I got more to say.

Which is absolutely like, which makes you an extremely good product leader and the leader for Varo in this case. But how did you communicate this to, you got strong backing as you initially started. This was not a cheap journey to start with. What was your pitch at that point?

The key pitch to our investors, and we have real kind of blue chip private equity investors, and we have VCs, we have a whole kind of gamut of investors that it starts with- You had me. And you, and you were one of our very early investors. But it starts with understanding how deep rooted the problem is and the fact that there’s a massive audience of consumers that could ultimately benefit by getting this right. And then you get to the conversation around, okay, well, how are you going to do this?

the conversation around, okay, well, how are you going to do this differently? Like, what are going to be the kind of unique, you know, sort of differentiators, whether it’s in terms of the consumer facing proposition that you’re putting into the market, or are there business model advantages? Like one of the real features of Varo is the fact that we are a nationally chartered bank. And I think we’re still the only fintech in the United States to get a full national consumer charter.

Can you explain this a little bit? Like, I know that this is important, but like, I don’t think the audience understands why this is so important. We’re veering off the AI a little bit, but I think this is a business. But your point around how did I pitch Varo was that I didn’t want to just be operating with a sponsor bank, which was, and we started that way, because that was the only way to get to market quickly, do proof of concept, scale the business, really understand if it was going to work.

But all along, I said to my investors that we will be a much more sustainable business if we actually are operating with a bank charter, because that gives us direct access to the payment systems. We were a direct member of the FDIC. We don’t have any of this, what I like to call sort of stroke of the pen risk of working with a small sponsor bank that suddenly gets into trouble and they shut you down because you’re affiliated with that sponsor bank. And it may be something that you didn’t even do.

It could be another program that screwed up. And so really kind of controlling our own regulatory destiny. And then going back to the course- And also- I would like to put a bow on it because many companies currently we see are putting just a little bit of lipstick around something. We call it wrapping.

And you see this in large language models, right? Like they see ChatGPT can do amazing things. They get an API from ChatGPT and then they put lipstick around it and kind of saying, look at how good this is. Yeah.

And then they’re really not, someone else is actually providing the- The value. Yeah. And so we have this in FinTech a lot. We have a lot of so-called neobanks that kind of take an existing bank layer, an existing bank provider, and then they just make a very, very nice interface into it.

And they can offer personalization, but what they cannot offer is the risk reduction and the cost reduction. And therefore they don’t go into the actual vision that you have pointed out. Well, and I think this is what really makes Varo unique is that we’ve created this, this vertical integration of our stack from front end customer facing all the way through operating as a fully functional national bank, but able to do it with very sophisticated technology. And this gets back to some of the early decisions around how we wanted to build the platform, how we wanted to think about using machine learning and AI and embed it into almost every aspect of our business.

I had a conversation a couple of weeks ago with another digital bank CEO, a different part of the world, some, someone from Asia. And we were talking and, and say, look, well, do you get asked questions about AI? And the response was like, do I breathe? I mean, like, yeah, of course.

I mean, like, it’s not like we have to go sell it as something special. It’s just part of how we run our business. And, but that’s all choices that get made at the early design stage, in terms of how you build out your, your technology platform, and you instrument it in a way that can allow you to get these benefits around cost reduction, around fraud, around, you know, cost reduction, around fraud risk management, around being able to lend to consumers that traditional institutions won’t lend to. But it all kind of starts at the design stage.

Yeah. Now, let’s walk through one of those use cases, because I think for all of those different areas from personalization over chatbots, over better lending or cost reduction, there are so many things which we could actually talk. But let’s be very specific. How about we talk about risk?

So VaroBank became amazing because they looked into credit risk. And maybe we have a slide for this. Maybe if you talk us a little bit through that. Yeah.

Before you go to the slide, let me maybe give a little more background context. And so the customer that we serve that’s financially strapped places an enormous amount of value around getting real-time access to credit. It’s largely kind of liquidity solution. And so the product that I’m going to showcase here today, and we’ve got a couple other credit products, but the one that is what we call the VaroAdvance.

And so it allows customers to get up to $500 immediately, real-time. And it’s available in the app, but it’s based on very sophisticated. It’s based on very sophisticated tools. That allow us to determine who can access and how much they can access at any given point in time.

But it really creates a critical lifeline for consumers. And it’s a bridge between paychecks. So if you’ve had to reduce your hours because you’re dealing with childcare issues, or you have a bill, an unexpected bill that comes in, and then suddenly you just find yourself short before the next paycheck, and it can be used for groceries and filling the tank with gas. Or whatever essential things you need to do, people rely on this product.

And so we’ve been able to, we’re actually now on our third generation machine learning model based on a series of, it’s using cashflow data. It’s using the direct deposit data that we collect from our customers. It’s looking at their spending habits, their account balance. And it creates a series of features that allow us to use this sophisticated cashflow.

Underwriting to determine how much credit we can give customers. And I can just say, just from the initial deployment of the model to the latest version that we’re using, we’ve actually been able to double the number of consumers that can actually access the full $500 amount. And as I said, this is something that is incredibly meaningful for people who have just fallen short before that next paycheck. And the models are displaying very strong risk sloping property.

So this allows us to separate. Goods from bads and being able to determine how to extend more credit to people that actually have the capacity and the ability to repay it. And then controlling how much credit available we make available to people that might have a problem paying that back. And it also fits very much within our mission of being able to provide access to customers for these sort of critical services.

So, and I think, and I think you’ve got a slide up now, Lutz, that, you know, unlike, you know, traditional credit risk models that tend to use backward looking data, we’re using reinforcement learning. So there’s a tool called XGBoost, which is an open source machine learning algorithm. And it’s been used in a lot of industries. I don’t think as much in banking and financial services that allows us to constantly monitor and learn what we can do and how much credit we can extend.

The other thing that’s also unique from Varo’s perspective. Is that we’re using certain constraints to be able to allow the model to be easily interpreted for regulatory purposes. And this is very important as a regulated institution, because you do not want to embed bias. You don’t want to have things that are too much of a black box that your regulator can’t understand.

And so we’re using tools and techniques to make it much more explainable. And then this is really creating what I, what I like to call. And the team calls really a bridge between traditional banking and much more sophisticated tech based lending because we’re not relying just on that traditional credit history. And I think the credit history can be incredibly punitive and prevent access to quality credit solutions for a lot of customers that we serve today.

This is amazing. And we had, let me bring in a couple of comments here because, um, Andrew Sabine, um, she, um, he’s a very good friend of mine. And she has done a study and she kind of chatted here in the, in the live stream. And she said like, well, um, we see that customers very, very often had warm, like, well, how did she call it?

Warm feelings towards those cash, um, uh, check cashers. And, um, how do you as Varo Bank now use that ability to overcome it? Essentially, you’re using an off the tool machine learning program, uh, X-boost, right? In order to more accurately calculate risk and what I’m doing.

As well. There’s another thing about this too. And it took several generations of the model to get this right because, you know, and to the question that was asked, you know, people have an affinity towards these, um, maybe cash checker cash, uh, check cashers in their local community, but they’re charging them exorbitant fees, but they, they feel like, okay, well, I know if I go there, I’m going to get the money I need. I’m going to get it when I need it.

So we’ve had to sort of recreate that in a digital context to be able to help customers understand that if they continue to bank with us on a consistent basis and we, and we use advanced personalization to also help people understand the steps they need to take to be able to access more credit. And that’s actually in the app for every person, every customer, it sort of says, here are some of the behaviors that if you do this, it’s going to increase your likelihood to be able to get higher. Limits. And now we have a line of credit product on top of our advanced that can actually help them access even more credit.

But to the question, you know, it’s like, how do you, how do you build that trust in a digital environment? And it is around creating both the tooling to, to be able to get the reliability, the speed, the accuracy, the consistency, and, and serving it up in a delightful, easy to use intuitive platform. And I think that like he come to two amazing technologies together, right? So you can do it because you use a traditional, I call this now a traditional learning tool like X boost, which is open source and you can deploy it.

You have the data, you can figure out a risk. Now you can offer a product like advance, which the, the cash, the check cashier to Andrew’s question cannot do so easily, but you can do it because you have the volume of information and you actually can say, okay. They, they are. Yeah.

They’re caching their check always. So I know my tool tells me, and I can do an advance. Now the other thing you can do, and this is more generative AI. You can start to replicate this trust level by offering in a nice interface and chats and so on.

And more tailored to the, and there’s also an element of building digital literacy as well. And this is something that I’m, I’m exploring more globally right now and in other, other parts of the world that maybe aren’t as sophisticated. Yeah. There’s also these new technologies that are falling into place in general, but also bringing in these new technologies that can then also help these new companies in general who can then also help these new as well as Brenton, who both kind of said, can you tell us a little bit, how do you use now this technology towards the regulator?

Because they want to obviously avoid fraud. They obviously want to see something. And if you want, I know you have a slide there, you can pull this up, but just give us a little bit the idea of what, how do you avoid fraud using essentially that base technology now? Yeah.

So, well, again, a lot of similar principles, you know, anomaly detection, understanding. And so the couple of, I think I heard a couple of questions there. One was around regulators, which I’ll talk about in a minute, but also just from a fraud use case that there’s several aspects to this. One is just even at the front door, like account decisioning and who, and looking at, you know, identity information, being able to look at kind of where they’re coming from, the type of IP device that you’re like, there’s a whole series, there’s a whole series of things and tooling that you can use right at the front door to make better account decisions around who you even let in, because you want to try to keep the bad actors out.

And as you know, and, and there’s also, you know, people, there’s bots now that, that are not even human. And so trying to detect, you know, whether somebody is actually human or are they just a machine that’s trying to open up an account for fraudulent purposes. And so we’ve gotten a lot more sophisticated in terms of how we use our tools to, to identify those bad actors at the front door. But then, as I mentioned earlier, when you’re dealing with more financially vulnerable populations, people are more susceptible to scams.

And so if somebody sees on their Instagram, oh, you know, borrow is going to give you a thousand dollars. All you have to do is click here and give us your credentials. People are like, oh, a thousand dollars. That sounds pretty good.

You know? And so, so you find that you do get instances of account compromise, but then again, going back to the customer, the customer is one of the things, they value extraordinarily is access to their money. Like if they feel like they’re going to get blocked. So if you detect something that looks anomalous, like somebody is logging in from a different IP or a different device that maybe the account’s not bound to, or, you know, can you in real time sort of block those logins?

Can you force a password reset, but still allow the customer to have access to their, their funds? I mean, these are all like, you know, there’s been a series of tests and learning and over time, but you know, the fraudsters are also adopting many of these tools. So they’re becoming quite sophisticated as well. So you always have to say sort of two, three steps ahead of, to be able to make sure you can keep the platform safe.

You can continue to deliver an experience that these customers want, because something I said earlier, you know, there’s an abundance of supply. So if you get it wrong, customers are going to go somewhere else. And so, again, using these sort of platform choices, the way we’ve designed our system, lots of experimentation. I think that’s the other thing is just be, able to continuously experiment with the types of things that you can deploy and then have the ability to just keep building more sophisticated modeling capabilities over time.

What I think is it’s so amazing because it is really one-on-one in the playbook for how to use AI in a business sense. If, if just to wrap our conversation like that, just the flow where we went, you identified a problem. There’s an underserved community. It is because they are missing risk.

They have higher perceived risk. You using their data, you are better than the FICO score. You not rely like everybody relies on the FICO score. You said, no, we don’t, we use our data and therefore you could offer better services, more personalized services.

And, that got appreciated. And therefore you again, get more data. And keep the platform safe. I mean, it’s so foundational, particularly when you’re dealing with banking services.

I mean, you’re dealing with people’s money. So I say this a lot that the currency we trade in is trust and that customers have to be able to trust that the platform is going to be safe, that their money is going to be safe. Let me quickly go back to the regulatory point. So what regulators care most about are customer treatment and, you know, making sure you’re not violating laws.

You’re not ripping people off. You’re not doing things that are unethical. And, you know, so, so really making sure that you’re, you’re compliant with the laws and regs. Now, obviously I founded this company, you know, for, with a mission and a belief that we can make the world a better place.

So, so I don’t, have a lot of concerns around ethical treatment, but you know, you need to also adhere to the letter of the law. So, so ensuring that the customer treatment is right. The other thing that regulators care a lot about is safety and soundness. And so making sure that you are not going to be exposed to large risks that you can’t control, whether they’re operational risks, whether they’re, they’re fraud risk, cost of fraud, or it’s a credit risk.

And so, so building tools that you can explain to regulators that say that we understand the risks that we have, this is how we’re managing them and how we’re able to run an institution in a safe and sound manner is incredibly important. And, and to your earlier point, there’s a lot of companies out there that sort of delegate that to their bank because they don’t want to take that on directly. They just want to design a nice interface and, try to capture customers upfront. Very cool.

So now, one of the folks in the audience asked, like, do you give a rate to the FICO score? Do you actually even use the FICO score? We do not on our advanced product, but we do look at credit scores as sort of a one feature in the higher line as credit, but we’re using much more of our, our cashflow modeling system to be able to, to be able to extend credit even for the, the higher loan amounts that we make. So, but it’s, so we really try, have tried to distance ourselves from some of these more traditional systems that, that again, can be very punitive and very exclusionary for people that don’t have a credit history or maybe have, you know, damaged their credit and, and they lack context.

And so by using all of this real time data that we have on our customers, it allows us to make better decisions. And, and very effectively manage the credit risk. And I, I think this is core to the understanding of how AI and data is changing every industry. And I mean, this is like the bigger topic, which we’re, which I’m trying to bring here to the audience is you guys don’t even use the FICO score anymore, right?

Because you have built a better way to detect, a better way to serve. And therefore, that knowledge, that, that has helped. But let, let me tell you, it’s not, and this is, I think just words of advice for, for folks that are, are, you know, thinking about creating systems like this, it requires a lot of investment, but also a lot of ability to learn. And so like one, so we built, you know, our first generation model, we started to collect data, we started to extend loans, you know, it was performing well, but we felt it could perform better.

And so then we introduced, we introduced a second generation of the model, which was doing a much better job at discriminating the goods and the bads and, and the sort of risk sloping. But what was happening is it was very volatile from a customer perspective so that it would see that, you know, one of the things I think I mentioned was like, if your earnings were reduced for, you know, maybe you had two jobs and you had an hourly job. And so your direct deposit came down for a period of time, it would immediately lower your advanced limit. And so, so what would happen is a customer, and this goes back to the sort of, okay, well, I can rely on the payday lender because I’m always going to get this amount of money from them.

You know, if one day you had a $500 advance, but the next day you had a $100 advance, like customers would complain and say, well, wait a second, I was relying on that. I thought I was going to have that $500. So we then introduced the next generation model, which started to address the smoothing factors. And it started to reduce that volatility that our customers were experienced while also allowing more discrimination of the modeling to be able to allow us to give more higher limits, to more people.

And that has been hugely effective. So not only have we seen a reduction in credit losses, but a huge reduction in complaints as well. So, so, and going back to this idea of trust and reliability, being able to provide that solution and having customers rely on it and know that it’s going to be there for them is incredibly important. But that took multiple iterations over the course of several years.

So I have a lot of questions of people, um, who think about, um, your future growth. And I will pick one here. This is from Farid, uh, Goya, Yef, who kind of looked at you and said, this is cool. You’re doing amazing.

Um, how do you capture now shares from company like, um, SoFi, other banks, other people ask for how do you capture shares? Well, like, like some, some groups talks about Canadians, the Canadians need have as well underserved, like how are you going there? How do you, how do you think about, you, you have no data, you have no tool. How do you think about this expansion?

Yeah. And, and there’s, there’s a few different ways we think about it. One is just kind of go to market strategies and how we continue to reach audiences through a variety of different channels. And a lot of that is testing and learning and making sure that we’re attracting the right audiences with the right messaging.

Um, and so we’ve just over, even over the course of this last year, experimented with a number of different channels. Like we’re now back on TV. We’re again, we also impose a lot of discipline on kind of our marketing and how we look at return on ad spend and making sure that channels are producing or bringing in customers that have good economics that are engaging in the right way. And so that, that’s certainly one way.

The other is just continuing to innovate and offer new solutions. So like right now we’re testing, um, an advanced solution that will allow customers to get the first advance, without actually having to do the payroll direct deposit. So they can actually link an account and we can now underwrite using the tools that we’ve built off of data from another bank account. And so we’re trying to learn like, okay, what is the, what is the performance?

How do they engage? Um, but you know, early signals are very positive that customers, you know, they come to us, they open up an account and the idea, if they really need that bridge financing quicker, you know, we’re not going to give them the full amount, but we’ll give them something if they have the right risk profile using that third party data. And like, that’s, you know, again, there’s just a series of innovations and then thinking about what the next set of problems that our customers need us to solve and, and making sure that those are in our pipeline, uh, from a product development, from a technology development standpoint. So, so it’s, it’s go to market, it’s marketing activities, it’s life cycle management.

It’s also thinking about the product development pipeline as well. So let’s, um, let’s shift a little bit gears and, um, that I would like to use a question from, uh, Dr. Uh, Paramita, um, who talks about how does AI help to minimize financial risk in a more global context? Um, if I’m in, in, if I want to be a little bit edgy and I’m European, then I’m saying, ah, Colin, this is all awesome.

So you generated a new score, but you are spying on people, aren’t you? Because you look at behavioral thing and now you create a score. Is that actually fair? And you saying, well, I use that data to offer a new service.

If you take a global perspective and tell, how does it tie it back to the bigger vision you started off? Yeah. I mean, I think if you look at the broader, if you zoom out on a global level, I mean, there’s so many opportunities, like there’s a billion people on this planet that don’t actually have, um, what would be considered a verifiable ID, you know? And so being able to use tools, to help people start to be able to access, uh, you know, whether it’s even just essential services or it’s being able to access payment systems, opening up bank accounts, opening up digital wallets, being able to access micro loan financing, um, could, could change the trajectory of, you know, a, a huge number of people on this planet.

Um, and, and being able to help people with one of the things that I’m actually working on with some of the big multilateral agencies, it’s like how to help, uh, help displaced people. You know, we have one in 70 people on this planet that have been forcibly displaced from their homes, but one of the biggest issues they have is proving identity and, and being able to access some of these services, whether it’s payments or, or lending solutions or access to medical care and education and employment if they’re in a host country. And so, so there’s a number of things that these technologies are going to only help us sort of bring people out of poverty, help, uh, reduce human suffering and hardship. When people are forced to migrate, so there’s just a number of applications that this technology can have, um, and that that are for good, that can really help, you know, preserve the quality of life of so many people around the planet.

And I, I, um, I think this, um, this brings your overall vision was I want to bring banking services because you, you are like, I, I don’t want to disclose your age, but like you have been in the financial industry for a while, you serve a lot of people, uh, you know, you serve people with money, with the banking, um, um, like tools, right? And your vision was, I want to serve now the underserved population. It’s about financial inclusion, helping people feel more included in the financial, formal financial systems, also helping to drive economic development and, and for individuals, for households and families for communities. So when people have access to quality banking services, they have access to credit tools.

They can start building savings habits and they can start having actual, even if it starts with small dollar savings savings, it helps people be more resilient when they have unexpected expenses. And so, so all of these things sort of ladder up towards, you know, how do we just create a more, a fairer, more inclusive system starting in the U S and then thinking about how that can apply more globally. Yes, absolutely. And we, as I said, like, um, I don’t actually know who asked about, um, uh, like, let’s see, like some folks here, uh, like, yeah, Andrew, uh, love say, ask about Canada.

Like how do you go abroad across the border? But I actually think you bigger opportunity lies in the regions of the world. They do have banks, but they have way more underserved people. A hundred percent.

Yeah. I think, you know, when you look at it, I mean, there’s a ton of innovation happening across Africa right now, across Southeast Asia. When you think about the global South, that’s probably where the biggest opportunities are from an inclusion perspective and how to use technologies to be able to help bring people into the formal system and, and access the things that they need to be able to get ahead. Now, uh, can you like, and I, um, we chat a little bit in the, in the prep about it.

Can, can you say a few words, uh, about Grameen bank? Sure. Well, I mean, Grameen was one of the pioneers in microfinance lending, and they solved really early on that. It made more sense to lend to women and the household, and they were going to be more responsible credit risks, but they also pioneered a number of community based, uh, solutions to help people, uh, to help women in their communities sort of pool resources and be able to repay loans and, and start businesses.

Um, and, and they’ve built a platform that has brought many people into a better place from a financial inclusion perspective and helped, uh, households and communities around the world. And so, I mean, it’s a great example, you know, kind of the, the, it’s the OG microfinance lender. Now you have other, other, uh, companies out there. Like I, a couple of weeks ago, I talked to the CEO of Tala, which is doing some great things in Philippines and Africa and other parts of the world, being able to provide microfinance solutions.

There’s, there’s lenders like Kiva and, um, and branch. And there’s, so there’s a variety of them out there that are trying to provide these types of solutions on the back of what Grameen has done, and also using more advanced technology and, advanced technologies, machine learning, AI type solutions. Fun fact, um, Colin, as I got married, I did not want to get, um, any presents. I actually started the Kiva fund.

Did you? Yes, I did. And, um, then I, I spent, I think I like, I like in Kiva, you don’t get paid back, right? You only can reinvest.

So, um, over time I lost all the money there, but I, I think I invested for like, like a good couple of years. I’ll let Vishal know. He’s the CEO of Kiva. Yeah.

That’s funny. So the whole point here is we are using data. Ramin Bang did, you do, did in order to calculate risk better. And make better risk decisions every day.

Make better risk decisions. And we are like, we are from the Johnson school, right? We are in a, in a business environment. And would we, in order to bring technology and business together, if we lower the overall risk, then we can deploy investments better and make everything better off.

And have better outcomes for consumers and business at the end of the day. But the other, we should just take a minute to talk about like, you know, we’re at such early innings here too. So like all the things that we’ve built and the tooling and the infrastructure, um, I think sets us up and, you know, kind of a culture of experimentation and learning, but sets us up for this next generation. So now with the generative, AI, large language models, and, and importantly, agentic AI, I think we’re now entering into a whole new field of where, you know, we start to move towards, um, more autonomous, like decision-making on the parts of these models, which is, I mean, it’s, it’s exciting.

It’s a little, a little frightening that they, but they’re going to be able to really drive that next level of transformation. And you move from a kind of reactive to more of a proactive AI where these tools are monitoring this, this information, they’re serving up advice, they’re being able to, um, you know, visualize data in a, in a much more robust, meaningful, personalized way. And so you’re going to start to see, they can actually, if you give permission to some of these models, they can go make trends, do transactions on your behalf. So from a commerce perspective, you can say, I want to, I want to go to Humber and then they’ll say, okay, well, what did the, here are the hotels and how much do you want to spend and where do you want to eat?

And they’ll go execute it all for you. I mean, so, so I think we’re really, just at a tipping point now where these tools, and now you saw obviously the announcement of Johnny Ives and Sam, all that, you know, they’re, they’re thinking about this and like how to supercharge this next generation of models that are going to be able to really change the way we live our lives. And so, and that’s certainly going to have an impact on banking. Now, I feel like the early investments that we’ve made in terms of building a platform that can start to embrace some of this technology is going to be a critical competitive advantage.

But I think we, there’s so much, we’re still going to, we’re still going to learn in the, in the kind of months and years ahead. So, yes. And I, like you, you probably saw my notes here because Marlin actually had asked exactly that question too. Like, okay, well, what is now the, trends?

And let me, you, you gave a lot of use cases about you going to Hamburg and then the tool is booking for you as well as making a recommendation, figuring out what’s for you given financial backing, the right setup, hotel, whatsoever. And let me break this down at a, into a tools layer for us to follow around, because much of what we discussed today can be put into the bracket of traditional AI exposed, very traditional tool. That’s right. We manage different information pieces to calculate a credit risk.

The value that Varo bank brought in was you recorded the right data to make that, that prediction and to reduce the risk. Now to the question we had earlier from one of the users where they said, okay, and how do you build trust for those you use more generative tools that you have a chat bot that you make it easy as well as product tools, making like the advancements now going forward, the direction and where this whole industry is developing. And this was the announcement we had just Google IO, right? And we had some Altman or open AI introduced, you know, introducing way more on the agentic universe that once you have that information, you can now start interacting on a digital level, meaning you can like generate of AI.

You give advice. Okay. You, you just handed in money. I gave you an advance because I did traditional.

Now I use generative AI to tell you, please save money. It’s good for you. And I have an agent that can use whatever I want to spend to, uh, either savings or booking something and having those workflows, meaning. Absolutely.

We are very early on in a, in a, in that space. I say this because I’m obviously an e-commerce, right? So an e-commerce people used to search and then buy. And, um, nowadays they have a discussion with chat GPT, or I think you’re going to see much more of it sort of like voice based.

And it’s going to be, yeah. Yeah. And there is where I’m like, for my own work, right? I actually trying to, I’m the sales intelligence layer and we are informing them, Chad GPT, informing perplexity so that brands get better into that space and be better displaced.

So it’s very early on for FinTech as well. But all the tools are there. The tooling is, yeah, the investments that are being made in the infrastructure are so important. And then, you know, as folks that are thinking about setting up their own companies and getting funding, but I think getting that the infrastructure layers right early on are critically important because it just gives you the platform to run a lot of experiments, to learn rapidly, and then, you know, build the right models, see how they’re working for your customer and then just keep iterating.

But, yeah. And so for anybody who actually thinks about starting a company, listen out, listen to how Colin did it. He figured out first the problem. He figured out a solution, which was a technical solution.

He used traditional tools first. It wasn’t the, like, isn’t the hippest thing on town. But once he had the data, he could iterate on the product and make it better and personalized and extend. And, well, you step back from being a CEO.

What’s, Farid actually had a question of what’s next. What’s next? Well, for Varo or for me? For you, for you.

For me. Well, like I said, I mean, you know, I’ve actually now handed my day-to-day CEO responsibilities off to a good friend of mine who is, you know, also well-known in the industry and an incredible leader, which is freeing me up to be able to start to tackle some of these broader global issues and challenges and thinking about financial inclusion, thinking about migration, thinking about things that are just so critical for humanity and the lives that we live. And so trying to leverage some of these tools and use cases around the world that will hopefully, you know, drive a next level of inclusion, a next level of access, hopefully thinking about economic development from a different perspective. So I think there’s just so much, so much opportunity and runway ahead.

And so I’m excited to sort of build off of the foundation that Varo has certainly created to be able to try to apply some of these learnings on a broader scale. And all of this, and coming back to the picture behind you, won’t happen in a day. Won’t happen in a day. Like Rome took a while, but it’s going to be as beautiful and as helpful as we build up this technology landscape.

Well, thank you for the opportunity to chat with you again, Lutz. It’s always a pleasure. And hopefully there are some ideas that are inspiring future Cornellians or current Cornellians to go off and make the world a better place. Thank you.

Go red, right? Absolutely. That’s right. Thank you, everybody.

Big red. All right. Thanks for joining this program. And thank you so much, Colin, for being on with us today.

My pleasure. Thanks.! Bye! Thank you.

Thank you.