• Subscribe
  • Contact Us
  • About LendIt Fintech News
  • Home
  • Menu Item
  • Menu Item
  • Menu Item
  • Menu Item

Lend Academy

LendIt Fintech News: Daily Coverage of Fintech & Online Lending


  • Editorial
  • Daily News
  • Podcast
  • Investor Forum
  • Events

Podcast 285: Nicky Goulimis of Nova Credit

The COO and co-founder of Nova Credit discusses immigrant credit data, partnering with large financial institutions and how Ellis can help immigrants find their next credit card

February 12, 2021 By Peter Renton Leave a Comment

Views: 129

Most countries in the world today have some kind of centralized repository for credit data. But this data exists in silos, cut off from the rest of the world. For immigrants moving to a different country this has been a real problem. A doctor with a 20-year credit history earning $250,000 a year in, say, Australia is treated as a thin file/no file credit applicant in the U.S. and is rejected for a credit card with a $500 limit. This makes no sense.

Our next guest on the Lend Academy Podcast is Nicky Goulimis, the COO and co-founder of Nova Credit, who have made it their mission to solve this problem. They have built the infrastructure to normalize the credit data from 15 countries (and counting) around the world so that lenders can use this data just as they would use American credit data. For more background on the Nova Credit story listen to my interview with CEO Misha Esipov from 2017.

In this podcast you will learn:

  • The story that really got Nova Credit going.
  • What they have built at Nova Credit.
  • The quality of credit data around the world.
  • The number of credit bureaus they are working with today.
  • Why it is important for them to serve everyone.
  • How their partnership with American Express is going.
  • The other partners they are working with.
  • The different credit use cases for immigrant data.
  • Why they decided to start a direct to consumer initiative.
  • How they were impacted by the pandemic.
  • What has changed in their conversations with mainstream banks.
  • How they are doing with the personal loan segment.
  • How the immigrant credit problem has now been solved.
  • Nicky’s thoughts on building a fair credit system.
  • What is next for Nova Credit.

This episode of the Lend Academy Podcast is sponsored by LendIt Fintech USA, the world’s largest fintech event dedicated to lending and digital banking.

Download a PDF of the transcription of Podcast 285 – Nicky Goulimis.

Click to Read Podcast Transcription (Full Text Version) Below

PODCAST TRANSCRIPTION SESSION NO. 285-NICKY GOULIMIS

Welcome to the Lend Academy Podcast, Episode No. 285, this is your host, Peter Renton, Founder of Lend Academy and Co-Founder of LendIt Fintech.

(music)

Today’s episode is sponsored by LendIt Fintech USA, the world’s largest fintech event dedicated to lending and digital banking. LendIt’s flagship event is happening online this year on April 27th to 29th with the possibility of an exclusive VIP in-person component. The verdict is in on LendIt’s 2020 event that was held online with many people saying it was the best virtual event they’d ever attended. LendIt is setting the bar even higher in 2021, so join the fintech community at LendIt Fintech USA where you will meet the people who matter, learn from the experts and get business done. Sign up today at lendit.com/usa

Peter Renton: Today on the show, I’m delighted to welcome Nicky Goulimis, she is the Co-Founder and COO of Nova Credit. Now, Nova’s a really interesting company, we’ve had her partner, Misha, on the show before, we’ll link to that in the show notes. I wanted to get Nicky on the show because they’ve made a lot of traction in the last several years and we wanted to talk about what they’ve done, how they’re helping immigrants, something that’s near and dear to my heart as someone who really struggled coming into this country as an immigrant.

So really, we talk about what they’ve done, their partnership with American Express which is groundbreaking and what else they’re doing even beyond just helping out with credit data and also all the different ways that they’re helping immigrants today. We talk about that and we talk about what’s coming down the pipe. It was a fascinating interview, hope you enjoy the show.

Welcome to the podcast, Nicky!

Nicky Goulimis: Hey, Peter, it’s good to be here.

Peter: Okay. So, I’d like to get this thing started by delving into some background and like me, you are an immigrant to this country. I know it’s part of the story of Nova Credit so why don’t you get started, you know, at the start of your career and how you came to this country and what you’ve been doing.

Nicky: Sure. So, I came to the US for business school from the UK, I’ve never lived in the US before. I think I visited a couple of times and so came here for grad school and immediately ran into a lot of the challenges that immigrants face trying to figure out how to buy groceries without being completely overwhelmed by the amount of choice in American supermarkets…..

Peter: (laughs) I remember that, yes.

Nicky: ….trying to understand what a health insurance was and how to make payments, what it did for people like me. You know, some of the biggest challenges was around financial access, you know, just renting an apartment without putting down a six-month deposit, getting a student loan because I was studying at that time at a reasonable rate, being able to get a credit card. You know, I’d applied to so many of these different products and was getting rejected, all because despite having the credit history and the financial footprint in the UK, I had no financial identity here in the US. That is a challenge that not only I experienced, but millions of people like me.

Peter: Well, I came back here before….I really was over here…..I moved over here in ‘91 and that just shows you how old I am, but there was not real Internet even back then, you know, of any discrimination. I got a Compuserve account to communicate with my friend back in Australia, but it’s the hardest thing to do to get into the financial system when you’re out of that completely and, yeah, I would love to have Nova Credit being available back then. But, anyway, why don’t we start with the founding story. You guys met at business school, right, just tell us about the aha moment, you know, we should really start a business doing this.

Nicky: Personally, I don’t know if you have this like beautiful aha moment to sort of tell that story, but it’s definitely a build up and for me, a lot of it was just loving working with my Co-Founder, loving building a company from scratch and getting really excited about the space and seeing that there was a much bigger market than we had imagined.

But, there was one aha moment which I remember a lot which was we had been pitching this one credit union and we had like cold emailed them, had a couple of conversations with their chief risk officer, it went super well, wow, this credit union serving immigrants, they are excited about our product. This shows us that there’s a real market and a real opportunity and yet like I kept emailing, I kept following up after this great meeting and they were never replying and I was getting more and more miffed and we were becoming more disheartened about….oh, maybe that meeting didn’t go so well, who knows.

And then, one day, actually my parents were visiting and we were driving down from San Francisco down to Santa Cruz, I remember, so beautiful the whole time…I’m not a great driver, I’m very cautious and i have my like phone in front of me getting directions and it kept ringing and I was like, stop ringing, I’m trying to drive. Eventually, I pulled over and picked up the phone and it was this chief risk officer saying, hey, like why are you not replying to my emails, something’s not working, like we really wanted to follow up here and actually build something together. I checked my inbox properly and realized that his emails were going to spam.

Peter: Oh dear.

Nicky: It’s not the best of starts, but the fact that someone would go out of their way to find my phone number to call me was just one of these pieces where I’ll say, okay, this is not just a good idea, this is actually something that people want and been waiting for and, you know, there’s still a happy customer to this day.

Peter: Alright, good to know. So, why don’t we just step back for a second and talk about what actually you do and how you’ve created this system.

Nicky: Yeah, sure. So, what we’ve built is basically a lot of infrastructure. So, we’ve connected all of the international credit bureau databases as well as some alternative data into a single system so we had to go around the world and build partnerships. We then accessed that data in that country, wherever it is, we bring it to the US, we post process it, we standardize it so we map it all to a single format and a set of standard industry metrics and then we deliver it to financial institutions, to property managers, to telcos.

So, if you look at our partnership, for instance, with American Express, in a question of seconds within their application, they embed Nova Credit, a consumer is able to pull that international credit history, Amex is able to instantly access it in their systems to algorithmically underwrite on the basis of the data that they’re getting and then to accept someone or issue them a credit card, but previously, they would have rejected them because they would have had no data.

Peter: Yeah. And so, how many countries are you working with because, obviously, not every country has a credit bureau or any kind of centralized credit data at all, obviously, some credit bureaus are better than others. Like how many countries have good credit data?

Nicky: Well, I actually think you’d be surprised. So, I think, part of it is like why now, why start this business now and I think the big reason is that there is more high quality data in credit bureaus around the world than has ever existed and I think if you look about 30 years ago, there were 30, 40 or so credit registries, credit bureaus around the world.

Today, you have well over 200 which means that countries have often multiple bureaus, a lot of these bureaus are new so they’re actually set up with the best technologies with, frankly, like superior user matching than we have in the US which is not driven over an SSN where, sorry to say this, Peter, I can probably find your SSN in the Internet within five minutes. But, you know, that’s actually based off biometric data and other pieces and so…actually, you find there’s extraordinary, high quality credit bureaus all around the world and they’re constantly opening new markets.

So, I think, you know, the Canadian Credit Bureau opened a couple of years ago, Saudi Bureau like ten years ago, you’re constantly seeing more and more bureaus opening and so there is more and more  of that data available. So, us at Nova Credit, we work with about 15 countries today, we have multiple providers for a few of our markets and different types of providers. We’re on, I think, every continent but Antarctica at this point which is super exciting.

Our strategy is two-fold, one is just like thinking about where the biggest migrant flows/come from to the US like India, Mexico, China, the top three countries, and then you have a number of others after that so it’s really focused on those markets and then expanded beyond that.

The second piece is like really got to catch them all, as I said at the start, we’re building infrastructure, we’re an infrastructure company and so if we want to build a fair earning piece of financial system, we have to build all of the world. It is a long game.

Peter: Yeah. Because in the US, I imagine, immigration has slowed down a little, but this is the country where everyone wants to come to, I mean, I grew up in Australia and my Dad started a business, he always wanted to expand it to America and how I ended up over here. But, you could be in Cote D’Ivoire or Uruguay and people want to come here and there are people coming here from probably every country in the world so how do you decide……obviously, you have 15, you probably got what, 90% of immigrants, I mean, are you trying to get that last 10% as well?

Nicky: We, definitely, are so, again, part of our strategy is serving everyone and, again, we’re trying to build this global financial system and so we need that. That said, sometimes there is more value for us going deeper in one country so getting an additional provider in a country or getting not just credit data but maybe bank transaction data in that country before going to build at smaller countries. So, we do have some prioritization, matrices and analytics that we do have to do internally and to make sure that we’re like deploying the product build in the right places.

Peter: Right, right, okay. So, I want to talk about, you already mentioned it, the American Express partnership, this is a big deal. In fact, for me American Express was my saving grace when I came over here because I could not get a credit card at all. I had an American Express card from Australia that was still being billed in Australian dollars, but American Express is a very international firm and that’s what I used. I’d go around buying groceries with American Express card that was being billed in Australian dollars. So, tell us about that partnership, how it came together and just how it’s been going. I know it’s been over a year now so I’d love to get an update.

Nicky:  Yeah. We’ve been partnered with American Express for a couple of years now, it’s been an awesome journey. So, we started off during a pilot with them back in 2018 and then it was meant to be just two/three months and then it went a while that we just kept it running and kept it running and then we did a full integration and a full rollout in 2019 and have been scaling the partnership since then.

I think for American Express and what’s been so powerful about us is, you know, first of all, they are really international bank or financial institution should I say and we find, therefore, they kind of understand the problem really well. They have people who…people like you, you said like, hey, I was your customer in Australia, why can’t I be your customer in the US, why am I using an Australian currency denominated card, this makes no sense.

So, they really understand the problem and also just their brand is around backing people going places. You know, they have always been a company that’s oriented on these components and so what we represent for them is a big acquisition opportunity to tap into all of their new arrivals into the United States.

And so, that’s been a great piece and we’ve seen, you know, them being able to approve people who they would, otherwise, reject to get customers who are performing really well, to not take on incremental risk. We have some really great outcomes from that and I think that’s sending the message and, you know, we’re seeing this across the industry. There is a massive opportunity here and how can this industry capture it, but really, if you think about….Ash Gupta’s actually former chief risk officer of American Express is an advisor and good friend to the company.

The way he always frames it is like three ways for a company in a credit space to grow, either you’re hoping for like the children of people who already are confident that you can underwrite on the basis of them being children or you’re hoping for people basically graduating university or entering the job market in any way that you can start serving or the third is immigration and I think the pandemic proved this out, I mean, in a year with lower immigration, you know, growth rates in America and frankly, across the OECD are incredibly low and, actually, where population growth is coming from is immigrants.

And so, you know, I think often we talk about the immigrant segment, the newcomer segment as if it’s this niche, as if it’s this small opportunity, but this is the trend that’s firing the future. I think the full cost at the what is it, the Office of National Statistics in the US, is that 80% of population growth is going to come from immigrants and it’s not a political statement, it’s just like a fact of the aging population and how to support the economy.

Peter: Right. People having less kids, that sort of thing, yeah.

Nicky: Exactly.

Peter: So, I wanted to just go back….you told me a story a while back where you said…how you started the American Express relationship, I think the listeners will get a kick out of this so tell us how you first went to approach American Express.

Nicky: Okay, I get a little embarrassed. So, I was talking to one of our advisers and he was saying, you know, American Express might be interested. There’s this person named Vernon Marshall and you should reach out to him, he’s talked to me about this before, but I can introduce you actually because right now, we’re working on something else with them. So, okay, great and I sort of wrote down the name and I remember saying to myself, well, what am I going to do, cold email this person, they’re never going to respond, but it was a rainy day, I didn’t have a lot to do so I shot him off a cold LinkedIn message and to my total surprise, he responded.

It turned out that he was based in London and I was visiting my parents for the holidays so we were able to organize like a 15/20-minute meeting and in the end he spent like an hour and a half with me and was super engaged and super excited about this because he said, you know, exactly what every other credit risk and marketing leader in the space will tell you which is like we’ve known about this problem for decades so you’re not telling me anything new, but we’ve been waiting for a solution. I was so excited when he said this.

So, he then introduced me over to the rest of the team at American Express and we had this amazing person just take it on and steward it as his baby and drive it with full determination and results. Yeah, it’s kind of been an incredible journey since then, but I think I hadn’t quite realized how senior Vernon was and I would probably be embarrassed to cold message him today, but it all worked out for the best.

Peter: It all worked out, yeah.

Nicky: LinkedIn, you should all use it. (laughs)

Peter: Right, okay. So then, what about…obviously, there’s lots of other large institutions that have the exact same problem that American Express has, I mean, what can you share about other partnerships that you’ve got today?

Nicky: Yeah. I mean, first of all, I would say that we really recognize the problems that immigrants face are across multiple areas. So, one area, for instance, is credit card which we’ve talked about and we are working with additional credit card partners over time, but the other areas, apartment rental can be extraordinarily challenging. And so, we’ve actually been working with some of the largest tenant screening platforms in the US, Yardi and Best Advantage, in order to power landlords to not have to request additional deposits, additional verification and actually be able to just approve these people like any other tenant that they would be reviewing. We’ve been working in the telcos space, things like device financing, we work across a range of credit use cases so student loan, mortgage, car lease.

We’re just thinking about whatever it is that an immigrant needs and I think, you know, what I’m really excited about is the opportunity to take this global because, you know, Peter, you were saying, people want to come to America and that’s very true. People also want to go to Australia, to Singapore, to the UK, there’s many other countries that are huge migrant hubs and that face exactly these challenges and where migration is like it’d only go up and not down. And so, we’re excited about some of our partners and soon, hopefully, announcing soon. You know, some of our partners are pulling us into other geographies as well and I think that’s where a lot of the opportunity lies.

Peter: Interesting, interesting. Okay, I want to switch gear, I’m on your website on my other screen over here and ……..

Nicky:  Oh, oh, (inaudible)

Peter: (laughs) Not at all, it says apply for US credit cards with confidence with Ellis, tell me about Ellis, who’s Ellis?

Nicky: (laughs) Who is Ellis? So, I’ll tell you about it and then I’ll give you like an overview of the pains of product naming and startups. So, everything I’ve talked to you about is B2B, it’s us providing data behind the scenes.

Peter: Right.

Nicky: What happened is that we launched our company, we’re B2B, we’re lucky to get some press, thank you for featuring us in different fora and we started to get people writing in to us and saying like, hey, I just moved from Brazil, can you help me get a student loan or can you help me rent this apartment. We’d always say to them, no, no, no, we’re a B2B company. After a certain point, we thought, okay this is a great opportunity for us to not only to serve these consumers, but also to be able to send leads to our products and help them grow as well.

So, sort of last year, we launched this platform Ellis which I would say is ….we’re still experimenting and still growing it, but what it includes is ways to see your international credit score or ways to get information, where are the best Indian grocery stores in the Bay Area or why do you need health insurance and how does it work. So, there’s content on that too and then, you know, if you’re looking for a credit card provider or other establishments, click on one of our partners here.

We’re still growing it in different ways so we plan to add more countries, more products, different features to it, but it’s been really engaging and very fun to have a direct line to consumers and, frankly, you know, getting to hear some of their stories as well.  And then, the name Ellis is, obviously, Ellis Island, which, you know, we want to be a migrant port of call and a place to answer their questions and help them build community. And then, we argued about a whole bunch of other names and the conversation was interminable. (Peter laughs)

Peter: Okay, great. You know, it’s funny, I mean, because I came over here, there’s an expat population of just about every country and that’s how….I mean, it’s hard to navigate the system when you don’t know. Again, you know, what you’re providing here is sort of a more objective kind of a way to gather this information because you don’t realize Americans take it for granted.

When you come over here, everything is unknown and it’s so different from any other country even Canada, it’s just very different here so there’s a lot to learn. So, it’s great, I think you could have the port of call, as you say, for immigrants and really….you can do a lot with that right there, for sure.

Okay, so we touched on it, but I’d love to get a sense of the pandemic and the impact….you know, immigration obviously went way down, it was already going down, the Trump administration did not have a pro-immigration stance and with the pandemic that exacerbated that, how’s that affected your business?

Nicky: Oh, it’s definitely a huge affect, I mean, if you looked at….you know, we don’t have perfect numbers on this, but just to qualify the statement you just made, I think immigration flows last year were 10 or 20% of what they are and, yes, they’d actually been growing. So, definitely, bad news for us and bad news for us just in terms of like, you know, short term volumes that our customers were seeing, but also, I think, you may recall that many banks had their hands full with other priorities to focus on during the….so it definitely…we were super fortunate we had just raised a really big Series B at the start of last year.

So, we raised $50 Million from Kleiner Perkins, Canopy and others so we had the sort of capital to weather the storm, but what had ended up pushing us to do was really to focus on longer term initiatives. So, we really invested a lot in product build last year, not just adding more countries, but adding more different types of data sets, tweaking our user experiences, also investing in some of these longer term partnerships and figuring out new solutions for partners or bigger opportunities for them in other countries, things like that.

So, it was a tough year, I think we can all attest to that and there’s no pretending otherwise, but sort of entering 2021, it’s been great that we’ve already seen like volume starting to ramp, we’re seeing a lot of great news already. Just yesterday, which was the inauguration, there were changes to some of the previous executive orders on immigration, the vaccination program is going extraordinarily well and we’re seeing the…..you know, banks are really coming back and starting to invest in growth having previously retrenched from the market.

Peter: Right, right, So, let’s talk about those banks for a minute, like when you’re talking with them, obviously, you have a range of different things you can offer now. I mean, what are some of the innovative tools that you’re seeing your banking customers use?

Nicky: You know, I think last year as really the year of like banks moving away from pure credit reporting, to start looking seriously at other data sets. Now, obviously, the fintech community….you’ve been talking about this for a while, but I think to see mainstream banks start to do that has been significant and that’s just been because first of all, they needed like much more active data sets, not just lagging data sets, given how quickly the economy was changing because credit scores were sort of going up, given some of the various government programs and that’s why they weren’t able to get a very accurate picture of credit risk and so it forced them to like look outside.

And, I think, what’s really exciting about this sort of investment in bank transaction data or even other alternative data sets as part of underwriting is that ……you know, I’m not like throw out the baby with the bathwater school of thought, I don’t think the credit reports are terrible, you know, we built a credit report business, we think this data is like highly predictive and frankly, the most fast solution that you can use. But, I think the current credit system and the credit industry works really well, call it for 80% of the people, and then I think that’s like maybe 20% and I’d include immigrants, other thin file Americans or those people who are sort of at the fringes of the credit system and you get really discouraged.

And so, there are a lot of analyses out there that show that as soon as you bring in transaction data or other alternative data, many, many millions more people become scorable and become underwritable or that you can also even just spare them super earner processes in terms of the actual application which is still amazing to me how many times you have to follow up with pen and paper and calls and this and that rather than having the seamless fintech application that we all aspire to.

Peter: Right, right. Which brings me to another question about…….you talked about credit cards and that’s the biggest product, I imagine, for personal credit, but what about the personal loans and the fintech sector’s really been the pioneers here in personal loans, a lot of them are pretty open to using alternative data sources…I mean, I would have thought by now that you’d be really heavily into many of the fintech lenders, can you give us some sort of sense on, you know, how you’re doing there?

Nicky: Yeah. So, we definitely….we do work with some of the big names in fintech. Yeah, so we’ve had some great results there, we’ve seen a lot of traction. I do think that they are very fast moving, very eager to work with new data sets and bring more people into the fold, whether that’s immigrants or thin file Americans, I think varies, depends on the provider, and what they’re really focused on, because I actually think like personal loan tends to be a little bit more scarce around immigrants. It’s often a debt consolidation solution meaning they previously have some debts outstanding already, but, yeah, I think it’s a terrific segment and I think like they have been….I mean, I think they’ve invested a ton in some of these bank transaction data analytics teams.

I think, maybe, what they’re looking for more is, you know, how do they get all of the data sets overnight without having to spend all of their engineering resources on it. And then, not just…once you have these data sets, but then how do you actually understand and build analytics on top. I’d say that’s like where our specialty comes in which is saying that we’re not just giving you raw data in any data that we deliver, we always will provide some insights and some intelligence on top which I think is where the industry is going to have to move to ultimately.

Peter: Right, yeah, yeah. So then, I was talking with your partner, Misha, the CEO of Nova Credit, a few months ago and he said something which surprised me. I would like you to kind of ….

Nicky: Uh, oh (laughs)

Peter: (laughs)…I would like you to sort of really tease it out for the audience here. He basically said that the immigrant credit challenge has been solved, like what you’ve been able to do is ….you’d be been able to solve this problem. Do you agree with that and maybe you could tell us, really has it been solved?

Nicky: Of course, I have to agree. (Peter laughs) We have a loyal partnership. No, I think that in the years that we’ve been building this business, we can now cover the majority of immigrants arriving to the US, we’ve built the infrastructure, we’ve built tools. We do work across a range of use cases, but I think we still are scaling our partnerships and so I think the tool is there, I think the adaption is there, but there’s still a way to go.

You know, part of our vision is like no immigrant should run into unnecessary hurdles or not be able to get approved for the right products that are appropriate for them when they come into the States. I don’t think we’ve realized that vision just yet, but I think, especially now, we can be really proud of just like where we’re at product-wise and it’s our job to step further on the gas and make sure this gets adapted throughout the industry.

Peter: So then, would you say that data quality around the world is good enough that US lenders really can underwrite…obviously, you said you have 15 countries, I’m just wondering because won’t some countries have worse credit quality, the quality of the data itself would not be worse than in others?

Nicky: It sort of comes back to the conversation we were having earlier, I was saying like, you know, some of these systems are just so much more modern that the actual quality of the data ends up being higher than the US data quality, I think you have challenges around penetration. So, I think, particularly, in some emerging markets there isn’t as much credit being issued in the first place and then less of it is being sub-reported and recorded in different ways or in formats that are accessible.

However, that’s where I think, again….like when you think about some of those markets that’s where like…. you know, transaction data that becomes really interesting as well as a sort of supplement so, you know, I think maybe the ratio of credit data to alternative data varies by market that you really need to be able to get an underwritable image of someone.

The other thing I’ll say is that this is kind of a….it’s like a bizarre…it struck me as very bizarre when I sort of realized it, but, you know, in the credit space an absence of bad data is also a good signal. So, even knowing that someone hasn’t left a whole bunch of outstanding obligations somewhere can be a really good signal to be able to get the context and directing them.

Peter: Right.

Nicky: So, yes. And then, you know, when it comes to sort of maybe your sort of implied point around is like do people in the industry trust this, the answer is, yes, we’ve been able to see that by pulling off not just one but multiple partnerships with Fortune 500 companies and we see this in like….you know, our pitch, Peter, is not always a very exciting one, like this is so boring, this is the same credit bureau data you’ve been working with for decades it happens to be from another country and like if you’re talking to Citibank, they were saying, you’re actually using this data in Citi in India so why are you using it in the States. So, no, I think this data is pretty darn good.

Peter: Okay. So, we’re almost out of time, but a couple of more things I want to get to. You know, we’ve talked a lot about international credit scores and credit data, but when it comes to…obviously, there’s thin file, thin file consumers in this country and every country, but if you’re running a lending platform today, how can you help to build a fair and inclusive financial system, let’s just say even beyond credit?

Nicky: Oh, you’ve just promoted me to lender, interesting.

Peter: Is it too tough for you? (laughs)

Nicky: I think what really matters in the space is responsible issuance of credit and I think it’s very easy to start saying things like credit is a human right and things like that and I think that’s super dangerous. So, the questions are like how do you deliver credit in a way that is not predatory so you’re not giving someone a loan obligation that they are unable to handle even if it ends up being profitable for you, you’re not hurting your customer, that’s maybe like the first tenor.

I think the second one is like how do you enable your customer base to put their best foot forward, whether that be like showcasing your data or showcasing your employment, just making sure that you’ve done the work to understand your segment deeply and enable themselves to portray themselves in the best light. I think often what happens is like you do a good enough job for a significant proportion of your applicants, but maybe not all of them or maybe you’re mis-characterizing the rest along the way.

I think that the last piece is also just like what is the application flow, how well do people understand what is going on and, you know, especially with data sets that are consumer-permissioned, are consumers really understanding what they’re opting into, are they getting to see their data, are they getting to control it, are they getting to revoke that consent, should they choose to. So, I think there’s definitely an education piece around credit as well that’s vital.

You know, one final thing I’ll say is that I’ve talked a lot about credit and underwriting, but there’s also just like a basic piece around identity verification and making sure you understand the identity of who’s applying and that you’re able to like get them through that such that you can prove that they are who they say they are and make them a good customer. So, yeah, that’s my four-point plan for when you make it…..

Peter: Alright, fair enough and then that last point, you could do a whole podcast system on that point. Okay then, so last question, what’s on top for Nova Credit this year, what’s coming down the pipe?

Nicky: A lot. We are going global so we are very much rearing up for our first international market which I’m super, super excited for and then the second piece is that we’re starting to launch new products and scale them so that is something that we plan to have some more news on, hopefully, early Q2. We’ve been doing some betas with some early customers around some product extensions and seen some great results so looking to continue to bring more people into the credit fold.

Peter: Okay, Nicky, we’ll have to leave it there. It’s always great to chat with you and best of luck, thanks for coming on the show.

Nicky: Thank you so much.

Peter: Okay, see you.

You know, I was chatting with Nicky after we stopped recording. To me, it’s amazing that this hadn’t been solved before Nova Credit came along because people have been talking about alternative data particularly in the fintech industry for well over a decade and yet the whole immigrant population….no one had really tried to solve it. I mean, American Express couldn’t solve it, she talked about Citi, they’re using data in various different places, but no one had sort of built the infrastructure.

And that, I think, is really a testament to the team at Nova Credit that they’ve been able to really solve the problem, as Misha said, as Nicky really agreed with there that they’ve solved the problem of immigrant information, credit information flowing across borders. It should never have been a silo, it certainly …you know, it shouldn’t have been a silo certainly for a long time, fact that they’ve solved that problem really means that there’s more places they can go here, they’ve got that expertise in solving difficult data problems. I’m excited to see what is going to be coming down the track for them going forward.

Anyway on that note, I will sign off. I very much appreciate your listening and I’ll catch you next time. Bye.

Today’s episode was sponsored by LendIt Fintech USA, the world’s largest fintech event dedicated to lending and digital banking. LendIt’s flagship event is happening online this year on April 27th to 29th with the possibility of an exclusive VIP in-person component. The verdict is in on LendIt’s 2020 event that was held online with many people saying it was the best virtual event they’d ever attended. LendIt is setting the bar even higher in 2021, so join the fintech community at LendIt Fintech USA where you will meet the people who matter, learn from the experts and get business done. Sign up today at lendit.com/usa.

You can subscribe to the Lend Academy Podcast via iTunes or Stitcher. To listen to this podcast episode there is an audio player directly below or you can download the MP3 file here.

https://traffic.libsyn.com/secure/lendacademy/Podcast-285.mp3

Podcast: Play in new window | Download | Embed

Subscribe: Apple Podcasts | Android | RSS

Filed Under: Lending and Fintech Podcast Tagged With: American Express, Credit Bureau, credit data, immigrants, nova credit

Views: 129

Podcast 239: Freddy Kelly of Credit Kudos

The CEO and co-founder of Credit Kudos talks open banking and building an alternative credit bureau in the UK

March 20, 2020 By Peter Renton Leave a Comment

Views: 275

The big three credit bureaus, whether in the UK or the US, have excellent coverage on the majority of consumers. But for those people who have never applied for credit, they tend to have very little information. Now, many people have a bank account who have no credit file or a thin file and the data from their bank account, if analyzed well, can prove to be an excellent predictor of creditworthiness.

Our next guest on the Lend Academy Podcast is Freddy Kelly, the CEO and co-founder of Credit Kudos. They are an alternative credit bureau that uses the power of open banking to score a far wider segment of the population than the traditional bureaus.

In this podcast you will learn:

  • The mission of Credit Kudos.
  • The data they use that is different to the traditional credit bureaus.
  • Why open banking is core to their offering.
  • How Credit Kudos fits into the loan application journey.
  • How they can better understand a consumer’s ability to repay.
  • The types of lenders they are working with today.
  • The traction they are getting with consumers.
  • Why it is important to provide a worthwhile value exchange to consumers.
  • Credit Kudos’ approach to data analytics.
  • Why bank transaction data is such a strong signal for creditworthiness.
  • Why Credit Kudos is for more than just the thin file segment.
  • How their new Liquidity Score is used.
  • How their partnership with ClearScore works.
  • The challenge of how to embed new systems into an existing decision process.
  • Their plans for implementations beyond the UK.
  • What is next for Credit Kudos.

This episode of the Lend Academy Podcast is sponsored by LendIt Fintech USA 2020, the world’s largest fintech event dedicated to lending and digital banking.

Download a PDF of the transcription of Podcast 239 – Freddy Kelly.

Click to Read Podcast Transcription (Full Text Version) Below

PODCAST TRANSCRIPTION SESSION NO. 239–FREDDY KELLY

Welcome to the Lend Academy Podcast, Episode No. 239, this is your host, Peter Renton, Founder of Lend Academy and Co-Founder of the LendIt Fintech Conference.

(music)

Today’s episode is sponsored by LendIt Fintech USA, the world’s largest fintech event dedicated to lending and digital banking. It’s happening on May 13th and 14th, 2020, at the Javits Center in New York City. Lending and banking are converging and LendIt Fintech immerses you in the most important trends of the day. Meet the people who matter, learn from the experts and get business done. LendIt Fintech, lending and banking connected. Go to lendit.com/usa to register.

Peter Renton: Today on the show, I’m delighted to welcome Freddy Kelly, he is the CEO and Founder of Credit Kudos. Now, Credit Kudos is a really interesting company, they are what we would call an alternative credit bureau, or as they say in the UK a credit reference agency. They’re using the power of open banking to be able to bring more people into the credit system, more people in expanding the credit box for a lot of lenders because they’re not really focused on credit history like the traditional bureaus. They are using the power of open banking to be able to connect. People can connect their banking information so they can get an accurate credit assessment on a much broader cross section of people.

So, we talk about exactly how they do that, we talk about the different data points they’re using, you know, the type of lenders they’re going after and who are using their product today, and we talk about just some of the attitudes towards open banking and the success rates that they’re getting. We talk about some of the new interesting relationships they have set up and their plans for expansion beyond the UK, and much more. It was a fascinating interview, I hope you enjoy the show.

Welcome to the podcast, Freddy!

Freddy Kelly: Thank you very much for having me.

Peter: My pleasure. So, I’d like to get this thing started by giving the listeners a little bit of background. You know, you’ve had an interesting career with…..you know, you’ve been doing Credit Kudos for a few years now, but tell us what you did before you started Credit Kudos.

Freddy: So, I’m a software engineer by training, I guess, so I studied computer science in the UK. I moved to the West Coast of America after graduating and went to work in sort of fast growing tech startups, so I started working for a business called BitNami back in 2013 when they were going through, or just finishing up with Y Combinator and that company was building sort of deployment systems for big cloud platforms like Amazon and Google so I spent a couple of years doing that.

I then landed in my first …what I now know as fintech gig at a business called TXN which was an analytics startup that was looking at the problem of understanding consumer spending behavior through the lens of their bank transactions and trying to re-purpose the information, if you like, for big retailers so they could understand what their customers were likely to do next. So, that was where I was ready to cut my teeth with fintech and this transactional data.

I then got the opportunity to join a program called Entrepreneur First in London which is a pre- seed startup accelerator, so I sort of jumped on a plane and came back to London. And it was about that time that I suddenly realized that ….rather painfully, I guess, that I didn’t have a complete credit history and it was through having to provide bank statements in printed form to my landlord to prove that I was able to pay the rent that I sort of hit on this problem and having had this experience using transaction data in a much more automated way. The idea for Credit Kudos was really born in bringing those things together and trying to make a much more effective, accurate credit reference agency which is what our business is all about.

Peter: Okay. So, then let’s just dig into that. Maybe when you got started, you said a few years back now but, what was the original mission and has that mission remained the same today?

Freddy: Yes, so we want to make credit more fair and accessible for everyone, but, particularly, with a focus on those that are excluded from financial services. So there are about 5.8 million people in the UK that are excluded from mainstream credit and depending on what your definition is, that that number changes.

But, essentially, the way we look at it as a lot of people currently get rejected for loans, or credit cards, or whatever else because their credit history doesn’t portray them in a fair and accurate way, and, indeed, they may not get rejected, but they end up paying a much higher price and higher interest rate. And if we can build a product that perfectly predicts risk, that was the dream, then those people shouldn’t end up paying. So, what we’re trying to do is get as close to possible to perfect by using this new source of data to serve every customer, particularly those that are financially excluded, so that’s pretty our mission.

Peter: Okay. So, then what is the kind of data that you’re actually……can you just maybe dig into that a little bit about what data you use, specifically, that is different from the main credit bureaus.

Freddy: Yeah. So, a traditional credit bureau is anyone, which one you look at, will use effectively your past boring performance as the primary indicator of your credit risk. So, if you never borrowed money before then you don’t really have a credit score. It’s not like you sort of have it to lose, you have to gain it, in the first place, by taking out credit cards, things like that, so if you never used a credit card, or a loan then you maybe don’t have a credit history.

There are also different examples where people having impaired, or non-existing credit cards where they really should be able to access credit. What we do instead of looking at that are source of input, we look at  open banking data, or PSD2 data, so what that is, basically, a new mechanism that gives consumers the ability to securely share data with a third party such as ourselves through an API, so a programmatic interface. We get, effectively, the bank transaction data for over an extended period of time, so typically two years, and we use that data to score the consumer and provide a decision and that score is derived from historic information we’ve seen from other customers.

So, our model looks at, essentially, the bank transaction data, or open banking data of customers and then how these customers went on to behave. So, did they repay back their loan, or now, looks at those data points at an enormous scale and then using that inference that we’ve built up, we can then predict for a new customer that’s sharing their data with us whether they’re going to repay, and it means that we’re scoring them based on their real financial behavior, and not just necessarily their past credit performances in a much more forward looking prediction.

Peter: Right, got it. So then, did you start the company…..I mean, obviously, open banking has been in place now in the UK for a couple of years, did you start the company knowing that open banking was coming and you thought this would be a good way to kind of have these data available to you?

Freddy: Yes, we did. So, open banking is a very seed of an idea that was actually in existence before what we now term to be open banking limited in the regulatory framework. So, that was a project by the Open Data Institute, the ODI, that was partly sponsored by Barclays, I believe, that was looking at this idea that data could be open and what about banking data being part of that.

At the same time, in fact, a little bit before the European regulator was introducing Payment Services Directive 2 or PSD2 which was a sort of much more high level overview directive that looked over some parts of the ecosystem, but within that was this scope that banks should be able to provide access to these data through some interface.

And so, it was on the horizon, but it wasn’t until the CMA ordered that, the Competition Markets Authority here in the UK, we already put the accelerator in the development of these API’s and that, as you say, in the last couple of years has seen those come to fruition.

Peter: Right, right, okay. So then, I’ll just step back a second and tell us how it works, I mean, do you operate in the same way that a normal, you know, credit bureau, credit reporting agency would operate and maybe you could explain the business model and how it works for both the business that you’re serving and then the individuals.

Freddy: Definitely. So, we’re regulated in the same way as the traditional credit reference agency, but the model we’ve adapted is very different. So, what would happen ordinarily is you would apply maybe on the website of a lender and they would take your identifying attributes, so your name, address, date of birth and they would search in the background for a matching record with one of the three major credit reference agencies, or at least three major credit reference agencies in the UK that would return whatever information that they have on you which might be nothing.

With our system, the customer is presented a consent window in the application journey so a little bit like buying something with PayPal, or using a Stripe type check. We embed our process into the loan application journey and the consumer is given the option to use open banking to fulfill that application.

The way that works is, say you bank with Barclays Bank, you would say, okay, I click on Barclays and then we take you through a normal Barclays online banking log-in process, or if you’re on your mobile, it’s the mobile app authentication and past that process will involve using how much data you want to share, over what period of time, and consenting to that. It’s very similar to the process that you might run through with Google, or Facebook when you’re sharing your data on a third party service.

Once we’ve got that data, we process that and build our report which is then provided to the lender through API, or basically through a programmatic connection. The consumer can then, if they so desire, they can sever that linking preventing some access in that data and they can do that from our interface, or they can do that from their own bank, so it provides a much higher level of transparency for the consumer in a process that they’re normally, in some cases, completely oblivious to, or at best they don’t really understand how it works.

Peter: Okay. So then, I’m curious about how you interface with the existing credit reference agencies because, I imagine, when a person is going through a loan application this lender is also going to be pinging the traditional agencies. Is it sort of a …….does it work where if there’s no record there, they go to Credit Kudos and go through a system there, how does it interface? I imagine it varies from lender to lender. What’s the typical way that it works?

Freddy: It does vary. Most lenders will use more than one credit reference agency anyway, irrespective of whether they use our product and they will combine the different sources to come to their ultimate decision. And so, we’re no different in that sense. We’ll provide an input to their decision and then they have their basic score card which is this set of business rules that ultimately comes up to the yes, or no decision. We are better at serving customers that are typically missed by credit bureaus, so you’re right there, sort of a waterfall there, you know, bureau number one, or bureau number two doesn’t give a response, we can provide an answer where they’re unable to.

But also, for an increasing number of customers that they use us to fulfill their regulatory obligations to understand the affordability of the credit. So, because we’re looking at live income and expenditure data for that individual, we can much better understand their ability to repay which is, of course, crucial in the way that the overall decision is made and it’s something that our regulators at the FCA has put a lot of onus on firms to better understand.

Historically, they were able to sort of use statistical data to try and understand it…..sort of a high level, whether someone could afford something, we’re now seeing the regulator push more and more for those lenders to use actual validated data, short of getting the customer to kind of send in pay slips and bank statements because, obviously, a huge source of friction and pain for both parties. We can automate that and provide that solution for the lender, so it tends to be across the board with all types of customers for various different use cases and different profiles.

Peter: Right, right, okay. So, I know you’ve been coming to our LendIt events for a while now, so are you mainly focused on the alternative lending space? Do you have traditional lenders you’re working with? Maybe give us a sense of the cross section of lenders that you work with today.

Freddy: Absolutely. So, we work with all types, I mean, as I kind of alluded before, if you can build something that really works for the most hard to reach customer profiles, you genuinely end up with a better outcome for everyone and we’ve certainly found that. So, we work with anything from small to medium-sized credit unions and alternative community finance providers right through to major credit card vendors, loans providers, motor finance and then up to more prime products and mortgages, secured loans and tier one banks and mainstream banks, and then a little bit as well in product advice and debt advice and things like that.

So, the whole spectrum of the market which is hugely encouraging for us because, you know, we’re effectively the fourth credit bureau in the UK and we’re really providing different products than what’s been on the market, historically.

Peter: Sure. So, I’m curious about the consumers, I mean, because there’s been a lot of talk about open banking in the UK and hasn’t had the level of traction, I think, that people were hoping. Do you find consumers that are being presented with your consent journey…..is there much reluctance there, do you find people are now much more open to open banking, shall we say.

Freddy: Definitely. There’s this kind of …..I think it’s a little bit of misinformation because I think from a perspective of a lot of the sort of skeptics in the industry, they were expecting this moment where everyone kind of talks about open banking and it’s really cool and everyone can’t wait to use it. I don’t think that moment is going to happen in the same way that no one talks about faster payment, or batch payments, you know, there are a number of users, but it’s not what people are talking about, their services, what’s interesting, the functionality of it, the how.

We’ve seen for the consumer groups that we’re looking at really good conversion rates, so anything from sort of 80/90%, the top end of people consenting to share date. We, actually, recently conducted some reschedule on our partners quality credit services, we looked at consumers’ attitude to adapting open banking in the context of receiving credit and we found that in the 18 to 35 sector, around 75% of those that were asked were willing to share data.

It really comes down to value exchange that you give them, so if you can save the customer using this process, we’ll be able to respond to your application in seconds rather than days, or we’ll be able to give you a higher likelihood of being accepted, or a lower interest rate than those types of value exchange. Consumers are really willing to share data and ultimately, this is data that in many sense is already being shared and this is just giving them control over that sharing which is, I think, empowering.

Peter: Yeah, that makes sense, that makes sense. So then, I’m curious about the….like how are you taking these data and maybe you could tell us, are you using Artificial Intelligence, machine learning in this, I mean, how you’re approaching, how you’re sort of building your models that provide a score back to the lenders. What’s your approach to the data analytics there?

Freddy: Yeah. So, I’m always wary talking about AI and machine learning because they come…. kind of buzz words that are often misplaced. I think the thing that’s really interesting with any type of lending decision, regardless of what data is going into it, is it’s a problem of patent recognition, right. What do I know from the customers I have seen before that typically meant that they went on to repay, or not repay.

And so, there’s some really simple things like knowing that, you know, if a customer defaulted a lot of times before, it’s probably likely that they might do that again and that is a really simple sort of knockout rule, but there are more nuanced complex behaviors that can be detected in much larger data sets that are also highly correlated to outcome. And the way that we get to those behaviors and understand them is using supervised machine learning, machine learning meaning pattern recognition and supervised meaning that we know what the outcome is that we want to get to, so the outcome for us is should we lend, yes or no.

The way we train those models is based on historical data so that bit I alluded to before is having a database that essentially says, this was Customer A’s bank transaction data before the point that they applied for the loan and then this is how they went on to repay that loan. Did they repay on time, did they repay the full amount, so on and so forth and by replicating those sort of layers of data at scale, you can build a model that basically given any new input like bank transaction data can sort of compare that to the universe of other customers that have previously been through that process and then use that to predict whether that customer falls into the yes, or no in terms of whether they’re going to repay.

So that in kind of really simple terms is how we look at it and I guess we’re doing it on thousands of dimensions because we have thousands of transactions for each customer. But, if we were to do it on….if we take a really simple example on two dimensions, we might have, and this is one from my university days so apologies if it sounds kind of silly, but you might have, you know, and X and Y axis with height and weight of a group of people and your problem might be to understand whether they play rugby, or a ballet dancer and you’re typically going to end up with loads of process in the top left and bottom right and then your machine learning process is basically just drawing a circle around each of those and knowing that when you get a new observation which group it fits into.

So, if you can imagine that taking place on a thousand dimensions, or more, that’s essentially the process that we’re applying and it’s by having good training data that you get good accuracy.

Peter: Right, because, I imagine, the data is being……you’re learning about all the transactions, right, so you’re sort of, you know, taking in the bank account data and being able to sort of….I imagine, you’ve got…every single person’s going to have dozens, or possibly hundreds of data points there by looking at that data. So, I imagine….I mean, once you get the model going well, you can get a very accurate model because each person has such rich data when you’re looking at their transaction data, right?

Freddy: Absolutely, absolutely. I mean, it’s a much stronger signal. You know, what we are comparing to is quite a blunt instrument when we think about traditional credit data because it’s either there, or it’s not and you don’t even know if someone’s bad, you don’t know if they’re good. If you see what I mean, we only really record the customers that don’t repay, or they do repay, but we don’t record, you know, setting money aside and saving, we don’t record their financial health, we don’t record their financial trajectory and money management capability over each month is good, or their ability to withstand some kind of financial shock is there.

All of those things are captured by the data that we’re looking at and even more so and what we’re able to do is predict things like the liquidity of a bank account so the likelihood of that account is going to stay in a positive balance over a certain period of time and that would capture up to the second the behavior of that customer.

Whereas, someone who’s maybe just had a sudden run of negative transactions would normally not have that data reflected on a traditional credit file, we can immediately see that and show that and capture that behavior and similarly on the output angle, we can show that a customer who maybe has poor perceived traditional credit history is actually on an upward trajectory, they’re making sensible financial decisions that are pushing them up.

We can also demonstrate that ability, so for some of our clients we’ve been able to find this kind of false negatives, so people they were rejecting, they could have been lending to and allow them to lend to those. For them, it’s a massive financial gain in terms of their revenue and their ability to lend more accurately.

Peter: Sure, that makes sense. So, which brings me to another point that I just thought of. In the US, cash flow data, bank account data is really becoming a must have for all the lenders. I mean, some of the traditional credit bureaus are creating products that will allow this kind of thing. I’m curious about….in the UK, because, I mean, your focus is on the underserved, but to me it sort of begs the question, why is it just that, why isn’t it for the already well served because these information, bank account data, as you just pointed out, is richer than just getting the borrower’s previous credit data that is there. You say in your website, better credit for all, but then you kind of talk about the underserved, isn’t this really a bigger play than that?

Freddy: Absolutely, yeah. I apologize if I didn’t make that clear. I think for us, solving the problem for the underserved allows us to be better across the board. So, when I think about it, and I sort of imagine a spectrum of risk from super prime to sub-prime, there are many, you’ll find it there…pockets of risk across that spectrum that are currently mis-served and that can be right at the top as it can be at the bottom and using our data to enrich those decisions is valuable at all these points.

You know, we do a lot of work in mortgage lending, for example, where understanding the financial ability and affordability of the customers is a huge pain point and it’s definitely not an underserved segment, but it’s still an area which there’s massive improvement to be made on the risk capability of those decisions. So, definitely, we’re providing for all, sort of focused view on the underserved is that if we can get it right, what we would argue is that hardest segment to reach then we can get it right for everyone.

Peter: Right, yeah, that makes sense. Okay so, last year at LendIt Fintech Europe, you introduced a new credit score, or new liquidity score, why don’t you explain a little bit about that and how that’s different.

Freddy: Absolutely. So, liquidity is effectively the availability of funds in an account, or a number of accounts, for a consumer in any period of time and it’s one of the hardest things to understand from traditional credit reference agency data. So, the moment some of the banks share sort of high level balance data with each of the credit bureaus and that data effectively shows that kind of monthly closing balance, but it doesn’t show how that balance was reached over the month and how that data sort of trends, and so the most simple approximation….you can understand if you were to sort of look at your own P&L over a month, you’d see a line going from payday down to the end of the month, hopefully, and there’d be some kind of trajectory there.

What we’re doing with the liquidity score over a month, 60 days/two months, three months is to basically predict where that direction of travel is going so that we can understand in a future looking sense where the customer’s likely to be and whether they’re likely to stay positive, and that’s, obviously, hugely important to understand whether a customer can afford credit. So, rather than just taking a static snapshot of the customer and saying, well, they’ve got money in their account, so they afford this loan, we’re actually going to look at that and see.

Well, maybe they’ve got money in their account, but we know that there’s a recurring bill that’s about to go out, so that won’t be there for much longer and if you’re going to lend them X amount, their repayment’s going to be Y, therefore, they’re going to need this much. So, can they afford that and is it going to fit, so it’s a much, much sort of higher fidelity measure of their affordability and it’s a predictive forward looking measure.

Peter: Okay, that sounds cool, that’s excellent. So, another thing I saw and this was announced a couple of months ago, late last year, you had a … I saw this thing about ClearScore which provides, you know, free credit scores, I think, to….maybe you can tell me exactly what they do, but they’ve partnered with you guys to serve underserved borrowers. So, tell us a little bit about that relationship.

Freddy: Yes. In the UK, a vast amount of credit is intermediated in some ways so people will typically go on price comparison services, or they’ll search through different products to find the right solutions for them. ClearScore is one of the largest businesses in that space, they have 8 million customers in the UK, I believe, and they provide free access to the consumers’ credit score and so they’re in some snap fact credit score.

One of the challenges they have is, obviously, for a lot of the customer profiles we just talked about, they can’t necessarily match the consumers to the right products, or even be able to find the right products for the individual and also when they’re sort of providing recommendations and coaching and allowing to understand how to improve their financial situation. A lot of these recommendations, if they’re based on traditional credit data, aren’t as bespoke, or prescribed as perhaps they could be.

And so, our collaboration with ClearScore is aiming to tackle those challenges, so first and foremost, can we embed this new source of data into the decision making process for the lenders that work with ClearScore so ClearScore is the broker, can we help them use that information to make more prescribed offers to their respective customers, and can we also use that information to help those customers coach, or understand their financial situation and move towards better financial resilience and better financial outcomes. So, it’s hugely exciting because it’s one of the largest sort of credit marketplaces in the UK.

Peter: Right, right, okay. So then, I’m wondering about…..you know, when you’re going out and trying to sell this to lenders, I imagine, you’re obviously a lot smaller than the major credit bureaus, you’re still a startup, I guess, but is the biggest sort of thing that you’re trying to overcome is the fact that it’s a lot of work. I just want to just get a sense….to me, as you’re talking, I mean, this is a no brainer, I feel like for every lender to have, but, I take it, you don’t have every lender in the UK yet, so what is sort of the friction there that stops people form jumping on board?

Freddy: We’re certainly working on it. I think the challenge there is quite interesting because, you’re right, it is a no brainer for most, and we certainly get that response with almost every lender we speak to. The challenges are in terms of how you embed what is a completely new data source to many, how you get that data embedded into the existing decision process in a way that meets their regulatory obligations, meets their compliance obligations in the way that they’re comfortable with and, obviously, with open banking data because it’s hinged on consumer consent.

There’s no sort of big historic data set that lenders have access to that they can kind of run some retrospective analysis on in the way that they would with traditional credit data before implementing some policy changes. And so, we have to invent a new process to actually integrate these data into a lending decision and get those benefits we’ve been talking to and that’s really where our business comes in and how we help the companies we work with.

So, we already have huge amount of expertise and huge amount of data that allows us to provide this modeling service, allows these scoring services sort of out of the box, but then we also have a huge amount of expertise in how integrate these into existing models, how we provide that into a lending decision.

We also have lending management software that allows people to interpret these data into cases and things like that. So, the challenge is kind of how you…..and I guess this isn’t unique to us, this is kind of universal with financial services, how you sort of get through the inertia barrier and get these stuff integrated and start to kind of……as many venture capitalists say, sort of take a wedge and then expand it. That’s really the approach we’ve taken, we’ve seen many of our customers in terms of where they were a year ago over a thousand percent growth of customers they’re putting through open banking as a source of risk.

Peter: Right, right, got it, okay. So then, did you find…..I imagine it’s easier, like the fintech lenders are a bit more nimble, the peer-to-peer lenders, the online lenders, they’re a bit more nimble, I imagine, than traditional banks. Are they an easier sell than, I imagine, going to a traditional bank?

Freddy: Yeah, I think that’s fair to say. I mean, some of our earliest clients went alternative finance and peer-to-peer lending, but, increasingly over time, that has changed. I think one of the really interesting things to see is because open banking has kind of eroded some of the walled garden of transaction data that banks have been sitting on for all this time, they’ve all realized that, you know, sitting still is not a strategy and they’re trying to understand how they could…you know, if HSBC is having to give these data away, how does it also benefit from the stature and input when one of their competitor’s customers are switching to them and that means we’re now seeing a tier of banks really move quickly and trying to build out new lending products using this information. We’re working with five, or six of the tier one banks in the UK to do that.

Peter: Right, right, okay. We’re almost out of time, but a couple of more questions before I go. Do you have any plans to expand beyond the UK. I know, with Brexit now, it’s going to be more challenging, I guess, to go over to Continental Europe, what are your expansion plans for international?

Freddy: Yeah, we’re definitely doing so. So, we recently launched in the Republic of Ireland, we’re working in about five, or six geographies across Europe at the moment on implementation. The challenge there is although PSD2, which is that piece of regulation I mentioned before, is in effect, we’re sort of seeing this kind of slow adoption process that arguably we saw kind of a year and a half ago kick off in the UK, and so there is a sort of various degrees of readiness across Europe, but our ultimate goal is to build that level of inter-operability because what’s really nice about these data set is it’s universal.

So, you know, a customer from France can come to the UK and apply with the same data, will get the same result and there’s a proper sort of “passport-ability” around the data. And so, that’s definitely something we’re eyeing in the next year, year and a half, or so as APIs become market ready.

Peter: Sure. So, what’s on tap for this year, what’s next for Credit Kudos?

Freddy: So, we’re expanding pretty rapidly and so a lot of that is going into engineering. So, we’re really seeing a range of new features that allow more decision making to be done in-house using this data set. We were recently awarded a number of prize grants from the Treasury in the UK and the grant body, Nesta, and they were looking at how new data, or new technology can be used to deliver better decision making for alternative finance providers. Also, through that work, we’ve built a whole range of new different scoring capabilities and new product features that we’re rolling out and we’re now scaling those up for newer and bigger lending applications.

We’re also looking at the intermediary market, so that piece I mentioned in relation to ClearScore earlier, I think, is a massive area of grace in the next year for open banking. And our consumers, generally, like to shop around and if they can use their data on a neural platform to be able to shop around across the maximum amounts of lenders in the market then they’re going to get best possible outcomes and I think that’s a pretty nice opportunity to flip the model and allow lenders to bid for the consumers’ business based on the ability to assess that consumer’s holistic data and open banking being a big part of that. So, we’re building a lot of pipe work that’s going to enable that in the next six months. So, lots of big things plus the expansion, plus new geographies, so we’re very busy.

Peter: Interesting. Well, you’re certainly doing some great work there, Freddy, I wish you the best of luck and thanks for coming on the show.

Freddy: Thank you for having me.

Peter: Okay, see you.

You know, sometimes I’m a little bit envious of the UK for their open banking initiatives they have where banks have been made to connect, to make available their data through APIs, through third parties. We certainly don’t have that here, but we are sieving some of these angles, anyway, here in the US. I think the power of cash flow underwriting, the power of banking data, I think, has been proven here and I would say, it’s now the norm and I think it’s going to become that way in the UK as well because it just simply is a much more accurate way, as Freddy said, to underwrite borrowers.

Anyway, on that note, I will sign off. I very much appreciate you listening and I’ll catch you next time. Bye.

Today’s episode was sponsored by LendIt Fintech USA, the world’s largest fintech event dedicated to lending and digital banking. It’s happening on May 13th and 14th, 2020, at the Javits Center in New York City. Lending and banking are converging and LendIt Fintech immerses you in the most important trends of the day. Meet the people who matter, learn from the experts and get business done. LendIt Fintech, lending and banking connected. Go to lendit.com/usa to register.

You can subscribe to the Lend Academy Podcast via iTunes or Stitcher. To listen to this podcast episode there is an audio player directly below or you can download the MP3 file here.

https://traffic.libsyn.com/lendacademy/Podcast-239.mp3

Podcast: Play in new window | Download | Embed

Subscribe: Apple Podcasts | Android | RSS

Filed Under: Lending and Fintech Podcast Tagged With: cash flow underwriting, Credit Bureau, Credit Kudos, Open Banking

Views: 275

Podcast 238: Adam Jiwan of Spring Labs

The founder and CEO of Spring Labs describes a completely new system for credit and identity data, one that is real time, secure and decentralized

March 13, 2020 By Peter Renton Leave a Comment

Views: 545

The way we have stored and used credit information has not fundamentally changed in decades. The big three credit bureaus each have massive databases of personal information on everyone with a credit file. But as we have seen in recent years this is not the best and certainly not the most secure system.

Our next guest on the Lend Academy Podcast is Adam Jiwan, the CEO and founder of Spring Labs. Spring Labs is working on a completely new system for credit and identity information. This is a system that is decentralized, real time, blockchain-based and secure. They already have dozens of lenders signed up and they are looking to go into production later this year.

In this podcast you will learn:

  • The major issues with consumer financial data today.
  • How Spring Labs is trying to enable sharing of financial information directly.
  • An example of how this will work in practice.
  • How the Spring Network ensures privacy and security of financial data.
  • Some of the leading lenders who have signed on already.
  • Why they refer to themselves as an Un-Bureau.
  • How they interface with the big three credit bureaus.
  • The blockchain technology that underlies the Spring Network.
  • The different verticals they are focused on today.
  • The new products they are looking to deliver this year.
  • Where they are at on the road to implementation.
  • When they expect the trading of data to begin with their partners.
  • The number of employees they have today.
  • How Spring Labs’ business model will work.
  • How they were able to get big names like Gary Cohn on their advisory board.
  • Adam’s perspective on the future of personal credit data.

This episode of the Lend Academy Podcast is sponsored by LendIt Fintech USA 2020, the world’s largest fintech event dedicated to lending and digital banking.

Download a PDF of the transcription of Podcast 238 – Adam Jiwan.

Click to Read Podcast Transcription (Full Text Version) Below

PODCAST TRANSCRIPTION SESSION NO. 238–ADAM JIWAN

Welcome to the Lend Academy Podcast, Episode No. 238, this is your host, Peter Renton, Founder of Lend Academy and Co-Founder of the LendIt Fintech Conference.

(music)

Today’s episode is sponsored by LendIt Fintech USA, the world’s largest fintech event dedicated to lending and digital banking. It’s happening on May 13th and 14th, 2020, at the Javits Center in New York City. Lending and banking are converging and LendIt Fintech immerses you in the most important trends of the day. Meet the people who matter, learn from the experts and get business done. LendIt Fintech, z and banking connected. Go to lendit.com/usa to register.

Peter Renton: Today on the show, I am delighted to welcome Adam Jiwan, he is the Founder/CEO and Chairman of Spring Labs. Now, Spring Labs is a relatively new company that has only been around for a couple of years and they have big audacious goals. I wanted to get Adam on the show to talk about these, they’re really looking at re-tooling how personal data gets shared, how it gets stored and how companies verify information on consumers and small businesses.

We go into that in some depth, we talk about how it works, go through an example, it’s somewhat complex, but Adam’s been able to explain it in pretty simple terms so anyone can understand it. We talk about where they are in the process of getting out to production, talk about the different partners, some of them they can share publicly, and we also talk about what his vision for the future of credit data is. It was a fascinating interview, I hope you enjoy the show.

Welcome to the podcast, Adam!

Adam Jiwan: Thank you for having me.

Peter: My pleasure. So, I wanted to get started by just giving the listeners some background. You’ve had an interesting career with a variety of different companies, it looks like, so why don’t you tell us what you did, give us some of the highlights before Spring Labs.

Adam: Sure. So, for nearly 20 years, I’ve had the opportunity to develop my career at the intersection of business building and investing in financial services. Through the course of these experiences, I got to assist in the development of the real estate finance industry in Brazil, help introduce student finance in Europe as a Co-Founder and Chairman of a company called Future Finance, backed a myriad of online and innovative financial technology companies in the US, including being one of the largest seed investors of Avant, now Amount.

In all of these different experiences at either, I came to appreciate that in the digital era, data is a lifeblood of any financial institution, but I think that’s probably pretty well understood that, you know, data is the new oil, as it were. But slashing beneath the surface, really came to develop an understanding of the underlying plumbing, i.e. where is these data relating to credit identity come from, who owns it, who has the right to it, where did they get it from, who shares it, who doesn’t share it, why and why not. And, in that plumbing, my partners and I saw significant amounts of fragility and frankly, a number of things that we found to be quite broken.

Peter: Okay. So then, you found things were broken and then what were the steps involved in really founding Spring Labs? How did the idea kind of come about exactly?

Adam: Sure. So, just at the highest level, let me just share with you what Spring Labs is all about.

Peter: Okay.

Adam: We’re trying to reinvent how information is gathered, shared and monetized in the financial services industry by deploying decentralized infrastructure, and we hope that will drive much greater accuracy, much greater security, much greater consumer privacy. In doing so, frankly, we think we can actually make a dent on things like, you know, financial inclusion, and we came to this, to answer your question, because we were lenders ourselves and we were ingesting vast amounts of data to do things like identity verification, as well as credit worthiness.

And, where we were gathering these data from, we saw a system that had a number of issues and let me just run you through what these issues were. Again, we saw security being a major issue, we saw that accuracy was a major issue because the participants in this ecosystem are, generally, a pretty narrow set of retail lenders. So, a lot of the information that you might actually see on a traditional credit report, they don’t actually include things like your assets, or your income, or alternate forms of credit performance data. If you are renting, do you pay your rent on time, do you pay your utilities, or your insurance, or your subscriptions on time, so accuracy was an issue.

We saw a system that was very vulnerable to fraud, especially from the more pernicious forms of fraud like synthetic identity fraud, meaning fraudsters could not only just take data out of these databases and let’s say credit bureaus and others, but they can actually stock synthetic files into those same places which can create significant vulnerabilities for lenders such as ourselves. And then, we also saw a real misalignment of incentives, as you likely know the most valuable businesses in the world today are the business of hoarding private data about each and everyone of us and monetizing it, and it’s no different within the credit and identity world.

Centralized data aggregators are in the hoarding data business, and so when they want to sell it, for instance, to a financial institution let’s say for identity verification, they don’t provide all of the underlying data itself, they don’t provide the provenance of that data, they don’t provide linkages of where that data was seen with other pieces of data. They, basically, give you a score on the probability of thumbs up, thumbs down because they don’t want to lose on to their precious oil. So, we saw misalignment of intentions because we, as a financial institutions, or lenders who are in that business, want the most granular information possible to inform our models to actually make the best risk decisions we could for our companies.

And then finally, the way the system works today, there’s very little respect for consumer privacy. There’s a vast amount of sensitive personally identifiable information that moves around anytime anyone applies for any product because so many different verifications need to take place on a point-by-point basis. So, we saw a system that was not only fragile, but had a number of significant issues and we wondered to ourselves whether there was a technology, frankly, that could be brought to bear to deliver the elements of an ideal solution.

Peter: Right, that makes sense, and not to even mention the hacks that have happened where the exposure of all this data has gotten into nefarious hands. So, that’s another……

Adam: Absolutely, and that’s what I meant by security which was my first point was exactly that, which is, if you’re in the hoarding business, you are taking vast amounts of sensitive information, putting it into a database, that database grows ever larger and represents an incredibly juicy attack factor. The truth is, the moat around that database, no matter how wide, can be breached because once you break into the filing cabinet, you’ve got everything, right. So, we believe that there needs to be a fundamental re-thinking on security architecture, again, in order to create a much sort of safer ecosystem for both consumers, frankly, and lenders.

Peter: Okay. So, let’s dig into that, let’s dig into exactly what you’re trying to do here. Maybe you can try and explain in as simple terms as possible what Spring Labs is about.

Adam: Absolutely. So, we’re trying to transform how information is exchanged within the financial services industry with security and consumer privacy as paramount considerations. So, we are developing a network that is an information exchange, and so, what we are trying to enable is financial institutions and others that have credit-relevant, or identity-relevant information to share information with one another directly, i.e. not mediated by centralized data aggregators, or credit bureaus, or the like, i.e. just share with one another and when they do the exchange of information, they receive value.

In the current system, there is what we call a “Give to Get” model which is retail lenders today give away their hard earned credit and identity data for free to many of these centralized aggregators, like the credit bureaus, because they can’t share information directly with their competitors. So, they share it with this little man who aggregates the data together and resells it right back to those same lenders that gave it to them.

And so, it was a wonky system when we were in the lending business, we really disliked it because we lost ownership and control of our credit and identity data, and so with the Spring Labs sort of network we were trying to foster the enabling conditions, or direct sharing among institutions which means that there needs to be a flow of incentives.

So, if an institution is sharing information, they’re not giving it away for free, they’re recruiting value, number one. Number two, they’re dealing with very high security and secure privacy assurances. So, for instance, Personally Identifiable Information does not leave the firewall of participating institutions in our network, and from a security perspective, again, plain tech data is not sort of shared within sort of like the network and that doesn’t mean that it’s restricted, it ends to be hashed and faulted and we use the series of anonymization technology, again, to address the competitive sensitivities that have prevented this work sharing in the past. To go to your question about an example, though, I think that might be very useful.

Peter: Yeah, for sure.

Adam: So let’s say, Peter, you were applying for a product prior to sort of the Spring Network, you’re applying for a new credit at J.P. Morgan. The first thing that J.P.Morgan needs to do is verify you’re the person you’re purporting to be and they typically do that by not just doing it manually, but going to a number of vendors. So, that could be a new store, it could be a credit bureau, and, again, let’s use the simplest example where they’re just trying to verify one identity field, your phone number.

So, they typically go to a party and ask, is Peter’s phone number X, and that party because they don’t want to give the financial institution underlying data itself, typically, will say, thumbs up, thumbs down with a probability score and this could be true across a number of identities, or other factors as well. And so, the challenges with that system are several, the first is, you’re relying upon one party. That party can be compromised, meaning, someone can change literally what’s in the database that could be overwritten, i.e. think about synthetic identity fraud.

The second is…the most pernicious forms of fraud that exist today often take place by stitching together real identity factors that are for real people with a fraudulent bank account. So, if you’re just doing point-to-point on a single identity factor, you’re missing granular information, linkages among information, i.e. where was that phone number seen, with what address, with what IP address and with what bank account, you’re not getting that information and you’re not getting the provenance of that information, i.e. how, when, how did that data aggregator get that information. So, that’s the old system.

Under the Spring Network, we employ an entirely different concept which is rather than going to a data hoarder, you ping the network and let’s say 30 different institutions, many of whom might be regulated through permission, who had an experience with Peter and Peter’s phone number. And, without actually revealing your phone number because it’s hashed with an entity factor and without actually using any of your PII, those certified institutions that may have had an experience with you, within say the past six months, can come back and say, yes, what we have matches what you have, number one.

Number two, we’ve actually seen it with the following and other identity factors with the following problems, so let’s compare scenario one with scenario two. In scenario one, you’re relying upon one party for what’s true and that party can be compromised. In addition to that, you are not obtaining the underlying granular information, linkages with other identity factors, or the provenance to that information.

In the second scenario, you’re getting the benefit of multiple parties attesting to the veracity of that piece of information, you’re obtaining the granular information, the provenance of the information and the linkages of the information driving what we believe is much greater accuracy.

Similarly, in that verification case, Peter’s, PII, for instance, is never leaving and never crossing on the network. As such, it’s actually system is fundamentally more secure and it’s one that actually we should respect, Peter’s consumer privacy, which we think you have a right to.

Peter: Right.

Adam: So, that’s a very simplistic illustration of the world before the Spring Network and the world after it as we envision it.

Peter: Right. Just so I’m clear, you’re not taking any data, you’re really enabling connections between the pieces of the network, so the data, there’s no central depository because each of the 30 parties, you said, have their own….they’re pinging their own database internally. So, you’ve obviously written code that enables them to do that and so there’s no PII going back and forth.

So, basically, this seems like a far better system because…I mean, the biggest thing is, the way I look at it, there’s no central repository and everything, I imagine….all these databases are also being updated in real-time. So, your credit report, you know, it still doesn’t get updated in real-time whereas….maybe you can comment on that, is that the case?

Adam: You’re absolutely right. About a couple of things. The first is we are not a centralized repository of data because that would create another attack vector, right. Rather, we are the pipe, or the plumbing, or the infrastructure that connects all of these parties together to enable them to share information and value with one another.

So, if you think about our business model, it’s something that came to a Federal Express, right. Federal Express mediates the exchange of packages and value, but Fedex doesn’t open those packages, they don’t retain those packages and they don’t monetize those packages, right. So, effectively, the Spring Network is a set of infrastructures, or pipes among institutions that give them security and consumer privacy assurances and that enables the flow of monetary incentives.

So, finally, rather than giving away your information on credit identity for free to like Equifax where you’re finally sharing it with your competitors, you’re actually receiving value and, frankly, the anonymization technologies that we introduced are what enabled this as well because, otherwise, you wouldn’t be willing to share with your competitors.

And so, one of your comments also resonated a little bit which is, over the past several years that ……you know, at first when we were noodling on whether we could actually have this technology get adopted because as you know, there are something….you can develop technology, but if it’s never adopted, it’s sort of worthless, right, and we’re dealing with a highly regulated, compliance-minded industry.

You know, we spend a lot of time speaking with chief risk officers, chief credit officers, CEOs, CTOs, financial institution and financial technology companies and every single one of them said that this type of architecture, right, this decentralized infrastructure that employs the concept of multi-party adaptation and yet minimal disclosure, meaning no PII crosses the network, was the type of architecture that makes tremendous sense in the world. Now, the challenge that we have, having a chicken and egg problem, is can we drive sufficient adoption for this to actually become something ubiquitous.

Peter: Sure. So then, maybe just talk on that…..obviously, you have Avant on board because Spring Labs was born out of Avant, but how have you gone with the other consumer lenders?

Adam: Sure. So, last February, we announced partnerships with 15 leading financial technology companies and lenders. I think we named some of those recruited companies like SoFi, Kabbage, OnDeck, of course, Avant, GM Financial, Funding Circle and some others. Since then, we’ve added dozens of other partners, some of those partners include trillion dollar plus asset institutions and very much household names. We will be making an announcement in the coming months about, you know where we are with those partnerships and the rate of growth which we’re very, very pleased with, but, yeah, I think adoption is coming along quite well.

Peter: Okay, it makes sense to me. I mean, this is a 21st century solution to what has been really …..like we’re a little bit 20th century and the credit bureau infrastructure is pretty much unchanged, it feels like at least. I shouldn’t say that completely, but they’ve made enhancements. The core way they do things, still seems to be the same.

Let’s just maybe talk about that for a second because on your Home Page there, right in the middle of the Home page, you say Spring Labs, The Un-Bureau. So, do you view yourself as a replacement for the credit bureaus, or how do you kind of ….what’s your relationship with them?

Adam: Sure. So, you know, I think we try to be realistic about this. The credit bureaus have been around for many, many decades and they have a treasure trove, specifically, of retail credit performance data. We are an early stage company that is two years old, so the notion that we can come in and disrupt an industry that’s been around for a long period of time and that is supported by a highly regulated industry is not necessarily realistic in the first instance, okay.

That said, the reason we refer to ourselves as The Un-Bureau is that we aren’t a centralized repository of data and not therefore an attack factor. We are an information exchange that is fundamentally aligned with the interest of financial institutions because when there are sensitive data that never leave their firewall, it means they finally retained ownership and control of their data. When they share information, they’re not sharing the underlying data, they’re sharing an activation which is something a little bit sort of different and they actually get paid for it.

So, we’re flipping the system on it’s head, so we’re under in the sense that we’re not a corporate data. We believe in facilitating the safe sharing of data in a much broader universe that exists today.

In terms of adaption, in your question about we view ourselves as a replacement to the bureaus and what our relationship is with them, the truth is we are introducing products on our information exchange that’s using, we believe, meaningfully better than what the bureaus could do today and, essentially, to enhance identity verification, or income verification, or fraud prevention, right. We’re a little bit less related to credit position.

Over time, if we are able to drive sufficient adaption it’s very conceivable that we will get into, of course, credit as well because it’s a natural extension once you have the same parties around the table. In terms of where our relationship is, we see a lot of different avenues to collaborate with the bureaus themselves and, of course, there’s some statements to know where it becomes highly disruptive as well, but I think our general approach, like with most market participants, is to be collaborative and not antagonistic and we are actively working with some of the bureaus today on a number of quite innovative things.

Peter: Okay, that’s good to hear. So, we’re over halfway through this interview and we haven’t mentioned the word blockchain yet, and I think that’s interesting to me. I mean, you developed your Spring protocol, it’s a blockchain-based technology, can you just sort of just talk about….the blockchain is integral to what you’re doing, I presume, so maybe just talk about why you decided to use blockchain as opposed to some other kind of way to implement this.

Adam: Sure. So, I’m very aware that we’re a sort of blockchain, sort of nuclear winter from a perception perspective. (Peter laughs)

Peter: Right.

Adam: But, blockchain is not something that we shy away from, so I will explain that there are three sort of core components to our tech deck. There’s blockchain, there’s the series of advance cryptography, and then our client software and I’ll explain each in turn. So, blockchain, actually, in distributed ledger technology can be quite powerful in a lot of different ways at scale. At the scale that we’re considering a permission network, blockchain plays several relevant roles.

The first is permissioning, so adding sort of new notes, adding new participants. Permissioning is a place where a blockchain could be very useful. The second is creating an immutable record of the receipt and exchange of information, so think about an index. Over time, of all of the information that’s been out there on an individual, or out of business because, again, we’re doing things still beyond just consumer. And then, thirdly, it can actually serve as a ledger around value exchange as well.

So, those are the three ways of which blockchain will be utilized. At scale, there are a lot of other things that blockchain can do, but those are the ways that we use it. The second piece of the technology stack is advance cryptography. So, one of the reasons that J.P. Morgan doesn’t share information with Bank of America in market today….one is there are some regulatory prohibitions on sharing PII between institutions for certain purposes, fine, but the other also is this notion of competitive sensitivity that if Peter were applying to J.P. Morgan, J.P. Morgan wants to know if HSBC had a good experience with Peter….J.P. Morgan can’t just directly ask HSBC at the time you’re applying because HSBC will realize you’re applying for any product and try to poach you.

So, our technology uses advance cryptography in secret sharing technologies to address competitive sensitivity. That’s a really important part of our special sauce, frankly. And then the third is client software which is if you went to a bunch of banks and say, hey, we’ve got this nifty information exchange, it’s obfuscates competitive sensitivity, it’s secure, it’s private, but it involves blockchain in really crazy cryptography, financial institutions will do with it.

So, we needed to have client software that we do with data standardization, they would actually do the cryptographic transforms, so, again, no sensitive information will leave the firewall of a participant, or a financial institution. Similarly on the way in, they can take the cryptographic information, transform it and put it into something useful for either their decisioning or fraud models, or however the financial institution might want to use that information.

Peter: Okay, that makes sense. So then, you’ve mentioned……obviously, you’re in consumer lending, you’ve talked about small business, some of the names you mentioned earlier, I read somewhere about a real estate deal that you guys were in, so what verticals are you focused on?

Adam: Sure. So, ultimately, our technology is generalizable and global, so it can be used in a number of industries beyond financial services. So, it can be used for anonymizing the exchange of HIPAA-compliant medical records, or genomic sequencing it can be used for verifications, or authentications between humans and IOTs, you can just think about the types of use case, they can use for generalized private communication. We are not spending any of our time on any of those other verticals, even though we’ve had inbound interest because we will be boiling the ocean.

So, our entire focus is in financial services, number one, and initially, on things that relate to consumers and small business because that’s where we think we understand some of the problem sets and how to deliver real world solutions to lenders and others. And if we can prove and create proof that it creates value and it works, again, we think that there are many different ways to sort of expand the value of this network.

You know, during the course of 2020, we’re going to be launching a number of different products and those products, for instance within consumer, relate to enhance identity verification, income verification and certain fraud prevention tools like fraud registries as well as loan stacking tools.

In small business where there’s even less information sharing that takes place because there’s no real great bureau out there, ultimately, we will be, again, delivering some of these similar types of fraud and identity, business identity related sort of tool this year. The third is the property lead registry that you reference which was an RFP that we won for PACE Lenders where we think that technology can ultimately be used to create registries that over shift. So, if you think about, you know, natural uses for blockchain like obviating the need for title insurance, you know, this type of technology is one that not only could lead to that, but frankly, can also accelerate the adoption of that because it’s not that the technology can’t be used, it’s how do you change behaviors to drive adoption.

Peter: Right, okay, So then, where are you at today, do you….I mean, I presume you have pilots running, as you said, there’s a chicken and egg problem that sounds like you’ve got a few chickens running around now with all of the partners that you got signed up, so are you in production with multiple partners today? Is it still a pilot that people are running, is anyone doing their identity verification through Spring Labs and that’s it, I mean, where are you at?

Adam: Sure, it’s a great question. So, just as a piece of clarification, nothing that we’re doing is in a pilot phase and nothing that we’re doing will be a pilot. Everything is going immediately into production.

Peter: Okay.

Adam: So, we have commenced the technical integration process with some of our partners and, again, imagine for a second we’re putting technology behind firewalls of highly regulated compliance-minded institutions and, therefore, we need to be stuck to compliance, which we are. We needed to have the best in class sort of penetration testing, we needed to have some of the best and brightest minds around security architecture which, you know, I can describe the work of some our people which is pretty extraordinary.

But then we also have this, you know, understand and deal with thousands of questions, info security sort of questionnaires, we need to get on the road map for technology development as well as to, you know, be on the road map from a risk perspective with all of our partners. So, the process of getting integrated, you know, takes some period of time, we’ve commenced that process

We think by the end of the 2nd quarter, we’ll have eight to nine institutions, all household names, starting to trade data and over the course of 2020, we’re targeting something like 30 institutions to be integrated into the network. You know, the number of partnerships, of course, just continue to grow well, well beyond that.

Peter: So, what about in the alternative lending space, I mean, if I go and take out a loan at Avant today, is the Spring Labs technology in the process yet?

Adam: Not yet. So, we expect trading of data to commence towards the end of the 2nd quarter of this year and to continuous wrapping up as we add, you know, additional skills layers because the focus that we’ve been adding more recently are quite sizeable sort of institutions. So, again, we’ll make an announcement, you know, later that I think will become more obvious where the overlaps will exist. But, we view the next…call it 12 to 18 months as the rubber hitting the road on new stage revenue generation and really sort of proving that this new model for information exchanging and, importantly, will add more value for our customers.

Peter: Right, okay, fair enough. So then, maybe give us a sense of the scale you’re at like how many employees do you have, where are your offices, that sort of thing.

Adam: Sure. So, at this point, we’re roughly 55 employees, almost everyone is based in Los Angeles, in Marina del Rey. We’ve raised capital to the tune of just shy of $40 Million for our Series A from last year. And, you know, I think the majority of the team, about two thirds are engineers, or cryptographers and at this point, everyone’s heads down because, you know, we’ve developed this long standing relationship with these partners and that have been actually involved in developing the products.

So, it’s not just sort of partnership in name, they’ve been actively involved in the development process with us with the past year, or so. So now, we’re just literally trying to deploy the technology and flip the switch and continue to iterate our products because there are a whole sort of use cases beyond the range of the market this year that we think are going to add increasing value to lenders, frankly, and others over time.

Peter: So then, what’s the business model exactly? How are you guys going to make money, is it going to be like….is this a SaaS product, is this like a transaction-based revenue generation, how’s it going to work?

Adam: So, great question. When we started the business, you know, we spent a lot of time thinking about business models and, again, because of the experience that I’ve had in the past at looking and investing in many different companies, we wanted to build a very durable, compelling business model. And so, to share…. our revenue model…again, we’re just an information exchange, so let’s say, Avant is sharing information with Prosper, Marlette, Marcus, and Kabbage, in that scenario with information exchange, the sharing party receives value and we receive some portion of that value.

So, we are a toll collector on the network which means we are not charging set up fees, or monthly sort of subscriptions, or anything along those lines, our interest is, fundamentally, aligned with the volume of information that flows through our pipes. And so, we think that aligns our interest with financial institutions who want more information, and we also think it’s a good business model because it’s one that at scale requires very limited capital.

We’re not in the lending business where we have to put equity in each of the loans, and so at scale, it’s a business that also have high margin, it is a business that ought to have very strong operating leverage, it is a business that should not be cyclical, frankly, in any meaningful way, and it should be a business that doesn’t require significant amounts of capital to be raised over time. Again, maybe to reach out at some point down the road, but, again, we shouldn’t be a serial sort of capital raiser for this business. So, we think it’s a good business model, but, of course, there’s a lot of work to do to get it to scale and prove out that this vision of ours is going to work.

Peter: Yeah, that makes sense. Okay, we’re almost out of time, just a couple of more things I want to get to. You have some very high profile advisors with your company. One, Gary Cohn, who was with the Trump administration earlier and obviously a very well respected executive in financial services so maybe…how were you able to get Gary Cohn on board?

Adam: We do have an incredible Advisory Board and in most cases, these are people who have either backed businesses in the past like Nigel Morris, who co-founded Capital One, or with whom I’ve done work before. So Sheila Bair and I sat on a board together for years, Bobby Mehta, who was the CEO of TransUnion for many years, still on the board of TransUnion, he and I sat on the board for a long time. These are people who have looked at what it is we’re trying to do and believe this is the way of the future for exchanging information.

In the case of Gary, when he left the Administration, a couple of us had this thought that, you know, this is a business that we’re developing that requires not only understanding the technology and how it can be deployed in a commercial context, but also how it’s sort of can exist in a broader and evolving regulatory environment. Gary was someone, having been President of Goldman Sachs and then the Chair of the National Economic Council, a person who really couldn’t understand better the relationship between commercial activity and evolving regulation.

And so, as it turns out, we had a mutual friend, one of his former partners from Goldman had backed another business that I started, he introduced us and we had breakfast in New York and when I explained to him what we were developing, he immediately understood two very interesting use cases for the Spring protocol.

One was in effectively crowd sourcing, ultimate forms of credit performance data and other forms of data that don’t find their way into the system like the asset side of your balance sheet from asset, or investment managers, or your income, or employment, and so, if we were able to flow the incentives of our system as well as security and privacy assurances creating a more vibrant ecosystem of data sharing then all of a sudden, you can start tackling major societal level problems like thin file customers, or no file customers who are caught into the vicious cycle of not having credit, therefore people are not having traditional retail credit, therefore not having a retail performance history, therefore can’t get credit, right.

And so, he understood the power and scale of what we were doing to drive financial inclusion, and the other was he understood how technologies could actually be used to identify, you know, real problems through the cycle for regulators. So, I think, it immediately resonated with him and he has been a terrific advisor, as all of our advisors, because they’re uniquely involved, frankly, in ways that in other companies I’ve been involved with, you know, they’re much more passive even at the board level. We’ve been quite blessed to have some great people involved.

Peter: That’s awesome, that’s really, really great. So, last question then, let’s just assume you guys are wildly successful and all of your plans come to fruition and we have this real-time system, where does that leave the credit bureaus and where does that leave the individual and how they……you know, like they’re still not really owning their data. Maybe the question is, what is the future of credit data in your vision?

Adam: Sure. So, I think that’s a great question and I would answer it from two different perspectives. The first is, we would like to see the world moving away from a silo of hoarding mentality to one where you can have safe sharing, with high security and consumer privacy, and, frankly, that applies within financial services and credit, but, frankly, in the broadest sense, and that is a big thing that sort of motivates us everyday.

The second is, and this is something that we intend to add over time, but we chose not to do it because we think we get to scale faster starting with enterprises, we absolutely want to have consumers in the loop. And we want consumers to be in the loop for several reasons.

The first is we wanted to have transparency and much better transparency than they have today with the existing system. The second is we want them to have better user experience, especially around contestability. Again, have you ever tried to contest the bureaus these days is a complete nightmare.

The third is, in many cases, we actually want consumers to have some amount of control and actual ownership over their data. The reason why I say some, rather than all, is I think there’s often this notion that consumers should own everything, the truth is they should own their identity. And so, they should be compensated, in fact, when some of their information ultimately is used. Again, we think our network can actually accomplish that over time. Perhaps that shouldn’t be the case for credit performance data because you shouldn’t be able to delete, you know, the time you actually forgot to pay.

Peter: (laughs) Right.

Adam: It’s note a universal thing, but the general idea is we want to see the world moving away from these data hoarding silos to safe sharing and we want to see a world where consumers are in the loop with privacy and some degree of control in dramatically better user experience and transparency.

Peter: Well, that is a wonderful vision and I hope we are able to get there this decade. (Adam laughs) It would be great for so many of us. Anyway, we’ll have to leave it there, Adam, I really appreciate your coming on the show today.

Adam: My pleasure, Peter, we really appreciate your inviting us.

Peter: Okay, see you.

Adam: Alright, thanks.

Peter: You know, I think even the credit bureaus would acknowledge that the future of credit and personal identifying information….it’s not stored in a centralized database. I think the way we have it set up today is, if not broken, it’s certainly in need for improvement. What Spring Labs has got is, I think, a pretty compelling case for one of the visions that could actually come to fruition when it comes to how all this information is stored and how access to it works. I think we have a long way to go before they get there.

Adam acknowledges that as well, they are not ready for prime time yet, but I think they’re getting there, whether it’s Spring Labs, or somebody else, I really feel like we are going to have a decentralized system. I think it’s going to happen this decade.

Anyway on that note, I will sign off. I very much appreciate you listening and I’ll catch you next time. Bye.

Today’s episode was sponsored by LendIt Fintech USA, the world’s largest fintech event dedicated to lending and digital banking. It’s happening on May 13th and 14th, 2020, at the Javits Center in New York City. Lending and banking are converging and LendIt Fintech immerses you in the most important trends of the day. Meet the people who matter, learn from the experts and get business done. LendIt Fintech, lending and banking connected. Go to lendit.com/usa to register.

You can subscribe to the Lend Academy Podcast via iTunes or Stitcher. To listen to this podcast episode there is an audio player directly below or you can download the MP3 file here.

https://traffic.libsyn.com/lendacademy/Podcast-238.mp3

Podcast: Play in new window | Download | Embed

Subscribe: Apple Podcasts | Android | RSS

Filed Under: Lending and Fintech Podcast Tagged With: Blockchain, Credit Bureau, credit data, identity, Spring Labs

Views: 545

Inside the Groundbreaking Nova Credit-American Express Partnership

Immigrants will be able to use their international credit history when applying for an American Express card

October 29, 2019 By Peter Renton 2 Comments

Views: 603

It all started with a cold LinkedIn message back in 2016. The co-founder of the (then) tiny startup Nova Credit, Nicky Goulimis, reached out to an executive at American Express to discuss a potential partnership. It was an audacious move but one that, three years later, has paid tremendous dividends for the company.

First, some background. Nova Credit is an award-winning startup (they were the 2017 PitchIt @ LendIt winner) trying to solve what has been an intractable problem. New immigrants, no matter how great their credit was in their home country, are treated like subprime borrowers with no credit history when applying for credit in this country. In today’s interconnected world this is just crazy.

I lived this problem firsthand when I first moved to this country many years ago. So, did the co-founders of Nova Credit and they decided to do something about it. I spoke with Nicky Goulimis, their COO, yesterday to find out how they were able to win over American Express and establish this groundbreaking new partnership.

After that initial message Nova Credit found a receptive audience in American Express. Nicky said, “They immediately grasped what we were trying to do. Our vision of creating a world beyond borders aligns with American Express’s promise to back its cardmembers, wherever they might hail from.” American Express recognized right away that this was a pain point for many potential American Express customers who were prime consumers back in their home country.

How the Partnership Will Work

[Read more…]

Filed Under: Fintech Tagged With: American Express, Credit Bureau, fintech partnerships, International, nova credit

Views: 603

Spring Labs is Building the Future of Anti-Fraud and Identity Verification

A new blockchain-based anti-fraud system is being built in cooperation with major online lenders

January 21, 2019 By Peter Renton 1 Comment

Views: 731

Last week fintech startup Spring Labs announced they are developing the Spring Protocol, a blockchain based anti-fraud and ID verification system, with 16 consumer and small business lenders as launch partners. The 16 partners include SoFi, OnDeck Capital, Avant, GreenSky, Funding Circle, BlueVine, Fundation, Upgrade, Fundbox, and Better Mortgage. There are six other lenders who were not named in the release.

I have been following the development of Spring Labs with great interest since they announced their initial funding in March of last year. Most of the management team came out of Avant so they were familiar to me and I have spoken to them several times since their launch, most recently, just a few days ago. What they are looking to achieve, I think, is groundbreaking and sorely needed in the online lending space.

Much has been written about the Equifax breach and it is clear that keeping credit and identity data in a centralized database is not the best solution and probably not sustainable long term. Spring Labs provides a real alternative, one that takes advantage of blockchain technology. They have built a peer to peer network that allows any member company to query information that may be held at another member company.

It is easiest to understand with the use of an example. Let’s say someone applies for a loan at Avant. In order to do identity verification on this borrower Avant sends a request to the Spring Protocol to see if any member company can verify the address and phone number of this person. A one way encrypted hash of this information is sent to the protocol as an API request to determine if any other network participants can verify the information for this borrower. It just so happens that Upgrade has made a loan to the borrower nine months ago so the protocol sends a request back that the information has been verified. Avant has no idea who verified the information only that this borrower is within the network and their identity has been verified. There is no central store of this information, instead, the validation of the request is provided via the protocol without unencrypted or personally-identifying data leaving Upgrade’s servers.

[Read more…]

Filed Under: Future Trends Tagged With: Blockchain, Credit Bureau, fraud, identity, Spring Labs

Views: 731

Experian Releases a New Credit Score Aimed at Non-Prime Borrowers

Experian’s Clear Early Risk Score is the first product released since their acquisition of Clarity Services

March 19, 2018 By Peter Renton 7 Comments

Views: 2,794


The ranks of thin file consumers continues to grow in this country. According to Experian these consumers now number 25% of the total U.S. population. These are people with five or fewer items in their traditional credit history.

Clarity Services is a credit bureau covering this non-prime population (you can listen to my podcast with Clarity founder Tim Ranney from last year). They have 65 million consumers in their database with a majority of these people not being covered by the traditional credit bureaus. Experian has been buying data from Clarity Services for many years but they decided last year to acquire the company.

Since the acquisition they have worked with the Clarity Services team to build a new score specifically for the non-prime segment. They are calling it the Clear Early Risk Score. As the name implies this new score is designed to give lenders a clearer view of the risk of these thin file consumers, many of whom should not be categorized as subprime.

I spoke with Alex Lintner, the president of Experian Consumer Information Services, last week to learn more about this new score and what it means for consumers.

Alex first explained that since the financial crisis more consumers have used short term lenders more than ever before. Many of these people have paid back these loans on time but they are invisible to the traditional credit bureaus. This is because many, if not most, of these short term lenders do not report their activity to these bureaus. But they often do report it to Clarity Services.
[Read more…]

Filed Under: Peer to Peer Lending Tagged With: Clarity Services, Credit Bureau, Experian, Subprime

Views: 2,794

Investor Intelligence

Peter Renton's Returns

Investor Forum

Lending Club Review

Prosper Review

Investor Resources

Most Popular Editorials

The Pure Marketplace Lending Model is Dead, the Hybrid Takes its Place

The 2018 Lending Club and Prosper Tax Guide

My Returns at Lending Club and Prosper

Map of Available States for Lending Club and Prosper Investors

Banks and Marketplace Lending Platforms: Ideal Partners?

Subscribe to the Podcast

Subscribe to the Lend Academy Podcast on iTunes
Subscribe to the Lend Academy Podcast
List of Podcast Episodes

Archives

Follow @LendAcademy Follow @LendIt

ABOUT LENDIT FINTECH NEWS

LendIt Fintech News, Powered by Lend Academy, has been bringing you all the news and information about fintech and online lending since 2010 when it was founded by Peter Renton. We not only have the industry’s most active news site, but also the largest investor forum and the first and most popular podcast.

We are a team of fintech enthusiasts who have been covering the industry for many years. With a deep knowledge of online lending, digital banking, blockchain, artificial intelligence and more our team covers the daily news and writes in-depth editorials.

Recent Editorials

  • LendingClub Launches Founders Savings Accounts
  • Top 10 Fintech News Stories for the Week Ending February 20, 2021
  • Podcast 286: Billy Libby of Upper90
  • Deep Dive into the MoneyLion and OppFi SPACs
  • Top 10 Fintech News Stories for the Week Ending February 13, 2021

Copyright © 2021 · Metro Pro Theme on Genesis Framework · WordPress · Log in