PODCAST TRANSCRIPTION SESSION NO. 193 / ANTHONY NOTO

Welcome to the Lend Academy Podcast, Episode No. 193. This is your host, Peter Renton, Founder of Lend Academy and Co-Founder of the LendIt Fintech Conference.

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Today’s episode is sponsored by LendIt Fintech USA 2019, the world’s leading event in financial services innovation. It’s going to be happening April 8th thru 9th, at Moscone West in San Francisco. We’re going to be covering digital banking, blockchain, financial health and, of course, online lending, as well as other areas of fintech. There will be over 5,000 attendees, over 250 sponsors and registration is now open. Just go to lendit.com to register.

Peter Renton: Today on the show, I’m delighted to welcome Anthony Noto, he is the CEO of SoFi. Now I think pretty much every listener has heard of SoFi, they really are one of the highest profile companies in all of fintech, they seem to have more news than pretty much any other company, I see them in the news pretty much every week, it seems like, these days and they’re really pushing the envelope.

They’re moving from just focusing on the lending side of the business to a really fully diversified financial services company. Now I wanted to get Anthony on the show, he’s been in the job now for over a year and I wanted to talk about, you know, how the last year has been, where the focus is today, what are some of the things that he’s excited about, what are some of the things they’re working on and we go through all the various different pieces of the SoFi business. It was a fascinating interview. I hope you enjoy the show.

Welcome to the podcast, Anthony!

Anthony Noto: Thank you for having me.

Peter: Okay, so I’d like to get this thing started by giving the listeners a little bit of background about yourself. You’ve certainly had an interesting career with some pretty high profile positions so why don’t you give us some of the highlights.

Anthony: I’ve been fortunate to be part of some great organizations with, you know, phenomenal cultures and really well known brands or products. I spent just about four years at Twitter prior to coming to SoFi first as a Chief Financial Officer and then as the Chief Operating Officer. We accomplished a lot of great things at Twitter and really restructured them and turned around the company and really proud of what the team was able to accomplish there and be a part of that team.

Prior to being at Twitter, I spent two different distinct tours at Goldman Sachs, four years from 2010 to 2014 in banking as the Head of Global Technology Media & Telecom Banking and the first nine years in banking, 1999 to 2007, as an Equity Research Analyst focused on technology and media companies, initially just on the Internet and then adding entertainment companies and then distribution companies like Satellite and Cable.

In between those two experiences, I was fortunate to spend three years at the National Football League as the Chief Financial Officer.

Peter: Okay, so you’ve been at SoFi now for just a bit over a year and I’m curious to sort of get your take on how it’s been and how has SoFi changed over the last 12 months.

Anthony: Sure, I’m really proud of the fact that we’ve made, since we spoke last year, and where we are now and having a couple of important things that tied back to our 2018 priorities and what we’re focused on in 2019, if I could. The number one priority when I arrived at the company was to ensure that we stabilized the core lending products in anticipation of an interest rate environment that would be quite (garbled) over the coming years and we focused on per loan economics, variable profitability so that the loans can be durable as interest rates increase and the economy slowed.

We meaningfully improved the approval and economics on a variable profit basis over the course of the year focusing on better efficient pricing, a tighter credit model focus and then really fine tuning our marketing initiatives to ensure that we’re getting high quality credit, but also managing customer acquisition cost.

On the operation side, there is an opportunity, not just to drive up efficiency in our operational co-spaces, but also to improve the value to our members that were applying for loans and shortening the time to fund and really drive meaningful increases in time to fund across personal loans and student loans in the order of magnitude on personal loans of 50% and about 30% on student loans.

In addition to that, we made a really tough decision to pull back a little bit on our home loan business or mortgage business as the experience that we were operating under wasn’t up to what we thought was adequate for our members, and so we’ve made a number of different decisions that put us in a great position now in 2019 to really step on the gas and double that business. So that was the number one priority and I feel like the team had to cover up the ball.

Number two was really accelerating our path to providing a border array of products. Our Mission is to help people achieve financial independence and realize their ambitions and we think in order to do that, we have to help our members, current members and new members, get their money right across borrowing, saving, spending, investing and protecting.

We’ve primarily been a lending business up until the point when I arrived in March and I really wanted to ensure that by the end of the year we are in a position to be able to offer SoFi Money which is now available to everyone to help on spending, saving and paying and also launch SoFi Invest which delivers on the investing component of getting your money right and they were also able to integrate in on the insurance side and to protect some partners. So we now have SoFi Money that’s available to everyone through the mobile application. SoFi Invest is available to everyone through the mobile application, in addition to insurance.

The team did a phenomenal job of not just building the right roadmaps to get us to this point, but actually, you know, building the code, deploying it and really building an integrated mobile experience. Our mobile application has been re-launched, the rating had been a 2.8 in the IOS Store when I started; it’s now a healthy 4.9 and 5.0 scale in the IOS Store. The mobile app is ranked in the top 80 under finance category and in the Google Play Store it’s ranked on the top 60.

We’re really proud of that and we’re seeing meaningful downloads per day organically on the mobile application which is helping fuel the demand for SoFi Money and SoFi Invest and ironically, even for our loan business so we’re really excited about that. And then our third priority, just to hit on very quickly, was to build the foundation of what the membership experience will be like and to build the principles of what we mean by member experience and we were able to do that in the first half of our position and we will start to see that unfold in the mobile app throughout the year, as well as behind the scenes in how we approach pricing, customer service, member events and member services & benefits.

The last thing, which, you know, was probably the most critical to the company’s long term success was working out our culture and making sure this was the place that people really wanted to work. We feel passionate about our Mission, we established 11 Core Values, they’ve been shared throughout the company, they’re integrated into our day-to-day functions and business, as well as evaluation of talent and retention of talent and promotions and we just scratched the service on the possibilities, but I’m really proud of what we have accomplished on the cultural side as well.

Peter: Okay, okay. So there’s a lot there to unpack and I want to actually spend a little bit of time on the different segments here. Starting off with student loans, because that’s really still been….I imagine it’s still sort of the biggest part of your business, but you guys invented student loan re-financing and, you know, you guys are, by far, the largest In think in the entire country, banks and non-banks, as far as everybody goes. Is this still the main engine of your growth as far as revenue goes and if so, like how do you continue to grow, you know, to what’s becoming a fairly mature segment.

Anthony: Yeah, I’d made a couple of points. We are more diversified on student loan re-financing from a member activity standpoint. It’s going to be the case that will have more members and SoFi Money and more members in SoFi Invest and SoFi Advice than we have them either student loan re-financing or personal loan re-financing. We’re not there yet, but by the end of the year we’re going to be pretty diversified across all over those different products.

Quite frankly, I anticipate that we’ll be relatively even across all those products by the end of the year and as 2020 unfolds, it’s likely going to be the case that we’ll see SoFi Money and SoFi investing larger than the number of members in student loan re-financing as well as personal loan financing. But, today, student loan re-financing is still a significant percentage of our overall revenue. I was referring before to the number of members that we have and that and personal loans are the two biggest drivers of revenue in totality. They’re both expected to grow quite meaningfully in 2019.

We expect to see an acceleration in our revenue growth given that we focused on quality in 2018 over quantity to ensure that our loans are durable. Now that we have that right quality measured by variable profitability and return on equity, we can really step on the gas within the credit and quality box that we’ve created. So, we expect decent growth from both those businesses today and an acceleration overall in revenue because of the opportunity we have to grow each of those businesses.

Peter: Right, and then you said….you mentioned before with mortgages, obviously mortgages….the loan sizes are much larger, is this something you’re also going to be trying to grow significantly in 2019?

Anthony: We think we have an opportunity to double the loan volume within home loans or mortgages and that will be a nice increase in revenue relative to 2018. Our members really want to have a relationship with us beyond one product; we’re seeing the cross buying already happen. On average, it’s been the case historically that about 40 to 50% overall mortgages are actually taken out by existing members. We’re seeing some really interesting trends of cross buying in our other products that we’re excited about and I think we’ll create a competitive advantage for us over time.

Peter: Okay, so let’s switch to SoFi Money and full disclosure, actually, I am a SoFi Money customer, I joined the waiting list as soon as I could and I opened my account, I think it was in January and I do….I think you’ve done a really nice job there and having a pretty compelling offering.

But, what I do want to know is like obviously this is a product that is competitive out there, but you’ve got a lot of competitors as well. Two questions, one, how are you actually going to make money on this product because you are giving very high interest rates and you’re can’t hold deposits yourself, as least as of yet. I guess maybe we’ll start there, how are you planning on making money on this product?

Anthony: Yeah, I’d make a couple of points. First, I’d say we’re looking at all of our products holistically, our products are member relationship and how those relationships translate into our overall business model and putting revenue and cost. So there’s an inter-relationship between all of our products that we’ll focus on over the long term.

But just to answer your specific question, SoFi Money, there are two primary revenue streams that we generate from SoFi Money today and there could be more in the future. The first primary revenue stream is the interest that we make on your deposits through a partnership that we have and that’s pretty common of both existing banks and new bank competitors, and the second revenue stream we generate is an exchange rate on your debit transactions. We do not charge any other fees from an account fee standpoint. ATM’s are rebated so you have complete access to your money to spend where you want and to be able to spend as much as you want.

The important thing about our product is that we’re giving you a really high interest rate at 2.25% and no requirements on direct deposit or advance minimums and you have access to that cash when you need it, where you need it to be able to pay, whether it’s person-to-person payments, Bill Pay electronically, Bill Pay…we’ll send a check for you, you can transfer money in and out of the account to your other accounts and you have the functionality of a debit card, ATM card and, of course, the integration with Apple Pay and Samsung and Google Pay. So we’re trying to give you the best of both worlds as it relates to how to manage your money and spending, saving and paying.

Peter: Okay, okay, that makes sense. So when I…I opened up my SoFi Money account which I have right now and you can see you’re pushing other products here. Right underneath it is SoFi Invest and I want to get to that in a second….like obviously the student loan product/personal loan product, they’re fairly high margin products and you talked about it yourself and you already said that you expect large numbers of people signing up here, do you expect significant cross sell between SoFi Money and then your loan products?

Anthony: Yes, absolutely, we’re already observing…today, we’re not trying to bring any new product marketing in the app to a SoFi Money member or SoFi Invest member and people are using the application assigned for those products by using those products. They’re becoming aware of other products and then they’re using a second product. So it’s very small numbers in total, but the observation on a percentage basis is much better than we thought it would be and it’s very encouraging.

So our strategy is to really put the member at the center of what we’re building and deliver to them the best borrowing or saving or spending or investing or protecting experience that we can. Our hope is that as we do that and they become comfortable with the brand and they trust the brand, they’ll be aware of the other services that we’re offering across those five activities and then they’ll consider those brands when they’re making that choice and they’re really signs that that’s working.

Peter: Okay, so then with SoFi Invest….actually, I can see there’s a button on your Home Page with SoFi Invest so it’s clearly something you’re also pushing hard and it seems like…..you know, you’ve launched a brokerage business. I mean, I feel like in the last month there’s been news coming out of your page almost every week pretty much where you’ve got the brokerage business, you’re talking about zero fee ETF’s, you’re talking about crypto trading in partnering with Coinbase.

So maybe let’s take a step back and say, what is SoFi Invest kind of ….what are you trying to achieve? Are you trying to be the one-stop shop so no one ever has to go to Schwab or Fidelity or Vanguard, whatever, what are you trying to achieve with SoFi Invest?

 

Anthony: Yeah, so I tie it back to our overall job to be done and then our Mission. When I mention job to be done, I’m really applying to our differentiation the concept that Clayton Christensen used in his book, Competing Against Luck, in that we ask ourselves, why will a member hire us versus others and we think our job to be done in that hiring decision by a member is to help them get their money right. If we help them get their money right they’ll reach financial independence and realize their ambitions.

In order to help them get their money right, we’re focused on all the activities that you do with the money as opposed to products or businesses and those activities are borrowing, saving, investing, spending and protecting. So our initiative behind SoFi Invest is to help people invest better and get the money right because they’re investing better in addition to their other activities. We think about within that category of SoFi Invest we want to offer a fast, easy to use solution; the fastest place to open an account, the fastest place to fund, the fastest place to buy a stock or an ETF or to invest in a Robo strategy, the fastest place to sell, the fastest place to do research; always accessible, always convenient.

Second is selection and so we’re focused not on just offering Robo advisory strategies, but also offering single stocks, ETF’s and our own ETF’s, for that matter, to fill-in a void where our direct target of millennials and Gen Z may have an area or pick an interest that’s not been met by this selection that’s being offered by others across the investing universe. So the first is fast; the second is selection; the third is content and the fourth is convenience.

As we release the content which are broad strategies; everything from education to helping for planning and so we do provide on the platform, on the mobile app articles and blogs about investing, about dollar cost averaging and to educate our members. In addition to that, within the app, you can click on the Membership tab which is right on the Home toolbar in the bottom next to the Home tab and choose to sign-up for a certified financial planning session with one of our certified financial planners.

You can schedule that right in the app and use your phone to conduct that conversation and so it’s not just the products that we’re offering in a fast way, but also supplementing the content across the spectrum of everything from quotes and news about companies, the news about business, to regional content to also live on planners.

And then last, convenience, we want people to be able to access their accounts from any device at any time. There are a number of other convenience factors that we’ll add-in over time in addition to the other four differentiators that I mentioned.

Peter: Right, right. So I want to switch gears a little bit and talk about regulation, you know, you guys…it was before your time at SoFi, but you had an ILC application out there which you guys pulled and clearly, you’re sort of ….you are entering into so many different areas of financial services and I’m sure it would be nice to have access to the banking system directly. So where are you at with bank charters today?

 

Anthony: Sure, 2019, and we have five priorities. The first is to build a mobile first/member first experience and drive the penetration of SoFi Money, SoFi Invest and SoFi Advice. Our second key priority in 2019 is to build out a membership experience more broadly and more overtly to members as they come on the platform. Our third is to continue to drive efficiency and effectiveness of our lending platform and as we relate to that, we will evaluate different ways to lower our cost of funding and financing.

We’re really fortunate today in that we have a three-layered approach to financing the loans. We have our own capital which is, as it has been publicly reported, $2 Billion of equity has been raised. In addition to that, we have partnerships with a number of banks, over 20 banks that provide us nearly $6 Billion of warehouse facilities and we also have a very important unsecured line of credit that’s over $500 Million. There is an opportunity for us to lower our cost of funding by building a deposit business and having the company as a whole being an IDI, insured deposit institution.

There are a couple of paths to go down to relate to that. we’ve evaluating those paths, whether it’s national banking charter or an ILC and the team is in the process of really thinking through all the strategic implications of either one. We haven’t made any decisions either on either one or actually going forward to try to become an IDI type of company, but it’s clearly an opportunity for us to meaningfully lower our cost of funding by 50%.

Peter: Sure, sure. Okay so then, when you look at the broader market today you’ve got all these different….you know, challenger banks, neo banks I guess you call them, like Chime, Stash, Varo and many others coming; Europeans coming, Revolut, Monzo and N26 and then you’ve also got the big banks. Many of them are putting out digital offerings directly to sort of the target market of millennials and Gen Z. So who do you see as your main competitor today and how are you differentiating?

Anthony: Yeah, I don’t think about it in that construct. I think about it in the construct of who provides better borrowing, who provides competitive abilities to spend to be able to invest, to be able to protect. So we think about those activities because those activities are trying to help our members do better and, as you can imagine, there’s a long list of companies under each one of those five activities. So there’s no one competitor, it’s we’re competing against that activity which is offered not just by traditional banks, also fintech companies and then what I’d say is increasingly and probably non-fintech companies.

Peter: Okay, okay. So basically then if you look at obviously the SoFi Invest, you’ve got to make something really compelling because you’ve got people offering free stock trading now, there’s free mutual funds, I know you guys have the free ETF’s so we’re looking at that specifically then and it’s a very sophisticated space already with many homely entrenched players.

I mean, like the zero fee ETF, you know, got a lot of publicity, I’m just curious about how you sort of make a dent in the millions and millions of customers out there. As you know, it’s not easy to get people to switch, are you focused primarily then, with this particular product, on people starting like an investment program for the very first time, are you trying to get people to switch, or both?

Anthony: They are core target and I use the word core purposely. Our core target for SoFi Invest is a new or beginning investor. And our goal, our goal is to help educate them on the benefits of investing to get their money right, our goal is to help educate them on how to actually invest and to, over time, become more confident in what they’re investing in and what they expect from a performance and a risk standpoint.

One of the reasons why we’re offering, you know, we’re in the path of offering stocks and fractional shares and one of the reasons why we’re offering unique ETF’s and one of the reasons what we’re not charging for trades or charging on the ETF’s that we’ve announced and also offering Robo advisory is to make sure that as we start to drive a connectivity point between us and a potential investor, new or beginning, that they have the broad selection they would consider.

Some want to buy stocks, some would prefer to start with the Robo account, some would prefer to buy a small amount of something that they already buy as a consumer, but actually the cost of one share is too high relative to what they would like to pay. As an example, Amazon is a company that has stock priced for one share is a really meaningful amount of investment and so being able to buy that in a fractional share is unique.

So we want to help these new and beginning investors, take the money that’s sitting in their cash account or their money account that’s not earning interest, that’s not really compounding over time and solely educate them on buying things that they like and being confident in what investing really means and taking them from some that’s very familiar like in the case of a consumer that spends a lot of money on an Amazon and having them become an owner of one of those companies that they spend a lot of money on and then over time as they get confident and understand what investing means, bringing them along the path of a more diversified portfolio and at a risk adjusted return that meets their individual long term financial goals.

The fact that we have stocks or we have fractional shares, we have ETF’s, we’ll soon have our own ETF’s and we have Robo and there’s no charging for trading or commissions is actually very unique, reduces the friction and gives them the assortment they would consider across the range of things that they would do first.

Peter: Okay, so I want to get back to the lending business for a second and talk about securitization. You know, I went back and looked at some of the numbers from PeerIQ last year and you guys were more than 50% of the total volume that went through in the online lending space in the securitization markets in the….you know, talking about consumers and student loans. So do you still look at securitization as really the absolutely critical funding source as you thought when you look at expanding and increasing the volume of the loans you put through?

 

Anthony: You know, we’re evaluating the varied different types of financing sources for the loans. You know, we obviously cannot use SoFi Money deposits or SoFi Invest deposits for financing source without a bank license at SoFi, either an ILC or a national charter so at some point, if we have an IDI organizational structure and a charter we could consider that, but until we do, it’s really not even a question.

Peter: Okay, okay, fair enough. So then you haven’t mentioned it…I presume this is not on the horizon, but I know the listeners will want me to ask this question, IPO plans for 2019, anything, where are you at with potential future IPO?

Anthony: It’s not a priority and we have no plans as it relates to that.

Peter: Okay, okay, fair enough. So then I feel like you’ve got so much that you’re trying to do here, you know, it feels to me, the news comes out all the time, I’m curious to see….like as you’re managing the different areas of your business, I mean, where do you spend your time personally? Are you really…..and obviously you’ve got a strong investment background with Goldman Sachs, obviously you spent time at Twitter, but like what areas of the business are you most connected to right now?

Anthony: I don’t think I would highlight one area as it relates to the businesses or being more connected than the others. They’re all in different stages of business formation so I’m spending  equal parts on all the five different areas that I mentioned. Outside of the day-to-day running of those businesses, I’m spending a significant amount of my time on recruiting great talent. We’re hiring a significant number of people in 2019 and one of the major areas of focus within is we’re re-hiring these great product engineering and design people.

We’ve launched a lot in a year and we have very ambitious clients that continue to shift more and more innovation every week and so that’s the area of the company that I’m most focused on, attracting and retaining great talent. I‘d say the second area outside of the businesses and attracting and retaining great talent is on partnerships. We teach you partnerships as they can accelerate the path for and making sure that we have the right top-to-top relationships with key partners, both current partners and future partners.

And then, last, really focused on building out the best culture in the world, our goal is to have the best culture in the world and we do not believe it takes any special type of innovation or technology or access to capital to get there. It really requires leadership, alignment and accountability and we’re committed to having the best culture in the world and making sure those three things are true and so I spend a lot of time on that as well.

Peter: Okay, so then where are you hiring people? I mean, I know you’ve got the San Francisco office, Healdsburg, I think there’s something in Montana, I think from memory, or Idaho, where are you hiring people today?

Anthony: Yeah, geographically, we’re hiring from all of our current locations across the eight different cities that we have physical locations in, we’ve had a small office in Seattle in addition to what we have already. As it relates to people, we’ve been really fortunate to be able to hire really experienced people from leading technology companies in the United States and the leading financial companies. I’ve been quite pleased with our ability to attract some people that have achieved significant things from the consumer Internet and technology world, in addition to getting some really experienced executives from the traditional financial world.

Rich Garside is the Head of Global Operations, thirty plus-year veteran from Citi; Assaf Ronen is our Head of Product who came from Amazon where he was a key leader driving the growth of Alexa Shopping; Tony Donohoe who came from Expedia as the Head of the Expedia brand CTO, he is our CTO; Michelle Gill is our CFO who was a Partner at Goldman Sachs and then was an investor in Structured Products at TPG.

We’ve also been able to hire great business unit leaders in each one of our business units from the walks of the financial services industry so I have been really pleased with our ability to find really talented experienced people across the big diverse area of industries.

Peter: Okay, okay, we’re almost out of time, but what is the total head count today?

Anthony: We’re about 1,400 people.

Peter: Okay, okay. Two more questions before I go, one real quick, are the challenges of SoFi had in 2017, do you feel like….obviously, it’s well behind you now, are you still asked about that when you’re talking to new hires, when you’re talking to reporters or do you feel like it’s now ancient history?

Anthony: I’m definitely not asked about that. I’m more asked about how do you build a culture, are you committed to build any culture, what’s the mission of the company, is the company, as whole, passionate about that mission, where do you want to take the business over the next five to ten years as opposed to the next year.

I would say those are the three biggest focus areas for the people we’re trying to hire, generally, and then when we get into specific cross functional areas and expertise like technology, there’s definitely a focus on innovation and using new technologies and how to think about the impacts of those technologies over the long term.

And then on the financial side, without a doubt, there’s questions about regulation and our ability to operate in a regulatory environment and as a regulated entity, which we already are, given that we have loan licenses by state, in addition to really understanding the amount of impact there’s in the business because of regulation, all of which we think is reasonable and has allowed us to operate at a really high pace of growth.

Peter: Okay, so last question, I’m sure you’re going to tell me anything that hasn’t been announced yet, but what part of your business should we expect the most future announcements this year? Where are you really focusing on innovating the most?

Anthony: I tie back to our priorities so number one is continue to innovate on being a mobile member first company with leading products in SoFi Money, SoFi Invest and SoFi Advice and so that’s the number one priority and I would put that at the top of the list, but right behind it is building out the member experience in a transparent and more over way to the outside world. Third is our focus on ensuring that we continue to drive really fast, efficient and effective lending with our existing and new members. So those would be the three key business focus areas.

Peter: Okay, Anthony, let’s leave it there. I appreciate your coming on the show today.

Anthony: Thank you very much for having me, really appreciate it.

Peter: Okay, see you.

SoFi is such a fascinating case study to me, you know, starting out as really a monoline lender in student loan re-financing and not just going into other areas of lending, but other completely different areas of finance. You know, I always thought that the secret to SoFi success was the fact that they were able to execute so well, particularly on the lending side of the business.

After talking with Anthony, you can really see sort of that military background which he didn’t talk about, but you see that sort of no nonsense execution type mentality that Anthony is bringing to the table here. I think it is going to be one of the most fascinating things to watch over the next few years…is to see how SoFi evolves, can they be a success in all these different areas of their company. I’m certainly not betting against them, it’s going to be interesting and I will be following along like everybody else.

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 2019, the world’s leading event in financial services innovation. It’s happening April 8th thru 9th, at Moscone West in San Francisco. It’s going to be the largest fintech event held in the Bay Area in 2019. We’ll be covering online lending, blockchain, digital banking and much more. You can find out all about it and register at lendit.com.

PODCAST TRANSCRIPTION SESSION NO. 192 / DOUGLAS MERRILL

Welcome to the Lend Academy Podcast, Episode No. 192. This is your host, Peter Renton, Founder of Lend Academy and Co-Founder of the LendIt Fintech Conference.

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Today’s episode is sponsored by LendIt Fintech USA 2019, the world’s leading event in financial services innovation. It’s going to be happening April 8th through 9th, at Moscone West in San Francisco. We’re going to be covering digital banking, blockchain, financial health and of course, online lending, as well as other areas of fintech. There will be over 5,000 attendees, over 250 sponsors and registration is now open. Just go to lendit.com to register.

Peter Renton: Today on the show, I am delighted to welcome Douglas Merrill, he is the CEO and Founder of ZestFinance. Now ZestFinance has been around for a few years now and in that time they have really built a reputation as one of the world leaders when it comes to the development and deployment of AI tools in the credit and underwriting space and their ZAML software, which we talk about, has really created some breakthroughs around explainable AI.

We go into that in some depth, we talk about some of the implementations that they’ve done, we talk about the data that goes into some of these models which makes it more predictive and we also talk about Douglas’ relationship with regulators and much more. It was a fascinating interview, I hope you enjoy the show.

Welcome to the podcast, Douglas!

Douglas Merrill: Thanks so much for having me.

Peter: Okay, so I like to get these things started by giving the listeners a little bit of background about yourself. You have had an interesting career so could you give us some of the highlights.

Douglas: Yeah, I think most people would describe me as having a somewhat random career. (Peter laughs)  I’ve got a PhD from Princeton, Princeton gave me a PhD in AI a long time ago, then I worked at a place called the RAND Corporation which is a think tank in Santa Monica, California where I did a lot of fun work, but notably it’s on the beach so you could walk out the back door and go on to the sand to go swimming and I spent a lot of time that I should have been in meetings out that door. (Peter laughs)

Peter: That must have been fun.

Douglas: Yeah, it was great fun, it just maybe wasn’t great for my productivity.

Peter: Right.

Douglas: Ultimately, I ended up at Google where I was Chief Information Officer for several years, but probably my most random job in my background was I was the Head of EMI Records, which was the fourth largest record company in the world when I was there. It has since gone bankrupt, but it went bankrupt well after I left; I want to make sure that’s very clear. (Peter laughs) After EMI, I founded Zest, I’ve had a fun career doing all kinds of interesting things with all kinds of smart and fun people.

Peter: Then so tell us a little bit about ZestFinance and…maybe start with how you got the idea for the company.

Douglas: I founded Zest in about 2009 or so, with a mission to make fair and transparent credit available to everyone. The operating idea was credit underwriting was a (inaudible) of modern life and yet about 80 million Americans either have no relationship with a bank or a poor relationship with a bank, 800 million Chinese are in the same situation, the numbers are similar for the EU and even more so for Africa, and I was interested in a world where so much has changed in the last couple of years in technology, particularly. How is it the case that underwriting hasn’t changed since 1950?

Before 1950, to get a loan you basically would go to a bank and you’d sit across the big mahogany table from a man and they were always men and they were always men wearing blue suits and red ties and you say, hey, I want a loan. And that man would say, oh, you know what, my kids played basketball with your kids, you’re a good person, I’ll give you a loan. That worked great unless your kids didn’t play basketball with his kids in which case you didn’t get a loan, that’s kind of unfair. Two gentlemen, one named Fair and one named Isaac, whose names later came together as Fair Isaac had a brilliant idea of using logistic regression and credit bureaus to come with a standardized credit score and that just changed the world.

I think it’s one of the most important innovations of the 20th century and credit availability went up markedly, losses went down markedly. It created this amazing, amazing positive outcome and I think everyone should thank FICO for the structure of the world today, but FICO has some weaknesses and the primary weakness is in the math itself. Logistic regression works well if you have every piece of data that you need and it’s all correct. In the event you have missing data or it’s incorrect, logistic regression fails in a kind of unpredictable manner.

I believed when I founded Zest that the primary reason that 80 million Americans were having trouble getting credit is that in many cases, on their credit bureau, the information was either missing, this is sometimes called a thin file problem, or there were errors. And so some number of them were actually good credit, they just didn’t have good FICO scores and we could use the math that we built at Google to do a better job of finding that and that turns out to be true.

In the last ten years, our clients have used our software to make material improvements in credit availability. This is not a ding against FICO, I still think FICO is the most important company in credit today, but I also think that lenders have more options because of machine learning.

Peter: Okay, so then…I guess you started obviously doing this. This was before really anyone was really talking about, you know, machine learning and certainly it wasn’t top of mind of many people in the credit space. So how did you kind of start the company? I think I read somewhere that you actually made loans yourself initially so tell us a little bit about those early days.

Douglas: Yeah I mean, I think people who came out of the machine learning companies, you know, places like Google or Amazon, would have recognized my company as another ML company so I feel like I came out with the notion that I wanted to build an ML company in credit. However, I needed to demonstrate to the world that that made sense so the way we did that is for a couple of years we actually made loans using our algorithmic structures and our tools and, you know, they worked pretty well and then we ultimately stopped but the problem with being a lender is it’s capital inefficient, it takes a lot of capital and the capital is expensive.

Peter: Right.

Douglas: The reason that banks make loans is they have very cheap capital so I didn’t really want to do that long term. I did it long enough to demonstrate our tools and then we ended up serving a couple of clients who themselves made loans and we did that in a fairly bespoke manner for quite a while and then when we moved to our broader product which was the same across the broader market and you can kind of see the trend there as the financial world caught up with, oh, I understand machine learning, I understand how these tools might work, we progressively got more and more towards the SaaS model that I actually wanted to start when I founded Zest.

Peter: Okay, so then, I’m just still curious about those early days, I mean, how were you able to convince other lenders that your solution was better than what they were currently doing?

Douglas: Ultimately, that’s just math. So you give me a sample of loans that you’ve made, whether it was a good loan or a bad loan, and we’ll build a model for that. And then you give us a set of loans and you don’t flag them, so you don’t tell me whether they’re good or bad, and I just predict whether they’re good or bad and then you go count…

Peter: Right.

Douglas: …tell me how many the model was right with, how many was it wrong with, ultimately that’s just math and it’s pretty well understood math. Our tools, we do that computation automatically, we do a bunch of economic parsing automatically so you can say, oh, if we had done this model we would have saved $100 million in losses, or would have gained $40 million because of increased approval rates. Those things are just math.

I think harder than that is getting people to sort of think through, wow, you’re going to use 500 variables instead of 20, we all understand 20 variables; 500 variables, hey that’s a little scary. It just turns out to really help in those cases where you have thin files or you have erroneous data. Having more signals, more variables helps reduce the impact of those incorrect things.

Peter: So then let’s talk about that for a second. What are some of the data points that you’re using today, or maybe even back then, what were the data points that you used, I mean, maybe just give me some examples. I know it’s not just one data point, there’s hundreds, as you say, but what are some of the examples of that that you found really helped lift the performance of the model?

Douglas: So one of the things which is true when you have 500 signals is that really, really good signals, still don’t matter all that much because every signal is worth, you know, 1/5 of 1% so a great signal is worth a very small amount. Early on, we discovered that people who put their name in in all uppercase were substantially riskier than people who put their name in correct case. So for example, if I put my name capital D O U G L A S, I’m much higher risk than if I put capital D lowercase… o u g l a s, I just misspelled my own name which is fairly funny (Peter laughs).

So if I fix that, I don’t exactly know why that’s true. I can make up a story that oh, maybe people who are putting their name in proper are better rule followers but your applying kind of post hoc reasoning to something you don’t know why it is, but that’s a real signal in a real model. Similarly, one of our recent clients discovered that if you put in the company name that you work for, including the company type so instead of putting in ZestFinance, you put in ZestFinance, Inc., you’re a higher risk. So being more correct in the company name increases your risk, well, why is that? Well, you don’t really know, but maybe that increases the likelihood that you’re a bot because no human uses “, Inc.”

Peter: Right, right, yeah, that makes sense and as you say, it’s not like someone…obviously there are many, many good people will actually put that in like that, but it’s just, as you say, it’s one of 500 signals. If the other 499 signals or 498 signals are good, it won’t make any difference, right.

Douglas: That’s correct because you end up adding them all up so if that one is a little bit wrong, well, alright the others are probably a little bit right and ultimately, it gets zeroed out in some sense.

Peter: So then one of the big things today that is talked about a lot when it comes to AI is this thing about explainability and you know it’s something that I know has made many banks and actually many lenders, in general, reticent to embrace this fully because you’ve got to be able to provide a reason to the borrower why he was declined. So tell us about a little bit about the advances that you guys have made in explainability.

Douglas: So first of all, it’s a valid worry because the banks in the US are required to give adverse action notices. If they decline you, they have to tell you here are the top five reasons why we declined you and it turns out that process is not ideal, but it’s still required. Also, you’re required to periodically demonstrate that you don’t have a disparate impact problem where you don’t tend to give loans more to men than women or any other of the protected classes.

Doing those things requires understanding what’s in your model. Understanding what’s inside that logistic regression model is trivial, you just read the coefficients. Understanding what’s inside a score picker machine or a massive gradient boosting machine, those are hard so we really did need some big improvements in understanding how to describe what’s in the model. There have been over the last few years, some improvements.

For example, there’s some really simple technique called permutation impact where you just bury stuff and see what happens; it’s like when you bury one thing and it doesn’t matter, it’s probably not important. That turns out to be a pretty, you know, gross view of the model, it doesn’t have much kind of fine grain and it’s pretty hard just to run against big models, but that was a really big step forward where someone realized, oh, let me just pull on some strings and see what happened, good insight.

I think that increasingly the world is demanding a much finer grain understanding of what’s in a model because we’re increasingly caring about implicit bias and other topics around there where you could, from a potentially regulatory problem and from a moral problem, back into something where you might be disadvantaging groups that you don’t want to do and that requires a more fine grained approach. Arguably, a much more fine grained approach, and there’s some other things to it as well, but ultimately, you need to be able to use a tool better, you need to look across the table at your financial services client and and say, yes, I guarantee you will understand what’s in your model, you’ll understand how it works and that will be able to work within a real-time.

Let me give you an example about what I mean by real-time. So there were two amazing basketball players kind of in the 1980’s, one named Michael Jordan and one named Scottie Pippin, they played for the Chicago Bulls together. When they played with the Bulls together, they won, I think, six league championships, I think Michael Jordan got three or four MVP’s, he was the shooting lead for many years, he was just a star on those teams. So I often like to ask the people I’m speaking to, for a show of hands, who is the better player, Michael Jordan or Scottie Pippin, nobody ever chooses Scottie. That poor guy gets no respect. But if you look at what happens after they’ve played together it’s kind of interesting.

So after they play together, Michael Jordan goes to Washington Wizards and proceeds to have two massively losing seasons where nothing good happens to the poor guy and he retires. Scottie Pippin goes to the Trailblazers where he proceeds to have three seasons where they make it to the finals every year, they don’t win, but they make it a long way, Pippin’s got an MVP, Pippin has a great career.

Now you’ve got to ask the question, oh, hang on, who’s better, is it Scottie Pippin or Michael Jordan? It made a lot of sense in one context, the answer was obvious when they were together. In the other context when they’re apart, the answer is also obvious, but they’re different and really thinking through in a complicated machine learning model how to handle that difference is the core of the explainability problem.

Peter: Okay, so I guess say more on that like put it into a credit example. A customer can send an adverse action letter to their borrower declining them, how does that sort of translate?

Douglas: So today when banks send adverse action letters or send these decline notices they more or less take the reasons from one of the credit bureaus and they just sort of package them up into a physical letter and send it off. Some banks do a much more thoughtful job of this, but some banks, if they’re a little bit rote or whatever, that credit bureau said, I’m just going to pass on. In machine learning, it’s hard to figure out what those things should be, right, you’ve got 500 signals and they’re all relatively unimportant. How do you find the five that are most important?

So that involves using the permutation impact idea, taking 500 signals and randomly permute them against each other which requires something like 500 factorial computations which is a number larger than the number of atoms in the universe. It’s a very hard problem to solve that way. Then you look at the ones that bubble to the top that say, oh, if this number had been higher or that number had been lower, your score would have been high enough that we would have granted your loan and then you’ve got the five approval reasons. The problem with that approach is that you don’t have enough time to have more computations than there are atoms in the universe…

Peter: Right:

Douglas: That’s just hard and where I come from just hard is the problem you don’t want to solve. So there are optimizations, as you can do, that yield both fine-grained, better fine-grained solutions and are more computationally trackable, but that’s a silly thing for a problem of how do you understand high level interactions between signals, between variables to know which of the five that are more important.

It always comes back to the same question, but if you look at variables alone, i.e. you look at Scottie and Michael together, you see one picture of the world. If you look at variables together, like maybe not just signal one, but signal one and 100 together, you get a different picture, i.e. when Scottie and Michael played apart. Our position is you have to compute and use as many different, conjoined probabilities or if you will combinations, as you can, to try and make the best possible list to borrowers of what they need to improve to get a loan in the future.

Peter: Okay, so something that you would do is say, right, you don’t just improve these one/two things, you maybe improve these six or eight things because they’re in combination. I guess maybe we can…maybe that is part of what you’re offering and I want to get into this in a little bit, your Zest Automated Machine Learning product, but is it part of the output or part of the offering to be able to create these adverse action letters that make more sense or that make sense based on what the borrower wants to actually hear?

Douglas: Yeah, that’s a great question. Maybe just for clarity, I’ll take a step back and say what do the demo tools actually offer and once I get you that maybe it’ll be a little bit easier to talk about…

Peter: Yeah, sure.

Douglas: So there are basically three kinds of steps in modeling. There is the actual math itself, there is convincing yourself and those around you that the math is actually correct and there is putting it into production where it lives for however long you are comfortable with it living. You can get from Zest, if you want, a modeling environment in which you build models, but you can also use your own modeling environment or any number of other tools out there.

We do sell some AI little probes, little kinds of measuring things that pay attention to your model build and we use that AI to help you do something which is super hard which is convincing regulators of what you’re doing and why you’re doing it. After that process we build a model for you, an AI model to look at your AI model, they help you understand and build the model that for any individual applicant will yield the adverse action letters. You’ve got this AI running to look at AI and the AI and the AI that is looking, is what you ask for help when you deny an applicant. And then we built a bunch of technology when you put your model into production to use other kinds of AI to notice if your model is consistent and [inaudible].

One of the things which is true about many large machine learning models in production is that they’re a little bit kitschy, their answers switch a little bit and sometimes kind of for no reason they go running for a cliff and throw themselves off (Peter laughs) and people who haven’t read a lot on machine learning models might sometimes forget that and your model runs off a cliff and crashes to the rocks below and you’re still counting on it giving you loans.

Our clients get a set of tools that maybe can’t prevent the model from trying to throw itself off a cliff, but they tell you, hey, something bad is happening, stop making loans before the model goes south. And also allows you to track things like monthly economic impact and return, lots of sort of useful stuff to the business folks so ultimately, what we’re trying to do is build ML-based tools that are invisible generally, they’re sort of half in the background, but yield all this stuff that business and audit and regulators can use to run the business and not care anymore that the technology underneath has changed.

Peter: Right, right, got it. Okay, so let’s talk a bit about business cases. It would be great to hear maybe a couple of examples of…there was a big name that came out, I think it was just a few days ago, with you guys in the Wall Street Journal, but just maybe take us through a couple of examples of implementations that you’ve done with names we’d recognize.

Douglas: Sure, so I think the Wall Street Journal, a while ago, outed Synchrony as a client of ours, so that’s an old one from like 18 months ago, 24 months ago and in that they described a very large amount on the order of several tens of millions of savings so that’s like a name you’d recognize and it’s the same process, right, the model was really good, their models were amazingly good and then this kind of monitoring stuff that we did.

The announcement you were referring to a few days ago was with Discover, obviously again, an amazingly talented organization. We’re super proud to be their partner and they’re using our tools to basically, their goal was to get a handle on increasing losses and our tools have helped them get a handle on that and also helped them interact with the regulators on that point and they’re in production or moving to production to do the same thing, around like is my model okay, is it healthy, etc.

So you get this case where they’re obviously quite talented modelers. The business case they were focused on was, hang on, we’re seeing some…a little bit erratic increase in losses, certainly not disastrous, but not the trend you like. So they built models on our tools, we helped them build the regulatory and audit documents for those models. When I say we, I say like this AI, which was monitoring their model build, built these things for audit and regulatory and then the model is in production with our other AI kind of bounding it to keep it away from the cliff and the rocks below.

Peter: Okay, so then when you’re going into these companies, is it…someone like Discover, do they already have like AI models in place, or they’re just sort of using…I imagine there’s still plenty of financial institutions using sort of more of a linear regression type models even today, I would suspect, so what is the process like when you go in and I presume you can work with someone who’s got a very advanced model and somebody who’s got a very rudimentary model, is that correct?

Douglas: That’s true, and lots of financial institutions still use a linear regression or logistic regression, some actually just use a score from a credit bureau so that’s sort of like the minimalist approach. We find that across the spectrum, from there all the way up to financial institutions that are actually working on machine learning themselves that the tools that we provide add a lot of value; we’re just describing them in a different way. So if you’re a financial institution that’s just using a score, you don’t have a lot of data lying around because you’re just using a score and so we might not be able to build a model or you might not be able to build a model day 1 because, you don’t have the data.

You might need to do what’s called a retro and get data from a credit bureau, or you might need to do a testing plan to generate some future loans and allow a model to be built so that might be a different process. If you are using a logistic regression you’ve got data lying around and some companies that use logistic regression for underwriting also have other interesting data like…maybe they have phone call data, maybe they have collections data that isn’t currently being used in their underwriting because it’s very hard to use that data in underwriting. But, machine learning models and the models that people build with our tools can use that kind of data.

So for those folks who might say, yup, give us your underwriting data and oh by the way, let’s get some of this data from underwriting, excuse me, from collections, let’s get some over here from call data and let’s use all of that stuff to build a model,  which gives us much more information, much more signals; the models are better.

Finally, the organizations that are building their models today, often have really, really great sets of data and sometimes they’ve got data lying around that isn’t used, etc. and where our tools add value. It’s helping them get that out of the research lab through the regulatory folks and in the production. So if you think about it we’re sort of adding three different kinds of value which map roughly to the three parts of our software.

Peter: Okay, so then just on that, someone comes to you and they’re just using a FICO score and that’s pretty much it…so you don’t have like an off the shelf model that you can provide them or do you? You said they need to go out and sort of get data from credit bureaus, whatever, so you can build the model. So you don’t have something off the shelf that you can just provide them?

Douglas: No, we don’t provide generic scores, it’s one of the powers of places like FICO that they have these really pretty generic scores. We don’t have generic scores or generic models, what we have are the tools to help financial institutions move into the machine learning future. Where presumably they’re going to use scores from great places, but they’re also going to have more complicated models around those scores.

Peter: So then are you finding more and more institutions already have decent machine learning models in place that you can certainly improve upon, but they’re already doing things reasonably well, or in the financial space are people still pretty backward for the most part?

Douglas: I think almost all banks have at least one person or one group doing research on machine learning, but they’re mostly focused on the math today so getting from that research to production is quite hard. That said, a huge number of financial institutions have, high quality fraud institutions notably like FICO’s Falcon which are machine learning products, they just don’t look like it to the banks. So in the underwriting space there has been not a lot of progress in figuring out how to get the mathematics from the laboratory all the way into production.

Peter: Right, right, that makes sense. So you see a lot of partnerships now that are going on between banks and fintech platforms, whether you’ve got Avant or Amount now doing…they’ve announced a couple of decent sized banks that they’re working with…there’s obviously Upstart and others that are coming in as well. How do you sort of play into this shift that we’re seeing between fintechs and banks, the partnership model that we’re seeing?

Douglas: I think it’s important to differentiate kind of a white label kind of approach with a tools based approach so you look at what some of the great partnerships have been. Both Avant and Upstart are really great companies, Upstart founded by Dave Girouard, who I was privileged to work with at Google for many years. So those guys tend to focus more on a white label approach that says, hey, come over here, write me a check and I will put up an underwriting engine for you and that serves certain kinds of needs extremely well, that’s not what we do.

What we do is give you the tools to build your own models so in some sense, if you want to think about it in, you know, aphorism terms, a white label partnership is I’m giving you a fish. What we do is we’re teaching you how to fish. Each one has really big advantages and really big disadvantages. We’ve proposed that we want to create organizations, develop an organization, grow from a handle on the side of a financial institution and the easiest way to do that is to actually provide tools. Now we have to provide training and support and a bunch of other stuff as well which is the downside, but that’s why we’ve chosen the way we’ve done deals so our partnerships don’t look like the kind of white label deals that you’ve seen a lot in the press.

Peter: Right, right, got it. So we’re almost out of time, but a couple of things I want to hit on before I let you go. I’m curious about how you interface with the regulators. You know, there was certainly…a lot of attention was paid when Upstart got their no-action letter from the CFPB because that sort of was the first movement by a regulator saying that, hey, this is…we’re interested in actually, you know, exploring and accepting some of these new changes, but some of the things that you’re talking about…I’m curious, it seems like it’s beyond some of the things that the no-action letter talked about so tell us about your relationship with regulators, how you’re engaging with them.

Douglas: So we spend a lot of time with the regulators because ultimately, our clients need assurance that the regulators understand and I’ve done things like…I’ve done machine learning training for all of the members of the CFPB, I did ML training for the examiners of the OCC, same in the Fed, same in the FDIC so I spend a lot of time with the regulators. In fact, we drafted a set of, you know, addenda to the FAQ used by the examiners of all three of the major bureaus to sort of make it a little bit more possible for them to ask the right questions about machine learning and get the right answers.

The short answer is everyone believes that ML is the future, and everyone we’ve talked to believe that ML could be the present, but they don’t view themselves as the (inaudible). There are some things that we have to work through. My perspective is that people who say that the regulators are inherently blocky and they’re really retrograde actually aren’t spending any time talking to them. There are amazing folks who are working at the regulatory agencies because they believe serving the US government is an honor and that protecting consumers is an important moral duty. These folks are not trying to take us back to the 70’s, they’re hoping to prevent 2008 again. So my experience has been actually quite positive and quite frequent, I’m in DC a lot.

Peter: Right, right. Okay, so last question then, I’m really curious about where you think this is going, I mean, obviously, things have changed a lot in the last five years and I think in the next five years, I imagine, it’s going to change even faster so take us through where…how good can these models get? Where will Zest be and sort of your abilities be in five years time?

Douglas: Five years to a startup is an infinite window (Peter laughs) so it’s quite hard to predict five years in advance. I think it is hugely indicative that Discover went out with their press release saying, hey we’re partnering with Zest. I’d say that the classic big monster dominant quality institutions are starting to pay attention and I expect more of those and for various reasons I expect there will be more of those.

So I think the next thing that happens is how does mass adoption happen, mass adoption for scores took about 20 years. I don’t think it’s going to take that long for machine learning because the process of actually adding the systems and the skills is easier now than it was then so some amount farther. My hope is just that in five years I have a pool table at my office that works (Peter laughs) so that’s my big goal. (Peter laughs)

Peter: Okay, well on that note, we’ll leave it there, Douglas. I really appreciate you coming on the show today.

Douglas: Hey, thanks so much for having me.

Peter: Okay, see you.

You know, I think most people in credit today recognize the importance of AI tools and how it can really help increase loan performance, it can help expand the credit box and, you know, can bring credit to more creditworthy individuals. It’s a testimony, I think, to the work that Douglas and his team have done at Zest Finance that they were able to bring in such large financial institutions such as Discover.

A quick plug, if I may, we have the CEO of Discover and Douglas on stage at LendIt Fintech USA in a couple of weeks. If you’re listening to this after April 9th, you’ll be able to watch the video on our website, it’s going to be one of our featured keynotes of the entire event.

Regardless, I think, more and more of these large financial institutions are going to be deploying these sophisticated AI tools and I think that will be a great thing as we continue to expand access to credit to the underserved.

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 2019, the world’s leading event in financial services innovation. It’s happening April 8th through 9th, at Moscone West in San Francisco. It’s going to be the largest fintech event held in the Bay Area in 2019. We’ll be covering online lending, blockchain, digital banking and much more. You can find out all about it and register at lendit.com.

PODCAST TRANSCRIPTION SESSION NO. 191 / JOHNNY REINSCH

Welcome to the Lend Academy Podcast, Episode No. 191. 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 2019, the world’s leading event in financial services innovation. It’s going to be happening April 8th through 9th, at Moscone West in San Francisco. We’re going to be covering digital banking, blockchain, financial health and of course, online lending, as well as other areas of fintech. There will be over 5,000 attendees, over 250 sponsors and registration is now open. Just go to lendit.com to register.

Peter Renton: Today on the show, I am delighted to welcome Johnny Reinsch, he is the CEO and Co-Founder of Qwil. Now Qwil is a fascinating company, they haven’t been around that long, but they’re starting to get traction and their business is all around providing advances for gig economy workers, typically freelancers, small businesses.

They provide an advance so these people and companies can get paid immediately so we go into that process in some depth and we talk about how they underwrite, the kinds of markets they’re in, we talk about the performance of the advances and we talk also about the challenges that gig economy workers face in today’s economy, in today’s financial system. It was a fascinating interview. I hope you enjoy the show.

Welcome to the podcast, Johnny!

Johnny Reinsch: Thanks, Peter, it’s wonderful to be here. Thanks for having me.

Peter: My pleasure. So I like to get these things started by giving the listeners a little bit of background about yourself and what you’ve done in your career to date.

Johnny: Awesome, yeah, let’s see, I grew up in a town called Fresno, in the middle of California. I went to UCLA for undergrad and then UC Davis for law school and took my first legal job in San Francisco. This was at a firm called Kirkland and Ellis in 2008 or so, 2008 was my summer associate job and 2009 was when I started, but got to see a lot of really interesting things happen at that time as you can imagine, especially centered around the financial markets. It was a really interesting way to get exposure to finance, in general.

We were sort of helping unwind a lot of some of the issues that had happened in the market around derivatives. I was an M&A lawyer, but there wasn’t a lot of deal flow so I was helping out with a lot of bankruptcies and the like and really got to see, you know, what things about the market had precipitated the decline and ultimate crash and what factors kind of led to that.

So I did M&A for about five years and then I decided that my heart wasn’t in it and part of this was from working on a deal where I really worked with and advised on some phenomenal founder teams, both sell-side and buy-side M&A. I always gravitated towards the mission of the companies and in one deal, in particular, you know, the founders were so passionate about what they were building that they walked away from a very significant amount of money to go with a lower bidder on the deal because they didn’t agree with the deal terms, solely on the basis of a disagreement with the deal terms.

That was kind of a formative moment for me in realizing that I was looking for a similar path where I felt so strongly about something that I could sacrifice a pretty significant amount of personal wealth in furtherance of my ideal. I think I spent about another four months as an M&A attorney at that point before I landed a job at a client actually which was in the bitcoin space, a company called Xapo, run by a phenomenal fintech entrepreneur, Wences Casares, and that’s where I really got my exposure to how the credit system works, aside from what happened in 2008/2009, but how the credit system works in markets where there aren’t stable banking systems or stable payment rails and payroll departments and all the things that we kind of take for granted here in the US which worked pretty well.

I was fortunate for that experience because, you know, I’d found a founder that was very passionate about what he was doing because he was trying to solve his own problem. His problem was that while growing up in Argentina Wences had encountered numerous points in time where currency controls and the devaluation of the peso essentially meant that he and his family, friends, etc. lost all their wealth, all their money really because of the doings of a central government.

And so I got the bug on financial inclusion through Wences and Xapo and then ultimately helped build, you know, the early days of the bitcoin ecosystem with some strategic initiatives and some regulatory outreach before encountering the pain point that Qwil currency solves which was my own run in with our financial system in the US.

So I like to call myself a “recovering M&A attorney” that learned about the credit system by seeing it essentially at its worst and then coming in from the disruptor side with a bitcoin angle.

Peter: Right, so let’s go back then and talk…I’ve read about this before, tell us a little bit about the moment that led to the idea for Qwil.

Johnny: Absolutely. This is, you know, really it’s fear, I felt visceral, palpable fear and I’ll talk about what I mean by that in a second.

Going through my professional career as an M&A attorney, growing up…my parents were not wealthy, but we never had concerns about, you know, could we afford our house payment and the like. I was very insulated, frankly, but when I left M&A and when I was making far less money, I ended freelancing quite a bit to help supplement my income.

There was one month where a client of mine didn’t pay on time and as a result I was going to be…you know, I had a mortgage payment due and not enough cash in my bank account so I had five days or so before I was basically going to default on my mortgage and I didn’t really know what that meant. I never had anything like that happen, I just knew it was really bad and as sort of the days go by and no payments coming in, this level of fear and anxiety continues to rise. It’s a very physical sensation that you get when you’re nervous, anxious, whether, you know, you’re about to jump out of the plane skydiving or you’re walking in a bad neighborhood late at night. We all know what that feeling feels like and this feeling that I have as I got closer and closer to that date where my mortgage was about to be…you know, the payment was going to be auto debited from my account, I was just living with that feeling constantly.

And so I finally went to my bank and I go to speak with the….you know, I have relationships with these people, they’ve known me and helped me with financial products for, you know, basically from when I graduated law school and I go to speak to my team over there, told them the situation, great client, you know, they’re going to pay, I don’t know why they haven’t paid, but they always pay, they are very high credit and walk through all the different products they have with me so my car loan at that time, my mortgage at that time, they see every student loan payment I’ve ever made, that I’ve made, you know, they have all of my financial info and I ask, hey what can we do about this, I’m really scared.

The response I got was that I should go look into payday loans or borrow money from friends and unfortunately Johnny, we’re not your friends in this case. I can’t tell you how disappointed, scared, frustrated, I mean, it was such a big mix of feelings in that moment and I just felt very much left out in the cold. What was confounding to me, ultimately, when I got over my emotional response to this, was they were in the best position to give me some kind of financial product.

They knew everything about me and something wasn’t matching up with that narrative in my head of great FICO score, great credit history, tons of financial products with this institution, you know, real problem, but they can’t do anything about it. Like they had a real opportunity to make some very low risk cash in that situation and they didn’t take it.

So I started to really think about that and realized that as a freelancer, as a small business owner, as you’re sitting in our credit system, in a bucket that’s very difficult for our credit system to underwrite, or there is no underwriting guidance for it, or it doesn’t make economic sense because, you know, the dollar amount is so low. The credit system we live in that provides mortgages and car loans and what not really, really nicely for M&A lawyers doesn’t really do anything for freelancers and small business owners.

So I started diving into why that is just from that one innocent little question of why did this happen to me, me and my co-founders realized that one, I wasn’t the only one, but also there are, you know, there’s a massive need and opportunity to really provide a lot of value and help to these populations of folks that are similarly situated as I was at that moment.

Peter: Right, so let’s just move on then to explaining what actually Qwil does, what is your offering?

Johnny: Qwil helps freelancers and SMB’s get a payment that would be otherwise paid, let’s say, in 30 days, we give it to them today. We do this by selling into the places that freelancers and SMB’s are working for. So if you work for a large Fortune 500 company and that Fortune 500 company has Qwil available when there’s a payment that that Fortune 500 company is going to pay you in 30 days, Qwil finds the data around that payment being at that time.

Sends the individual that’s going to get paid in 30 days an email saying congrats, you’re going to get paid in 30 days and if you’d like to get paid today, we’ll give it to you for…on average it’s about a 3% fee that’s just taken off so if it’s $100, we would say you can get paid $97 today. It’s really that simple, no fee if they don’t do anything and if they don’t want to take the funds early and that small fee doesn’t bear any interest if they want to take the funds today.

Peter: Okay, so then let’s maybe give some examples of some of the places that offer Qwil, maybe some of the different verticals that you’re in. Why don’t you give us some examples.

Johnny: Absolutely, and I’m glad you asked. You know, in my situation at that moment, I self identified myself as a freelancer and so one of our earliest set of partnerships was with freelancer marketplaces. I don’t know if I’m allowed to name anyone, but we work with all across the realm of skill sets so we have folks that are drone pilots and work for drone marketplaces, we have folks that are delivery drivers and work for driver marketplaces, software engineers that are freelancing and, you know, those types of freelancer marketplaces and design, etc. so we really cover a range of skill sets.

Anyone that’s a freelancer in one of those marketplaces that we cover, every single time they have any payment that’s been approved, it’s going to come to them in the form of hey, you can get paid today, if you want, or we’ll just push funds through to you on day 60 as well and incur no fee.

We started to apply that model, we found a lot of…one, we had a lot of success with that and we realized that actually, a lot of the volume that we have isn’t from people like me that are looking to avoid a bad situation, it’s freelancers and agencies, SMB’s, etc. that they’re using the capital to grow their business.

So we started to identify the verticals that would similarly sort of follow that narrative because we really like the story of, if you take a dollar from Qwil that’s, you know, going to be 30 days earlier than when you otherwise would get paid you’ll actually make more money, that’s a wonderful story.

Peter: Right.

Johnny: So we started to go into digital media marketplaces so app developers and really anyone operating in these sort of publisher networks like the Apple App Store, Google Play are very well known examples and realized that, you know, these small businesses are so sophisticated from a marketing perspective that the same kind of rules apply if we advance them a dollar that they would otherwise get paid in 60 days by Apple, they’re going to make much more than the fee that they paid on that dollar by deploying that dollar today.

Peter: Right.

Johnny: So that’s another area where we’ve been quite successful.

Peter: Right.

Johnny: Then we have a final vertical that we just broadly refer to as “liquidity as a service” because we’ve been able to drop it into a bunch of different contexts, but really where you have any B2B payments ecosystem or, you know, an ecosystem where there’s a technology solution that’s facilitating either the exchange between a business and another business or freelancer, the payment flow between a business and another business or freelancer, we can, you know, basically let that ecosystem leverage our tech.

They can continue to do what they’re doing well and we offer liquidity to that ecosystem broadly as just a part of their baked in offering and so that’s our “liquidity as a service” thing and we’ve done that with ERP’s, payment networks, invoicing networks, big vendor and supplier networks, those are sort of examples of things we’ve done that in.

Peter: Okay, so really it’s still all of those things that all boil down it sounds like…it’s one product that’s providing an advance on an invoice or future earnings. So just so I’m clear though, say you’re an independent contractor like you described in your situation with your mortgage payment, you’re an independent contractor and you’ve got some kind of random company that you may have never heard of, can you still get financing there or do you really need to be hooked into the corporation providing the invoices?

Johnny: We have to be hooked in for now and we do have plans to expand that, especially with…we get a ton of recurring use, in fact it’s staggeringly high. You know, we’ve built up relationships with a lot of the folks that are using this as a new source of working capital and in the future we’ll definitely have options for going outside of our immediate ecosystem, but for now, yeah you do have to be working with a company or a platform that Qwil is embedded into.

Peter: Right, right. Okay, so then what are the dollar amounts that you’re typically doing and what’s sort of the range and the average of the dollar amounts you’re advancing?

Johnny: About $1,000 and the beauty of the technology is that by essentially going from the top down, we’ve cut out a lot of the costs that are associated with underwriting or acquiring, you know, a given customer and so that means that we can do a $10 advance or we can do a million dollar advance. Ultimately, our machine and the way that we underwrite and manage risk and match the advance with the lender that’s actually providing the funding, that can be hyper efficient so that we can do tiny, tiny advances, or very large ones.

Peter: Right, right. So you’ve got someone like you’re embedded with a company, there’s someone who comes on and says, right, whatever it is, let’s use an example, I don’t know whether it’s actually your client, whether let’s say a Lyft or Uber driver comes in and says, right, I want to get my pay now and you’re hooked into them, what actual underwriting do you do? Do you just…because if you’re hooked into that company , wouldn’t you…maybe you just tell me what kind of underwriting you do.

Johnny: Yeah, it depends, we could be underwriting a bunch of people, we could be underwriting a bunch of companies. So in the Uber scenario, we would probably be underwriting Uber and the driver on, you know, do they have a high dispute rate, is the rider likely to ultimately come back and say that the driver was driving like a maniac and they want their money back.

Let’s take another example where let’s say we have a designer that’s going to be doing some work on behalf of a staffing firm, that has a client that’s a Fortune 500 company. The Fortune 500 company is ultimately the one that’s paying for the invoice, the designer is the one providing the services, the staffing firm is kind of making the market; we might underwrite everybody to get to the ultimate decision and it happens very quickly obviously. But to make the ultimate decision about price and try and get the best price to the freelancer in that scenario. We’ll go to as many counterparties as we possibly can to get comfortable there.

Peter: And then once the company then pays you, instead of paying the person who earned the money…is that how it works?

Johnny: That’s right, we are typically in the funds flow and we’ll get paid back first and then if there’s…let’s say our designer, they’re going to get $100 in 30 days, may decide, okay, I want to take an advance inclusive of the fee on half of the amount so they take $50, we would get the $100 payment from whoever the staffing firm client is or the staffing firm directly and then we would just pass through whatever the remainder is, to the designer.

Peter: Okay, I’m curious because one of the great things about this business…I know you haven’t been in business that long, but these are really short term duration types of advances so you can get a pretty good idea how you’re doing, I mean, you’ve had many, many turns on your book, even if you have only been doing it a couple of years so tell us a bit about the performance of your portfolio.

Johnny: Yeah, I’ll talk about the turn first. Initially, it was average 7 days or something and as we’ve worked with more sort of high end freelance and SMB’s, the counterparty risk gets better, but the payment terms get worse. So it’s sort of counter intuitive, but you’ve made it as a designer, you just landed that contract to do design work with Apple but then you find out that Apple pays you 90 plus days in the future. So as we’ve worked with more counterparties like that, what initially started out as a week has trended to more like 30/35 days and we assume that it may land somewhere more like 45 or 60, ultimately.

But the risk model and how we underwrite has performed very, very well and we’ve had nominal defaults across the entire book and have been able to provide very competitive pricing for the freelancers and SMB’s that are taking our offering such that we ultimately end up being usually less than what a freelancer or SMB would have to pay to accept a credit card payment or use a payment processing platform. That’s been great and the fact that we do have fast turning stuff and we are leveraging sophisticated underwriting to minimize risk on defaults, it just ultimately means that we can push the best pricing back to our SMB’s and freelancers.

Peter: Right, right, got it. So then can you give us some sense of the scale that you’re at today?

Johnny: Totally, so from a headcount perspective, I think this is really interesting, we started last year with about seven people so 2018, and last year alone we advanced $70 million to freelancers all over the world and we’re now about 25 and growing quickly from a headcount perspective and on pace to do a very large multiple of what we did last year.

So we’re still lean and mean, we’re still leveraging tech in all the right ways to grow as quickly as possible and retain as low a headcount as possible, but also in the process of expanding headcount and make sure that we can deliver the best product to our global SMB’s and freelancers.

Peter: Right, so then I read somewhere that when you were starting Qwil, you were turned down by investors more than a hundred times when you were initially raising money. I know we got introduced through Ron Suber who pretty much every listener here would know and I know he’s involved in your business. But, maybe you could tell us how you got over that hump and who are your investors.

Johnny: Absolutely, you know, it’s funny when you’re first starting, everything takes longer. I woke up last week and we were featured in Fast Company’s Most Innovative Companies and everything moves way, way faster now, but going back to that initial seed round that we raised, man, that was brutal. I knew that we were on to something big, I knew that our ability to make an impact in a way that would keep me excited and interested…you know, effectively, I’m trying to selfishly solve the problem and the fear that I felt when I couldn’t get help, right.

But I knew that this was something I wanted to do and devote a lot of my life to so that the 300 million or so freelancers like myself at that time and countless SMB’s don’t have to have that problem. We were going to need venture funding to do this in the right way. Fintech companies are hard, you know, we’re sending payments all over the place, we’re advancing money out that is from the capital markets and from our lending partners and doing that as a small business or something less than a technology company is very tough and so it was very obvious that we were going to go for VC dollars.

That said, I’m a first time founder, I previously was a lawyer and had worked in bitcoin, like there were always these reasons why… I didn’t know anyone in Silicon Valley that was really going to back us at that time so I had to do say a hundred, but it has to be more, frankly, and just to get the word out about what we were doing…

Peter: Right.

Johnny: …and then all the reasons why you wouldn’t back a first time founder come in. So ultimately, we had to do tons and tons of meetings just to bring in the first $500K or so that came in through Plug and Play and 500 Startups and then it gets incrementally easier after that and then it starts to move much faster, but it was a slog, it was really tough until we got the anchors from Plug and Play and 500 I was basically just doing meetings as my full-time job and getting told no a lot.

Peter: Right, you’ve got to have the vision there because that can be very demoralizing.

Johnny: Man, even…I had full conviction that I was seeing something, me and my co-founders were seeing something that the market had just not yet seen and was not capable of seeing because not everybody has felt this fear in the pit of their stomach about how am I going to make my mortgage payment, but it was tough, it was really hard.

But then as we ended up finding…I’m glad I did that because it introduced me to a ton of really helpful people, you know, Alireza Masrour and his team at Plug and Play Ventures and Sheel Mohnot and his team over at 500 which I think is how we initially got connected, have been incredibly helpful throughout the lifecycle of the company and have became personal friends of mine. So ultimately, we got a Series A done about a year and a half or so after we did that first seed round and have done a bunch of debt deals and have brought in amazing investors like Ron Suber and Sam Hodges from Funding Circle so I’m very thankful, but yeah, it definitely was hard at the beginning.

Peter: Okay, so then when you sort of look at what you’re doing, I mean, it seems to me that there’s a lot of people doing obviously factoring or invoice finance, some are doing it online and some are doing it in like for app developers in different niches, but when you look at your business who do you see as your main competitors?

Johnny: Yeah, I get that question a lot and as you mentioned, there are a lot of folks that do something similar and I don’t view us as competitive with a traditional invoice factoring company. I would actually view us as a complimentary offering where you know if you get the email from us that says hey get paid now or get paid 60 days later when the company finally pays you.

The small business that might use BlueVine or FundBox or someone else to factor their invoice, they can also use us. We’ve applied an approach to customer acquisition where we can really leverage technology to embed in an ecosystem and be alongside all of these other offerings.

So there really aren’t a lot of folks that I view as just like us though there are good examples of folks that offer sort of parallel services and Clearbanc does advances to Airbnb hosts and DailyPay does advances to freelancers and employees and Earnin does advances for employees before the payroll runs. Yeah, those are kind of the folks that I would put in the same bucket as us, but our approach is novel and our use of technology and how we leverage technology is novel.

Peter: Right, right. So it sounds like you’ve really focused on the I guess…it seems like the gig economy workers seems to me like where you have really focused your energy and that’s sort of your primary market. It’s interesting because it seems like when I look around, I see it’s massive and getting bigger and people now talk about the fact that this is a whole shift in the way people earn a living.

Some people are going to be earning a living as gig economy workers for their entire career and they may have two or three jobs, but it just doesn’t seem like…there’s not someone helping them along the way like I hear things like Uber are providing quicker advances, these companies are providing quicker advances, but you know, it seems that the gig economy worker has kind of…from a financial services perspective, they are now underserved, it feels like, and they are becoming the largest segment. Why do you think that…is it just because companies move too slowly, why do you think these gig economy workers are underserved?

Johnny: Well, you know, this is a massive paradigm shift and by 2027, we’ll have over half the workforce that’s working freelance and that doesn’t even account for the SMB population of newly minted small businesses that in my mind serve as the backbone of the business economy. Everybody has to start somewhere and so, yeah, it’s a big problem.

I think on the pure freelancer or contractor or however you define that term because, you know, everybody self identifies a little bit differently, but we’re talking about revamping a payroll process that has been embedded deeply since before the 50’s when folks stayed at their same job…if you’re really, really lucky, you know, GE or something similar for your entire career and then you retire rich with a pension.

That paradigm is shifting and the infrastructure around it is not and these are deeply embedded companies that have long standing histories and not a lot of incentive to switch and accommodate a small dollar, much higher frequency type of existence in the form of your gig economy, your freelance contractor, etc. worker. So I think that’s one reason and there’s also just a lag in the awareness from the financial services standpoint.

Companies know this, companies understand that to be strategic, to be competitive in the marketplace right now, you have to have, and it’s called contingent workforce strategy, and that means you have to be able to supplement your existing workforce with the entire range of the talent spectrum in the form of folks that are willing to freelance basically at a moments notice, or that you’re able to actually acquire and get the right freelancer to work on, whatever your discreet sort of security job might be or something like that where like the best folks in the industry actually will never work for you full-time, they’re going to be freelance. So companies get this, but financial services…I think there’s always a lag on innovation on these fronts. I give financial services a pass there because there should be….

Peter: Right.

Johnny: …moving, you know, large amounts of money into like new novel stuff might be bad, you know, macro-economically or systemically, and so I think that’s one of the reasons. But I’m sure there are a host of others that I’m missing, but either way there’s definitely a massive opportunity for startups and founders especially if you’ve had the pain point and you can weather the storm of getting rejected a hundred times. (laughs) It may ultimately be very much worth it.

Peter: Right, right. Well we’re out of time, but before I let you go I’d like to get some insight into what this year holds, what are some of your goals for 2019?

Johnny: Absolutely, I mean, my high level goal is to continue to solve this problem and to expand our impact in just the number of people that we can help. We’re not quite at a 100,000 of SMB’s and freelancers that we’ve made an advance to, but getting there pretty quickly and I’d like to get that number as high as possible in addition to just sheer dollar volume of folks that we can help.

And then, obviously, sort of the final bit of that is to make sure that we’re embedding ourselves into new platforms and channels that are springing up all over the place, whether it’s, you know, a gig platform or a freelancer workforce platform or an invoicing platform. You know, the more of these things that we can embed ourselves in, the more people that get to benefit from our service.

Peter: Right. Okay, Johnny, we’ll have to leave it there. It’s a fascinating story and I wish you all the very best. Thanks for coming on the show today.

Johnny: Yeah, thank you Peter so much for having me and looking forward to seeing you at LendIt.

Peter: Yeah, likewise. Okay, thanks, Johnny, see you.

You know, companies like Qwil are taking an age old problem and bringing it into the new world and the reality is gig economy workers…they want to be paid immediately, they don’t have to wait 30, 45, 60 days to be paid. And when you’re looking at just a 3% average fee, for them that’s not going to be too high price to pay, I don’t think in many cases, where they can get their money, they can either put it to work in their business again or they can make their mortgage payment or whatever it means because we’re living now on this on-demand world and this sort of 30, 45, 60-day payment cycle, it just doesn’t gel with the modern world which is why I think companies like Qwil have a lot of runway to go because they are providing this on-demand payment in an on-demand world. It really feels like that is what consumers want, that’s what these freelancers want and, you know, I see a lot of room for a company like Qwil to grow.

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 2019, the world’s leading event in financial services innovation. It’s happening April 8th through 9th, at Moscone West in San Francisco. It’s going to be the largest fintech event held in the Bay Area in 2019. We’ll be covering online lending, blockchain, digital banking and much more. You can find out all about it and register at lendit.com.