One of the great things about the near prime space in online lending is that there are fewer competitors operating there. The reason is simply that it is harder to make work. There are challenges with data, pricing loans and customer acquisition costs just to name a few.
The next guest on the Lend Academy Podcast is Tom Burnside, the CEO and Co-founder of LendingPoint. His company has made tremendous strides in the near prime space in a short time and is now one of the emerging leaders in the industry.
In this podcast you will learn:
Tom’s background building databases and credit systems.
The founding story of LendingPoint.
How he was able to transition from small business lending to consumer lending.
A profile of the typical LendingPoint customer.
The loans terms that LendingPoint offers for their loans.
How they are acquiring customers today.
The kinds of data they use for underwriting.
How they are using technology to run their underwriting and elsewhere in their business.
What was behind their decision to acquire LoanHero, the point of sale platform.
How they have integrated LoanHero into their company.
How they closed the additional $600m credit facility from Guggenheim.
Where loan performance is coming in versus expectations.
The scale they are at today with loan volume.
Where they are on the journey towards profitability.
Tom’s thoughts on what we are doing well and what are we doing poorly as an industry.
What they are working on at LendingPoint for the future.
This episode of the Lend Academy Podcast is sponsored by LendIt Fintech Europe 2018, Europe’s leading event for innovation in financial services.
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PODCAST TRANSCRIPTION SESSION NO. 153 / TOM BURNSIDE
Welcome to the Lend Academy podcast, Episode No. 153. This is your host, Peter Renton, Founder of Lend Academy and Co-Founder of LendIt Fintech.
Today’s show is sponsored by LendIt Fintech Europe 2018, Europe’s leading event for innovation in financial services. It is coming up on the 19th and 20th of November in London at the Business Design Centre. We’ve recently opened registration as well as speaker applications. You can find out more by going to lendit.com/europe.
Peter Renton: Today on the show, I’m delighted to welcome Tom Burnside, he is the CEO and Co-Founder of LendingPoint. Now Tom has been around this industry for a long time, he’s got some fascinating insights onto the industry as a whole and he came out of retirement just a few short years ago to start LendingPoint so we talk a lot about that story. We talk about their approach to underwriting, sort of the segment of the industry that they’re focused on, we talk about a new acquisition that they did just a few months ago and their loan performance and how they’re sourcing capital today. It was a fascinating interview, hope you enjoy the show.
Welcome to the podcast, Tom!
Tom Burnside: Thank you, Peter.
Peter: So I like to get these things started by getting a little bit of background about yourself. I know you’ve had quite an interesting career so why don’t you give the listeners some of the highlights of your background.
Tom: Sure, Peter. You know, I have been in the credit market telling credit stories or big data stories for 20 plus years, closer to 25, and my story really kind of starts back when I was working with a company called First Data. At that particular point in time, we were trying…our mission statement at that point was to try and make a transaction as quick as possible. We were kind of moving out of more of a manual process for authorization of credit cards and for, you know, at that point, checks. What we were trying to solve for was to make sure that we could make the merchant’s experience as easy as possible for them to accept a credit card or a check.
So we really started using data at that particular point in time to differentiate the good guys from the bad guys, the transactions at the point of sale. So we started by first building big negative databases that allowed us to identify customers that were going to be bad or had been bad before and the probability was very high that they were going to be bad again and where we found ourselves was trying to become creative in saying, okay, now we think we can predict the people that are going to be bad in the future. Unfortunately, that didn’t always work as well as we would have liked it to work and I’ll give you a really quick story there.
One day, I’m sitting in the offices at a company called Telecheck at that time, who I was working with for First Data and the mayor of Houston had just been declined for checks that he was writing during the holidays (Peter laughs) and he was not a happy guy. The realization around the table quickly at that particular point, was hey, guys, the bad check writer and the good check writer look very similar, they both write a lot of checks and so how do we distinguish between the good check writer and the bad check writer? It’s going to take more than just a negative database and it’s going to take more than just a frequency count to make sure that we’re deciphering between the two.
So the first thing we did was did something that hadn’t been built yet and that is, we actually built for credit card and for checks, we start building a good check writer base or good transaction customer, a good credit card customer base and what that meant was we started storing information immediately upon every transaction that would come in. We start incrementing your files and putting you in different ranks of just how great of a customer you were and how many times we had seen you, the frequency of your transactions, you know, cyclicality, meaning that during seasons you might do more than you do during the middle of summer so we start to really kind of compile this information.
That was my first endeavor into really building databases and systems using kind of the power of data and technology to tell the story of somebody in a unique way. Over the years, we’ve done that, you know, over the years we’ve been developing that particular technique. I did it recently, probably I guess now I say recently now, probably 18 years ago,19 years ago, we did it with a company called Comdata Gaming which became Global Cash which was a publicly traded company for a while, it was taken private just a few years ago, but there we were solving for a different issue and that was the consumer.
You had a recreational gambler and you had somebody that was more of a tourist that was coming into Las Vegas or to one of these gaming institutions with less frequency and what we needed to do there was be able to issue lines of credit or prove their check or credit card transaction within seconds so they could go out and enjoy the entertainment. So that transaction took typically about 35 minutes to 40 minutes to move through, we got that down to about 12 seconds and that became a booming industry, but it was really around our ability to be able to tell the story with, again, unique credit, being able to separate the difference between a recreational gambler and somebody that was a tourist so we may not have seen them anywhere else so we had to use other data sets to be able to compliment that story.
You know, that really took me to CAN Capital, I was recruited away from First Data to move into CAN Capital to be able to take kind of data, unused data, a very thin credit file which was a small business owner and be able to give them access to capital by using data that wasn’t traditionally used in credit scores to be able to tell their story and give them access to credit.
Peter: Right, right. And so I know that you spent quite a bit of time at CAN Capital along with others on your team there at LendingPoint so what was the sort of the original impetus to leave and really to start LendingPoint? What was the original vision there?
Tom: You know, what’s interesting about the small business market…you know, I had been doing it for almost 14 years and what I had looked at is I actually retired…I decided to step aside for a while. At that point, CAN was contemplating going public and so I had an opportunity…I said look, I’ve kind of taken it where I can take it, and so I decided to retire.
What was interesting about it is a couple of my partners who we had done some business with prior, we’d actually tropicalized the small business lending and so Juan Tavares and Victor Pacheco, my partner at CAN which was Franck Fatras, came down to my wine cellar and said, hey, Tom, we’ve got to talk about, we’ve got a new idea, let’s talk about consumer. And so that was really kind of the impetus of this and we were just kind of talking about what was the opportunities and how would you go after it and my wife said, boy, this is a great time for you to get out of retirement, I need to get some things done so here I am.
Peter: (laughs) Right, so on that, I’m actually curious to get your perspective on the shift from a small business focus which obviously CAN Capital…I mean, these are small businesses often with the owner of the company very much a part of the credit and underwriting decision. How does that sort of translate to a consumer lending operation?
Tom: You know, the disciplines are very similar. So what we chose to go after in this market and we really said, look, let’s start up LendingPoint, was we wanted to go back into credit and tell the story of an underserved customer. That underserved customer and the target that we were looking at was customers between 580 and 680 and the uniqueness of them as well as the small business owner is both of them have very thin files.
A lot of why you see the teammates around me at the moment that I have brought with me is because those teammates came with me because they knew how to tell stories on thin credit files and use alternative data to do so and differentiate between a good credit and a bad credit. I think the disciplines are very similar because it was both an underserved market that needed to be complimented with other types of information that help round out their story.
Peter: Sure, you mentioned a credit score between 580 and 680, but beyond that can you sort of give us a little bit more of a picture of the typical LendingPoint customer?
Tom: Yeah, so our core business as we started off…the core business was really targeted at 580 to 680, the average customer was around 655 to 660. It was somebody who has been in their job for roughly ten years, they tended to have thinner credit files. You know, what we’ve been told by some of our larger online aggregators is that by us using this additional information and augmenting the data to tell a better story of the customer, 30% to 40% of the time we’re actually giving a unique offer or an offer that they weren’t getting from anybody else.
And so that customer, you know, they’ve been on the job for ten years, they tend to be a 665 customer, they tend to be looking for about $11,000 or so and for a variety of reasons; it could be anything from adopting a child, to a wedding, to consolidation of bills so it can be kind of across the board. That’s been our core customer for a long time, that’s obviously diversified recently.
Peter: Okay, so then can you give us a sense of the loan terms that you’re offering in your core product here as far as interest rates, length of term, you said it’s a $11,000 average, what are the other terms on the loan?
Tom: So the customer is typically seeing something in that 11.99% from an APR perspective, 11.99 to about 34.99. They’re seeing offers from 24 months to 48 months and typically the average right now is 41 months if you kind of look at the overall blend of the portfolio. So again, this is in the core product today. That profile has been pretty consistent over the last few years.
Peter: Right, and you mentioned that this is a segment that is less competitive than the prime segment, that’s for sure. You said oftentimes these people don’t see many other offers or any other offers so how are you actually acquiring these customers? What are the different approaches you use?
Tom: Well we use a variety of ways. One of the ways that we do it is obviously the large aggregators. You know, you can see us between a LendingTree, Credit Karma, Dot818, Underground Elephant and kind of across the board. We have about 300 partners that are pointing to us today some way or the other, but some are simple referrals. These are people that either have a link on their website, they’re just pointing to us for a transaction and we may be the only person looking at that transaction so it’s a variety of different ways that we see. We also do e-mails, we have SEO, we do our own mailers, we’ll drop a million pieces roughly this month. You know, so we’re across the board, we’re acquiring customers in multiple different ways.
What’s interesting, one of the areas that was emerging for us was this point of sale, we start to see a lot more of those transactions coming our direction and so we have been focused on that as well.
Peter: Right, we’ll get to that in a little bit, but before we get there I want to actually dig into your underwriting approach because you’ve talked several times already about the use of data and the use of alternative data for thin credit files, can you tell us a little more about that and what kinds of data you’re using and how your approach to underwriting…what you do that’s different when it comes to underwriting?
Tom: Yeah, so you know the question that we’re always trying to answer, it’s not about the quantity of the data but it’s the quality of the data, right?
Tom: And just how predictive it is in making a decision with a customer and what we’re really always trying to understand is the customer’s willingness and desire to pay because if you can understand that…you didn’t grow up as a 800 FICO. You had to grow into that score and the way you grow into that score is by having good transaction history, good credit transaction history, but you have to have enough of it that builds a big enough profile to build that score. A lot of people don’t realize that a thin credit file…that doesn’t mean they’re bad, that just means they have a thin credit file and a thin credit file will typically keep you below a 650 FICO.
And so millennials are learning that the hard way; millennials, specifically, is somebody that hasn’t established a lot of credit and if you really get below three credits, you need three credit reporting agencies or three credit reporting venders. What you need is you need to augment their story with other information like cell phone information, like light bill information, things of that sort that allow you to get a better understanding of their willingness and desire to pay back.
Peter: Right, and then how has sort of technology weaved into this whole piece? I was in your office earlier this year and you had quite an impressive operation there, there was lots of technology everywhere. So tell me a bit about what you’re doing there.
Tom: When we start to look at this I think that the first thing you’re trying to solve for is you’re trying to solve for the ability to scale the business, make sure that it’s a reliable platform, make sure that security is foremost, that you’re taking care of the information that the customer has enabled you to view or to access and so those are kind of your core fundamentals.
From that, the next issue is really saying, okay, how do you use data to tell stories that affect the overall organization. So what that means is that not only are you trying to tell credit stories, you’re trying to store data in a way that is indexable, that we can use to create additional variables, additional information in the future to tell a better and better story.
So to kind of give you an example, you know, when we started the organization, we started with eight variables, today we are at 56 variables to make a decision on a customer. That came through a series of being able to take that information and log it in such a way that we were able to go back and do a better job of understanding it so we grabbed a lot of data up front and then we put in a system called Hortonworks on our side and Hortonworks allowed us to use structured and unstructured data to be able to tell stories across the organization.
Structured data is used for credit decisions, but unstructured data can be used for marketing decisions, it can also be used for skill based routing when a call comes into the building, who best handles that call, what call should I be prioritizing up front. When you think about it, when we looked at the scale, the reliability and the security, we said, look, we are going to use AWS or Amazon Web Services to give us this massive amount of scale that allows us to kind of plug & play and build, you know, we can take on 30/40/50% bump in transactions.
I can give you an example. Last month, we took on 370,000 transactions, a month before we only saw about 260. So when those things happen you need to be able to have that kind of reliability in your systems, but then we used this Salesforce rail which keeps the information very structured for us, it really works as a magnificent CRM for us. From that, we plug in all the components that allow us to give that customer that frictionless process.
On top of that, now you’re looking at things like a GDS which is a rules engine that allows us to strip off transactions at the top of the funnel when a customer comes in and sell that particular transaction in the way that a bank would want to see that transaction, we can strip it off and give it to them first and then rank order the rest of the transactions through our normal systems. So we are very, very dependent on obviously systems in our case from marketing to the actual credit to the operational side of business.
Peter: Right, right. I remember you showing me some of the cool dashboards you had on your phone that sort of monitored everything. That whole thing was quite impressive to me. So anyway, I want to talk about the acquisition that you did a few months ago now of LoanHero, a point of sale platform. You mentioned point of sale earlier so firstly, maybe we can…this was your first acquisition, I believe, so tell us what was behind that decision to acquire a point of sale platform.
Tom: I think when we start with this, you know, what you’ll see is a lot of times what we’re doing inside the organization is we’re identifying new areas of opportunity and what had emerged in our overall reviews was we were starting to see a lot more point of sale transactions of partners that were plugging into us, were plugging in to solve a point of sale need.
So we start building that, we start plugging in more and more of these partners and we are doing $5 to 6 million of it a month so the conversation at the executive team was hey, do we buy this or do we go out and build it. And what we found was an opportunity to buy something that allowed us to get to market much quicker and as a result of that, we purchased a company called LoanHero at the end of December of last year.
Peter: Right, and then so how are you sort of integrating them into your organization? You’re based in Atlanta, I believe they’re in San Diego, how have you integrated them into your organization?
Tom: That’s a great question. I think from our side, we were looking at them as a technical platform only, that’s really what we were looking for was to try to build…and what I mean by tech platform is you kind of have to think about the merchant’s experience is different than the consumer experience. So their frontend really works with the interface as an interface with the merchant and our backend works as the interface with the consumer.
So the merchant can select…you know, that platform allowed us to have the merchant select the offer they wanted to give to the customer and what that delivery would look like so they can choose the discount rate, they can choose the type of product they want to deliver to the consumer and that identifies the criteria and it comes to our backend here. Our backend here then makes the credit decision with the understanding of what the product is that they wanted to offer to the customer. So what it really allowed us to do is have more of a merchant facing platform and we’re still using the rails here on the backend to do all the credit decisioning and the data capture.
Tom: On the LoanHero acquisition, all we did was buy the platform of the organization. We didn’t buy their customer base and other things, we bought specifically the platform. That was the transaction there.
Peter: Okay, okay, makes sense. So I want to talk about a recent announcement that you did just earlier this month. You made an announcement about a $600 million credit facility from Guggenheim, I think you also had an existing facility with them. Tell us a little bit about how you were able to get that deal closed and what it means for LendingPoint.
Tom: Yeah, the Guggenheim team has been a great team…partner for us, but I think what it has allowed us to do is to really build the capacity to continue to serve our customer, both our customers and our customers’ customers now, right. As a result of that, we now have about a billion dollars of access to credit to be able to balance sheet about a billion dollars. They’re also walking us through today, they’re moving us in through Kroll ratings so we will have a rating on the assets before the end of the year which will allow us to get more access to the ABS markets and otherwise. That to us is kind of the ongoing journey so they’re actually being our partner in that process to take us through that journey.
Tom: The other thing, as you probably recognize, the rates are amazing. We’re coming in at 467 spreads, we’ve got a 90% advance rate, most of that is just because we’ve had phenomenal credit performance, we were oversubscribed. JP Morgan was the lead in this as well as Macquarie were the two leads and it’s worked really well for us. We were oversubscribed in the A, B and the C tranche, we were oversubscribed in the other areas as well.
Peter: Okay, what can you tell us about credit performance? Obviously, you’ve been in business now for a few years, you’ve had a few turns on your loan book or at least a couple of turns, can you give us some sense of where performance is coming in versus expectations?
Tom: Some of the early vintages the pool factors are pretty well done as an organization. The discipline at the organization has been really to augment the scoring model so it has allowed us to stay consistent on our journey and performance…you know, performance right at the moment is we’re going to come out somewhere between 13 and 15% write-offs on most of the pools roughly ’17, you know, the end of ’16, ’17 and obviously this year…I don’t know that that’s the right number yet. I think we might want to bring that up a little bit so that we can serve more customers. You know, when we think about our targets, we’re really pricing it to be around a 15, when we’re coming in the 13 handle it doesn’t feel right, it doesn’t feel like we’re saying yes frequently enough, but we can change and refine that. You don’t do that in big movements, you do that in slow movements…
Tom: …but, you know I think that we’re feeling very good where we’re at. What we think about the world is we think about this as a two times coverage, we want to be able to absorb some beta risk in the portfolio if something were to go bump in the night, a shift in the economy or otherwise.
Peter: Right, that makes sense. So then what about scale, I mean, can you tell us like how many states you’re operating in, what kind of scale when it comes to loan originations you’re at today?
Tom: So today, we operate in 49 states plus DC and so you could access a loan on one of our platforms in 49 states + DC today.
Peter: Okay, and so can you give us some sense of volumes?
Tom: Yeah, we’re just now bringing up really the point of sale so it hasn’t contributed at the levels yet that we are anticipating, but overall, we will close this month probably around $45 million. Last month, we did $44, we’ll do 45, 46 million this month. We should be closing in on $70 to 75 million a month by the end of the year based on our current projections.
You know and some of this Peter, to be honest with you is us digesting the volumes that we’re currently seeing. Just by example, we do $43 million on about 260,000 applications that we saw coming through so some of this is on us just to make sure that we’re digesting at the appropriate rate, to make sure that I’m staffing right so that bump we saw again recently in volume, we just need to be able to digest that. If we digested that at the same rate as we have before, this is a $70/75 million a month business without us really needing to expand our acquisition channels.
Peter: Right. What about profitability and generating cash flow, where are you at on that journey?
Tom: Thank you for asking (Peter laughs). You know. I think this is a wonderful part of our journey, right. In three years we’re going to be generating over…this year, we’ll generate over $100 million of revenue and that is a very unique…if you think about the market and others that are in the space that’s very unique.
I think the second thing to it is that we’ll get into cash flow position of being positive this year. We’ll get into an EBITDA positive 1st quarter of next year. We pushed that off a little bit with the acquisition because we knew that we were going to have to spend a little bit of money to get the platform up. We were originally targeting to be profitable by 3rd quarter of this year on an EBITDA basis, but then we pushed that back off a little bit for the acquisition which I think at this point was the right thing to do.
Peter: Right, that makes sense. So we’re almost out of time, I want to step back a second and ask you to sort of…when you look around the online lending space…we’ve talked privately and you’ve mentioned some of the things you don’t like about the space, I’m curious to get your sense on what do you see, what are we, as an industry, doing well and what are we doing poorly at?
Tom: You know, I think there’s two areas that I continue to be impressed by. One of them is I think we have done, the industry has done an amazing job of allowing a consumer to know that they can make a transaction in the comfort of their home, on a beach with their cell phone, but they can get access to the financial needs that they have. The industry has done an amazing job with that.
You can see that in the most recent TransUnion study where it’s showing the access to…that the consumer unsecured lending is growing at a pace faster than even credit cards so I think that’s an amazing feat. I think the other side of it is that the process continues to be with less friction in the process, it’s more friendly to a consumer and so I think those are two areas we’ve done an amazing job with.
The areas of concern that I have…the areas of concern that I’ve had and I’ve voiced these with you in the past, but I think the two areas of concern is predictive outcomes on the credit. We, as an industry need to continue to focus on the predictive outcomes of credit because that’s where not just the consumer confidence but our investor confidence comes in play and their willingness to continue to sponsor this industry, this fledgling industry that’s really starting to blossom.
We need to make sure that there’s a lot of confidence. You saw that being evident in the fact that premiums have started to dry up starting in 2016 because they weren’t getting the kind of outcomes they were looking for and you see that on even some of the Kroll ratings that you look at. You can see where they said they were going to come at and where they actually came in at. So what we really need to do is continue to focus on telling a story and getting quality credit.
I think the second part to it for me is as we’ve become bigger and larger as an industry, more and more focus and every day there’s another article about how the CFPB or a local attorney general is looking at this space. We need to be in the forefront of education and just make sure that we’re guiding the process, that the lawmakers are understanding the real story and how we’re making credit acceptable and how we’re growing the economy and what the benefits are for our services being available and help guide the process because at the end of the day, this is not the Wild West.
We want it to grow, flourish and blossom in such a way that allows us to be a sustainable industry for years to come, but I think it’s going to be that education process to our lawmakers that are going to allow that to happen.
Peter: Right, right. I completely agree, we need to do more and better in that area. Last question before I let you go, so what are you working on specifically at LendingPoint, what’s the future looking like over the next 12 to 18 months?
Tom: I think the next 12 to 18 months is very exciting. As I look out to this, to the next year, year and a half, what I’m seeing is an opportunity to make money as accessible on a term loan as it is by using your credit card. This should be money on demand, this should be as easy as me using my cell phone, doing a transaction at the point of sale and being able to pay for that transaction in the same way I might with my credit card.
The fact that we can turn an authorization back to a customer in 5, 5.5 seconds makes it as usable as a credit card, we need to make it more and more accessible at the point of sale so if somebody is buying a large ticket, they have the same ability to use their phone as they would a credit card to get a term loan instantaneous.
Peter: Yeah, that makes sense. Okay, we’ve run out of time, but I really appreciate you coming on the show today, Tom.
Tom: Thank you, Peter, I appreciate you taking the time to learn more about LendingPoint.
Peter: Okay, will see you later.
Tom: Alright, bye.
I just want to pick up on that last thread that Tom was talking about there when it comes to point of sale, I’m very bullish on this space. I think it’s got tremendous potential, particularly over the long term. I think right now we’re still very much a credit card focused and cash based, to some extent ecosystem when it comes to point of sale. I see that changing certainly over the next ten years where it’s going to become a mobile-centric experience. You just have to look at China where the vast majority of retail sales now are done over a mobile phone and that will come to…I think it’s inevitable it comes to developed economies as well, including the US.
When that happens, I think you’re going to see an approach here where you’ll certainly have credit cards, they’ll be around for a long, long time and you’ll be able to put something on a credit card, whether it’s through Apple Pay or Android Pay or some other pay system, that will be around for decades, but I think you’ll also see a seamless integration of lending platforms into this experience where you’ll be able to go up to the counter and buy a couch and put it on a loan from a point of sale provider just as easily as you can put it on a credit card. We’re not there yet, but that is coming and when that actually arrives, I think you’ll see an explosion in the amount of volume that goes through these online lenders at the point of sale.
Anyway on that note, I will sign off. I very much appreciate you listening and I’ll catch you next time. Bye.
Today’s show was sponsored by LendIt Fintech Europe 2018, Europe’s leading event for innovation in financial services. It’s happening November 19th and 20th at the Business Design Centre in London. Registration is now open as well as speaker applications. Find out more by going to lendit.com/europe.[/expand]
You can subscribe to the Lend Academy Podcast via iTunes or Stitcher. To listen to this podcast episode there is an audio player directly below or you can download the MP3 file here.
Peter Renton is the chairman and co-founder of Fintech Nexus, the world’s first and largest digital media and events company focused on fintech. Peter has been writing about fintech since 2010 and he is the author and creator of the Fintech One-on-One Podcast, the first and longest-running fintech interview series. Peter has been interviewed by the Wall Street Journal, Bloomberg, The New York Times, CNBC, CNN, Fortune, NPR, Fox Business News, the Financial Times, and dozens of other publications.