How LendingPoint CEO Tom Burnside grew to $865.3M revenue and 300K customers in 2024.

hey folks my guest today is tom burnside he is leading lendingpoint.com an ai driven credit tech lending platform he sees the company as a way to do well and do good simultaneously by protecting nourishing and growing each consumer's financial future he does this with over 25 years of experience and a wealth of industry knowledge prior to landing point he's an accomplished credit and financial services leader and trusted data scientist tom leads the rest the team in serving their borrowers their originating financial institutions their merchants and other service providers while delivering predictable returns to their capital market providers tom you're ready to take us to the top we are all right so what gave you this idea i think back in 20 was it 2013 the start date well it was 2014 the end of 2014 really started funding in 2015. um yeah what gave us an opportunity is what we were looking at was a marketplace that was serving uh some of the credit bands well uh really kind of the the assets that other banks and otherwise would buy uh what we saw was an opportunity for kind of the more challenged credits to start there understand that do it really well through ai and then continue to broaden our funnel uh and so today you know today we fund all you know all credit bands from you know from 550 all the way up to 8.50 but we started in that really that's 660 and under space just so we could try to understand them give them a reasonable price and a reasonable product and tell their story in a way that nobody else was telling it so these are folks if you're listening and you're doing 50 000 a year in annual revenue and you want to take out a what a five thousand dollar loan tom something like that they can check out your offer yeah five two you know five thousand now that that market goes all the way up to fifty thousand so as we got better in the marketing and the better on the understanding of the customer we've been able to expand the offers what what did you sorry what did you start with though what was your initial sort of target offer size in 2014 it was about 5 000. it was about 5000 very beginning start at 5000. so that was your sort of thesis and then it scaled from there i guess take me take me back to one of those early deals so i'm a consumer i have a great you said a credit score above what so typically in the early days that credit score would be around 620 625 maybe in that area okay and then they weren't well it was either somebody that was likely you know had light credit footprint right they were just getting established and nobody could really kind of put all the other kind of api and data information together to be able to tell their story so there's a lot of other things that you would look at if outside just credit you might look at phone bills you might look at rent history you might look at some other things to tell the story of their willingness to pay right or their ability to pay and so those are the things we you know we really focused on we focused on i mean one of the problems you always have is fraud and we focus a lot on kyc know your customer uh and we were able to get a very predictive outcome on those particular scores so you know this was typically somebody that was either on the way back up you know i had gone through a dip or just had a very light credit footprint and today and even back then in your pro forma is when you're you know building in sort of a charge off or a losses or bad debt expense is this two percent three percent four percent what do you build in as a buffer well you know really what you're doing is the ai models have done an amazing job of predicting risk uh and so you know what so the way that the ai models work today is it predicts risk which in a category uh and then tells me okay basically here's what the risk is going to be but then pricing is the next kind of uh optimization tool that we use and we have about five different buckets of credit grades at risk right and we but we now are up to 400 different pricing points within side of those grids so we are getting really really good at giving you the right product at the right time at the right place with the right terms and conditions that you can understand how affordable it is for you to finish a project or or to to you know to resolve some consolidation of bills or whatever it is that you need to do oh what's going on there youtube good to see you guys now imagine this you love watching these interviews with sas founders but imagine if we took all of the valuation data out from over 2807 interviews i've done manually saves you a lot of time well we've done this we've built it into the beautiful interface inside of founder path check this out i'll show you how you can access this in a second but you log in you connect your stripe account you see your valuation real time you can see what it changed over the past 88 days and even set goals for valuation this year now the secret evaluation 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all right let's jump back into the interview so let's go stay in 2014 before because you've had a lot of growth let's stay in 2014 though for another minute or two i take i'm one of your first customers i have a 650 score 700. i go ahead and take 5k what am i going to pay you back over what term uh total so is it 5500 over six months or what's the term look like yeah typically the average price factor back in those days was about 20 to 23 percent uh uh know uh weighted average uh coupon if you think about it in that particular way um and it was typically over three to four years is what we were doing oh that's a long payback period that's a long time to pay back oh absolutely yeah even back then the models were doing a really good job of predicting something typically what happens in this this is why we saw the opportunity in this particular area you saw a lot of very very short transactions 16 12 months and we saw an opportunity to give somebody something that was very affordable and so we didn't want to go below 24 months and we were able to get those out as long as 48 months even back in the day by telling by by taking the ai information and doing a better job of telling the story and therefore giving them something that was affordable because one of the problems if you're paying back five thousand dollars over 12 months it's a very expensive payment when you start to elongate that out it becomes very affordable and you give them the opportunity to get back on their feet and be able to pay back and now you know most of those customers have come back to renew with us or come back to take another loan with us you know we now have about 30 percent of the base is is in a renewal status with us because we made it affordable they were able to pay it back and they're able to take more money just to be clear if i take that 5k from you in 2014 and i pay back worth three to four years my total interest on that 5k over four years is 1200 right that's 23 ish or is it 25 per year no that's roughly that's roughly about 23 over that period of time i see i see interesting okay yeah so it wasn't based on typically what you see in this market is it is a discount rate or you see a percentage that's not we did we actually use a an interest rate and if we were charging 23 percent it's 23 a year based on the outstanding balance which averages out to about 20 you know 23 to 30 percent over the life of the of the of three years okay got it so i mean i mean can we basically say that that's effectively an 8 apr or interest rate um no you would want to think about it as an interest rate is a 20 year it's a 23 interest rate right so yeah it's not really a discount it's it's just a simple interest rate like you pay for a car or a house or you know anything else that you do if you take some of these discount rates uh you know they could be upwards over 100 interest and so we think that we're given the best deals in the marketplace uh hands down yeah we're and i would i would agree with that based off comps i know but talking about the pure interest rate makes you seem really bad uh be because it makes anyone seem bad because the numbers are scary but because you give people just a long like i get i totally get this you give people a long time to pay back it it makes it sound a little bit better but the reason i'm asking these questions is you were able to secure to lend you have to secure money to lend right you secured 100 million basically on day one so i believe so how did you do that well look i mean some of this was based on track record so we were able to get better pricing on the facilities uh that we that we borrow from for ourselves to be able to make that money available to our customer so we were able to get uh you know some pretty good rates back in the day that rates of those rates have come down a lot right over the last few years what was what was good back in the day what was good back in the day we we started off at around the ten percent cost of funds are on our own that's not a lot were there warrants involved there wasn't there was a few warrants in there like we i think we gave up uh one percent in the first year to get to get some of the good deals that's that's not bad not too bad not too bad we didn't feel too bad about it but the the good news the customer is able to get you know because we were able to save money we were able to push that forward to the customer no that's great i mean that's exactly why you want to negotiate obviously that low cost capital now was your credit box really tight right did they really restrict you what states you could lend to scores above you know 650 or above or did you have enough flexibility to actually deploy that capital yeah we had enough uh flexibility i mean obviously being in this market for quite a while what we did is we did it we really used more of our own equity so our advance rates were a little bit lower so our box was wider so we could test more so we were able to go right out the market and test it in the best of ways we did most of our own balance sheet to start with just so we could prove the concept and the model worked so time to be clear you raised equity on day one and you were using that capital to test to test the market how much on day one do you remember well yeah so the what's interesting the gripper team uh the team we have here raised about 220 million dollars of friends and family uh just really no outside rounds but uh friends and family we raised that over you know over about probably four or five different tranches that we re-raised it but we didn't really consider them eight rounds b rounds they were friends and family coming to the table uh we had other deals that we had done together that worked out well and so it was relatively easy to raise that uh our first institutional raise didn't actually happen until 2020. well that's why i'm asking so that to to that 220 like that capital you starting your balance sheet on day one was that like a prom note on the operating company or was it actual equity that your friends and family they put money in for equity in the business combination of those some some of just pure equity some of it was uh you know more mezzanine type structure right it's a little combination of both but it allowed us to use that money to leverage to be able to get us uh you know access to capital uh be able to to to test our models and and uh you know make sure that our models were as predictive as we were hoping they were going to be and they you know they became that uh over a short period of time and therefore then the cost of capital continues to go down as your you know as your performance the models are better it's magic that way right there's a lot of fintech entrepreneurs listening to this interview and they're all wondering how much loan tape and vintage history do they have to build to drive their costs from twelve percent and one percent warrants down you know eight percent or bringing a bank on top of the credit fund or whatever to drive the blended down what did you back then have to grow your loan tape to to get significant savings below that 10 cost to capital on your first 100 million you know that's a great question um i think there's a couple things the weighted average life of an asset even though you write it for 24 or 36 months you know people end up paying it off in 18 months or they paid off in 16 months so you know you you know within about a year a year and a half you have a pretty good idea of how the how the curves are going to work uh because most your losses are really front-ended in the first six months you'll see about 60 of your losses on a vintage analysis curve so it's relatively easy once you get past six months they can kind of predict the rest of your curve and and so a year into it you've got a couple turns of products um but you know it really was until we got over call it 100 million dollars of of of transactions until they said look you got enough scale enough predictability in a couple turns of the product that we feel comfortable in giving you better you know better pricing and better advanced rates okay this makes sense and let me a couple things i want to pull out here because it's great for consumers listening first off if you don't charge pre-payment penalties which is great right you let people pay off early if they want that's right it's a very flexible capital product that's fantastic no hidden fees um talk to me about scale that first year so just total amount of loans done in 2014 do you remember yeah it was about 15 million dollars uh it wasn't a lot uh it wasn't a lot but it felt like a lot back then though it sure didn't uh and we learned a lot i was going to say i mean we can sort of calculate right if you had 15 out you gave us 23 minus 10 that's 100 what is that you know 13 points of spread right that's nice it's good business so you go okay we're on to something what's next well then you have these little things called losses right so you have the cost of capital but you also have losses your second largest component uh so it was a little it was a lean year uh you know but it was a great year of learning a great year of kind of understanding how our credit models were going to perform and you know what we needed to augment them to you know to get the losses in line with where we were hoping them to be i love this okay so that's a great this guy this is your one you just heard it year one now let's go fast forward tom 2018. how much how much total capital raised over the prior for sorry lent over the prior four years so i mean we got uh you know what's interesting about it is we hit about 360 or so uh you know so things were starting to grow the 360 million in loans in 2018 yeah the capacity is starting to grow now you know we're we're starting to get in a great place um uh the models are performing well we have a couple different lines of credit now at this point all of them have been upsized you know so now we have access to you know roughly i think at that particular point about 350 to 400 million dollars of capacity uh at that particular point in time so life is getting better costs of funds are coming down by a couple points so those spreads are widening uh which is always good it helps pay bills mm-hmm so i mean we're talking like you get down to seven percent in 2018 six percent something like that i was about eight percent it was about eight percent at that point right and it wasn't until you really cross over a half a billion dollars of originations a year until pricing really starts to come of real scale when did you hit that uh we hit that in 2019 okay that's happening yes wow okay that's fantastic i mean one of the problems a lot of folks have when they do these deals is they think they want to go raise a big warehouse facility the problem is you end up with unused fees if you can't attract customers and deploy it quickly it sounds like you just told me in 2018 you had 400 million capacity but you did 360 million that's very good optimization in terms of actually utilizing what you what you took down the facility size how did you plan that so so well well you know uh the the nice thing about growth is is um you know our models have really led our ai models from a marketing perspective have really led who we go after and how big that tam is or that total addressable market so we knew basically based on our efforts what we needed to do to grow the next 10 million next 20 million dollars a month right and so what you're really trying now to do is line up capital right the capital that you need to you know to to backstop that advance rate right and grow continue to grow the business and so you know that was all kind of coming together at that particular point uh and really limiting factor was capital it's just how much capital do you have on the books uh that was really more of your limiting factor uh more so than the capacity of the lines yeah very cool and then fast forward to date obviously we're in the middle of 2022 but what was 2021 total loans new loans done that year uh 20 2021 i mean it was a very interesting year we grew 144 year over year 20 to 21. what number in capital deploy yeah i'm sorry what number grew by that amount the capital deployed or the revenue the the the uh the funding levels the funding levels we'll talk about revenue here in just a second but the funding levels themselves so we grew we went from roughly um about you know we we went to uh to 2.1 billion and 21 uh which is a you know a huge uh you know huge growth we slowed down a little bit during covet in 2020 uh but then we had you know just significant sort of close a little under a billion then went to 2.1 billion so we had significant growth kind of year over year you know this year we're already starting out about 800 and about almost 900 million dollars just you've already done 900 million in q1 yes yeah it's it's growing quickly now that's incredible okay i didn't bring it up but you did talk to me about revenue so you know from a revenue perspective i think there's probably two pieces uh here in 2021 we were at about 330 million that's a run rate and and that's and that's net interest margin or does that before you pay out your your cost of capital this is basically this is our this is our net revenue right after you pay back your warehouse providers all that that's right wow 350 you said 330. so approximately 330 and we'll be around 600 million this year that's incredible when was your first million dollar year do you remember was that 2015 yeah it was 2015. yeah you got there pretty quick because i'm doing the spread on 15 million out right with with 13 points of spread you get there quick interesting um most most businesses like this have trouble scaling because for two reasons they have to fight yield compression right because more money will plow into the market right if it's a known known asset class right the second is your google ad expense goes up right you have to have tac arbitrage somehow so how have you fought both of these you know headwinds on both sides keep scaling so fast well look i think the question has always been in the fintech space uh is it scalable is it predictive right and is it sustainable can you grow it can you continue to grow it at the appropriate rates and and i think we've answered those questions uh you know i think you know first of all uh the biggest challenge you have is you have to be able to optimize your cost to acquire a customer and that really comes through your ability to renew a customer i mean we have an 86 net promoter score we work really hard on our customer to make sure our customer is is having a great experience because they have a great experience they come back they see you as kind of that trusted advisor so i mean right now about 25 to 30 percent of our base runs in renewals just renewals alone these are customers just right and how many by the way so how right now if you look at your loan tape how many customers have at least a dollar out with you uh we're a little over 300 right right at 300 uh uh and we've serviced about 450 in total wow so that's a very high renewal rate actually right i'd expect the number to be way higher if people weren't coming back but it's actually not a big number of people that's right and the average transaction today is about 11 000 right so it's moved from the 5 000 days to about 11 000 today and you still like it's still sort of three to four the average deal three to four year payback 23 ish well we bought a point of sale company and so things moved changed a little bit when we bought the point of sale we bought the point of cell company we were able to move to seven and ten year paper for home improvement so we're in the home improvement space and point of sale as well as medical uh and in those spaces you know you tend to go a little longer in turn on a direct-to-consumer type of product it's typically still around five years um uh is you're coming to the typical duration so this is really interesting i talked to a lot of sas founders that have built like a really interesting marketplace that that maybe connects um i'm gonna make this up uh a lumber provider of two by fours with construction workers and there's massive there's a massive audience on both sides and they sit in the middle and what they're doing now is sort of factoring the paper right on 30 day things if crime if i'm wrong you're you effectively understand that you know how exciting this is you might go buy that thing and you already know the finance side of the business so you'll set the middle and just start doing that in the in the construction space that's right interesting yeah i mean for an example if you look at our e-commerce space i mean we're we're setting uh you know we're sitting there to help the small business uh or the e-commerce uh business we're helping them uh buy inventory so they can they can deploy so yeah they run a sale they run low in inventory they use us as backstop to be able to fill back up the inventory mm-hmm as well as small business i'm just impressed you've been able to do this because there are competitors that only do one of these things that you're dealing with so like bild.com does this in vendor management construction obviously paylocity and clearco and the e-commerce space like all the areas you just mentioned there are billion dollar competitors so how are you winning how are you going into these markets and sort of building a better mousetrap um look we have an amazing team we really do the team has been around this uh around the space for a very long time we also have great connections into the financial markets so we've been able to to get the lines of credits the things we need to do in order to be able to provide uh the service we also you know we we operate you know our platform today services banks and credit unions and uh you know uh abs forward flows as well as our own balance sheet so you know we have a lot of optionality for our customer and we continue to grow the optionality to be able to make sure that we can aggressively price that we can give them the right product at the right time with the right amount of money is you know to to fulfill whatever it is that they're trying to do at that point i have a bunch of other questions on how you securitize and all that but unfortunately we're running low on time so i'll just simplify the question how much debt capacity do you have right now you're doing 900 million a quarter how much could you lend um you know we we we can easily get to a billion to a billion five with what we have right now it's a combination of warehouses abs forward flows you know bank commitments things of that sort that make that happen but uh you know we we know we can get to at least a billion five and the question really is in this market you know we we uh we're seeing some pullback from some of our competitors and i think you know it's an opportunity we have the right kind of uh product and the performance on portfolio i think we're going to continue to take advantage of the marketplace where it's at and and what you know better than anybody what are these kinds of companies getting valued at today is it a multiple like let's look at 20 21 is it a multiple on your 330 or do you get a multiple you know lower multiple on loans done 2.1 billion you know it's a challenge this is the challenge for for uh for evaluation right the challenge valuation is a very fast-growing company earnings lag so so typically these are discounted cash flow models and really looking more at a multiple of revenue uh typically a lot of times it's either forward or or you know post but a lot of times they're they're looking forward now because on a really fast growing uh revenue company they probably look more like in our case the 600 million dollars than they would look at the 330. um you know what i'm saying so but that is a valuation challenge because a fast growing company will continue to have massive scale and efficiencies of scale and growth and revenue that won't show up in earnings in the first year so you really kind of need to do it more as a discounted cash flow we see primarily over a five-year period or even a 10-year period to pick up the value of the of what you're creating so if you do this analysis last year what was what did you buy the business out if you're on a dcf model on it i mean it's got to be in the billion i mean definitely in the billions right you passed 5 billion so i'm going to hold that at this particular point because i got so much the flow is so good we're smiling we're laughing and then i hit him with evaluation and he he shuts down yeah here's the deal we're making money uh we'll we'll make 100 plus this year 100 million net yeah uh 100 120. um uh and we're one of the only ones that are growing at the rate we're growing and making that level of profit uh so you know we um we're gonna let the markets decide that at some point uh but uh you know we feel like we're in a really great spot we feel like uh you know the things are moving in the right direction the predictability the models continue to get better the ai's growing fast on both the you know both the credit but also on the on the on the pricing of the products uh and uh you know it's really accelerate our growth and we're we're pretty excited about where we're at let me just put this the 125 warborg put in more or less than five percent of the business uh yeah i i was you know warburg is an amazing partner this guy's a great politician over here they they they had look warburg that was the first institutional round that we've taken uh and they've been amazing partners like they they have been really really good um you know um i think we do well as a company so that helps the relationship they were dead first i imagine right were they one of your debt partners early on no well they weren't oh wow no no they didn't come in until two thousand but they came in during copenhagen so even during covet the models had uh really really really helped uh the beta risk was still low uh and they followed us for about six months and then jumped in um and so you have great great partners uh you know they put in a 175 million dollars now themselves uh into the company so uh how much was secondary uh none no i thought you're going to say all of i thought all of it would be secondary with you printing out 100 million in free cash flow why would it all be secondary well remember we continue to pour that money back into the business right now to continue to grow it you know at the pace we're growing you know you need to feed the proverbial beast right so that money keeps going back into the organization and we have taken no secondary uh we have poured all the money back into the company to keep the coffee ground well tom okay last question as we wrap up how do you keep early employees excited about an eventual payday you're not public there's no secondary options they've been with you since 2014 you maybe use options to recruit them when are they going to see money um we're going to let the market when the market's ready we'll be ready uh we you know look the uh we have an amazing team of people uh a lot of these people have worked with me at least two to three other companies um and and uh you know they they believe in kind of the overall dream in where we're going and i uh our success is squarely on their shoulders when did you hire...