How LendingPoint CEO Tom Burnside grew LendingPoint 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...