Business Breakdowns
· S0
[00:00:00] S0: I've actually heard Max say that the credit card has been the best form factor for payment, and he couldn't really imagine that being disrupted, but obviously he's working on doing that. And his background is in cryptography and anti-fraud at PayPal, so he spent a lot of time thinking about quantitative ways to underwrite. Maybe walk us through some of the ways that Affirm is approaching underwriting that traditional credit cards haven't been able to build up.
· Fraser
[00:00:00] S1: The clearest and biggest differentiator is that they're underwriting transaction by transaction. If you imagine a credit card, you're approved for a general-purpose line of credit — maybe it's a few thousand dollars — and you're told, "Good luck, don't spend it all at once," and you're left to figure it out on your own. Affirm is doing every single transaction uniquely, whether that is interest rates are changing, the economy is changing, the type of purchase you're making. They're always feeding that through their models to adjust who they're lending to, how much they're lending to, and what rates they're lending at. And so that's the core structural difference. When you then go a layer deeper, the business has talked a lot about the use of AI and transformer-based underwriting models, particularly at their recent Analyst Day. And so they're basically seeing, every time they release a new underwriting model — which is on a, call it, six-, seven-month cadence — they're getting materially better underwriting outcomes. And this is as they load more and more data into those models to train over time. And so what you're seeing is the core model of underwriting on a transaction basis gives you a step-function better outcome. And then also Affirm's sophistication in the team and the models they're building gives them another step-function change in underwriting outcomes. For context, when we compare credit or underwriting metrics versus businesses like Synchrony, Affirm gets better outcomes, and that's reasonably material when you think Synchrony would talk about having 75% of its customer base being prime, whereas Affirm would talk about, call it, 50% of their customer base being prime. So despite the differences in the customer base, the Affirm process ends up in better underwriting outcomes.
· S0
[00:00:00] S0: It's an interesting segue, because I was going to ask about what the expansion opportunities would be from better underwriting. One of them is you can service more potential customers, whether that's prime or non-prime. So that's one. Are there other things, whether from a demographic perspective or otherwise, that being much better at underwriting opens up — different pools of merchants or consumers that maybe other peers or other card payments wouldn't want to touch?
· Fraser
[00:00:00] S1: It impacts the whole network. Merchants are happy because they're getting more sales — there's more consumers that browse the website that can actually transact. Consumers are happy because where maybe a credit card wasn't approving them for a limit, maybe a $100 loan for a very short repayment schedule can be approved by someone like Affirm. So they're also getting access to credit. And typically what we see when, again, Affirm is talking about their business is that frontier of: we can approve more of these people across more, let's call it, credit score bands and end up still with very, very strong repayment rates and very low delinquency levels, because they're optimizing the dollar amounts and the terms for each individual. I do want to highlight there is a big misconception that all of BNPL and all of these financial technology businesses that have credit aspects to them are generally lending to non-prime customer bases. Like the credit card industry and other traditional lending industries, it's the full spectrum. So yes, you get some players that are predominantly lending to subprime, but then someone like Affirm, as I mentioned — their average customer is actually generally prime, particularly when you look at the dollars they're lending rather than customer count. It's also just not young people. It actually skews perhaps older than you would think — ages 30 plus is the age bracket. And so the product isn't subprime lending to people that otherwise couldn't have access to credit. It's a much broader pool of the population. And I would re-highlight and double down on the statistic that half of US credit cardholders are sitting on revolving balances. And so there's this huge portion of the population that haven't necessarily been served by a great product best suited to them historically, and Affirm is targeting that part of the population. They're not targeting the American Express Platinum cardholder that wants points and airport lounges.
· S0
[00:00:00] S0: And maybe take a step back and set the stage on the actual business. So I want to spend some time on the economics and unit economics, because I think a lot of people, when they hear buy now, pay later, they may assume it's just that merchant discount rate where the merchant pays a fee, and that's the business, or you have these very simple no-interest, pay-in-X or pay-in-4 types of installments or options. But from reading and hearing more about the business, it's actually much more complicated and layered than that. And there are various products. I think there are four primary revenue streams for the business. So maybe you could walk us through the different sources of revenue, how they fit together, the volumes for each in terms of GMV.
· Fraser
[00:00:00] S1: It certainly is one of those businesses that can feel very complicated when you look at the P&L, but at its core, when you think about the biggest line items, it is reasonably simple. There are basically three types of offers that Affirm generates GMV from. So there's interest-bearing, the 0% loans, and then the Pay in X. And, let's call it in round numbers, 70% is in that interest-bearing category, and then the rest is evenly split between the 0% loans and the Pay in X. Pay in X and 0% loans look pretty similar from a P&L perspective, and then interest-bearing looks different. So I'll split it really into the two categories. So if you look at Pay in X, you're buying some jeans for $100 and you're agreeing to pay $25 over four installments. The primary way Affirm makes money there is on the MDR that we've talked about. So that's negotiated merchant by merchant. Big merchants get a smaller MDR; small merchants get a higher MDR. Interestingly, the MDRs on the Pay in X transactions are generally trending towards credit card rates over time and are almost there. The 0% loans — because the interest is being funded by the merchant, those MDRs are still very high. But that's typically where you see the big merchant revenue or network revenue coming from. The second pool of transactions, which is the 70% of GMV, is the interest-bearing loans. This is a different economic structure, because the MDRs are actually very small here, much smaller than the Pay in X or the 0% APR loans. The vast majority of the economics are coming through the interest income that the consumer is paying to Affirm. And so that's the way to think about the revenue. Pay in X is mostly coming through merchant revenue. Interest-bearing loans is mostly coming through interest income. And then the way to think about — well, how profitable are these products? And Affirm discloses this in their recent investor forum. Those Pay in X, those shorter-term, smaller-dollar-value loans, generate RLTC margins, or gross profit margins, of about 1 to 3% of GMV, whereas the interest-bearing loans generate a gross profit margin of 4 to 6% of GMV. So you can see the smaller ones, 1 to 3%; interest-bearing, larger value, 4 to 6% of GMV.