Beforepay Group Limited (B4P.AX) Earnings Call Transcript & Summary

Good morning, and welcome to the Beforepay Full Year '25 Investor Webinar. My name is Danny Younis, and I help with Investor Relations for Beforepay. With me this morning, we have the CEO of Beforepay, Jamie Twiss; the CFO, Laavanya Pari; and the Head of Finance, Shreya Prakash. Before I hand over to Jamie, just to note that we will be having a Q&A session at the end. [Operator Instructions] I would now like to hand the webinar over to Jamie. Please go ahead.

Well, thank you, Danny, and good morning, everybody. Thank you all for joining us as we release our full year results. I'm delighted to be introducing Laavanya Pari, our new Chief Financial Officer; and as Danny noted, also joined by Shreya Prakash, our Head of Finance, who was previously our interim CFO and helped to produce these numbers. I'll cover off a few of the highlights to start, and there are a few highlights to cover off. Laavanya and Shreya will then go through some of the more detailed numbers, and then I'll wrap up as we head into questions. And as Danny noted, you can ask questions at any time during the webinar. So just to start off, this was a very, very strong year for us. I am really pleased with how FY '25 finished up. I think, obviously, we'll work through the numbers, and you'll see some very, very strong outcomes there. I think what we saw over the course of the past year was all the work that has been occurring over the last 6 years really falling into place. So I think pieces of work that got started quite some time ago in terms of how we think about the risk modeling, in terms of the product, all that grind and effort and all that attention to detail, we're really seeing that come good and drive business forward. So we're really pleased with these results. And even more so, I'm pleased that they rest on a very solid foundation of the capabilities that we have, and I think we are very, very well set up for the future. So really pleased with where we ended up overall. I think just to kind of recap for anyone who is new in terms of who we are and what we do. So as many of you already know, our business has 2 sides to it. The overall company is Beforepay Group. And within that, we have Beforepay and that's our mission-driven lending business in Australia. And then we have Carrington Labs, which is our enterprise risk analytics business, which operates globally. Again, as many of you know, Beforepay, our lending business, that's the heritage of the company. That's still very much the core in terms of what's driving the financials today. And the original vision behind the lending business, which continues to be the vision today, is to provide a safe and affordable alternative to a high-cost or unsafe lending practices with a Pay Advance product, our flagship product that is safe and affordable for customers, priced in a way that is both helpful for them and also protects them. We also offer the Tax Refund Advance product through our partner, H&R Block, and we have our larger and longer-duration personal loans as well. The core of this business is, of course, that very strong risk management capability, which is also what drives Carrington Labs as well as that very high level of automation that comes. We are a genuine digital-first cloud-native business. We write tens of thousands of loans every week with a very small team because the vast majority of our loans go through no human intervention. So that's our lending business, and again, that continues to be the core of the financial numbers you're about to see today. We're also, as many of you know, extremely excited about our Carrington Labs business. That's our enterprise risk analytics business. And what that does is that takes that core IP around risk management. It puts it in a product wrapper, and it offers it to other lenders to take advantage of that pretty distinctive capability that we've got, primarily overseas. The client base is very heavily weighted towards the United States. You can see here on this page the core offerings that we have. And essentially, the way to think about these is these range from very deep, bespoke, customized models for lenders with significant scale and large data sets, so they can get some of that same pretty distinctive predictive power that we've built for the Beforepay business but bespoke to their market, their lending experience, their product economics, customers through to lighter touch scores that can be quickly stood up, especially for smaller lenders or those with less data. And I'll talk more about Carrington Labs, but it's been a very transformative year for Carrington Labs. And we are really excited about where that business is going. To briefly cover off just a few highlights, and I would love to run through all of these, but I would steal thunder from Laavanya and Shreya. So I'll start by just covering a couple of things on this page. The first and most obvious one is the very strong profit outcome. So $6.7 million in net profit after tax. That's up 74% year-on-year. Really, everything contributed to that, obviously top line growth but as well very strong margin outcomes driven by a really step change, I think, in our risk management capabilities and the way that we do that heavy data-driven analytics around credit, around loan limit configuration. So the profit outcome was a really remarkable one. And again, I think I'm most pleased that, that sits on a very solid foundation of capabilities and assets in terms of the customer base that we've got, the position that we've got in Australia. I won't speak to the other numbers at this point because we're about to go through them in more detail. The other thing I would mention in terms of highlight from this year, which isn't on the page, is the progress of Carrington Labs. So as those of you that have followed our story know, there's been a flurry of announcements of new clients and partners in recent months. I think the growth in that commercial activity, of course, speaks for itself. And as I've said before, that rests on quite a strong pipeline and a high level of interest. I think we continue to find that the Carrington Labs capability is globally distinctive. We are -- what we do is difficult, and we do it well. And while we're never complacent about competition, we see very few other businesses even trying to build that capability, and I think we genuinely have quite a bit of white space in that market right now. So it's been a tremendously strong year commercially for Carrington Labs in terms of the growth, in terms of the pipeline. And then the other thing which is probably less visible from the outside is, I think, we've really sharpened up the proposition as to exactly what that product is and how we've taken that pretty significant capability and placed that out to the market and offered to clients. So we really kind of sharpened that up. I think we've got a very clear go-to-market strategy at this point and then just building the underlying infrastructure and assets from our side so that clients can easily integrate with us, work with us, and we can train models for them in a bespoke way, in a highly automated fashion. So there's been a lot of progress both in that kind of top line commercial momentum side but also in terms of the product, the product capability, how we define that and then how we deliver that as well. It's been really transformative over FY '25 as well. With that I'll hand over to Laavanya to talk to some numbers.

Absolutely. So what we can see is that our total advances have actually increased by 14% with our default rate decreasing to 1.1%. And what that's translated to is a net transaction margin, which has continued to improve over the half years. So we're seeing that improvement -- if we jump into the next one, we see that improvement resulting in the margin continuing to increase, while our operating expenses have flattened over time. So we've really been able to optimize the business, improve and continue to grow without having that impact our operating expenses and really sharpening that net profit that Jamie talked about earlier. So all of this has translated to that improved net profit number. So with our total advances going up by 17%, and really, it's a seasonal business, so the half year that you are comparing to is H2 FY '24, you can see that 17% improvement in total advances. However, our actual average advance has stayed pretty flat, so a 3.4% increase, which is quite small. So really, that's -- what that's telling us is that the volume of our advances have really increased. So if we jump into the next one, so you can see our active users. So that's really translated into an increase in our active users. So that's gone up by 12% compared to H2 '24. So that's really where a lot of the improvement in the revenue has come from. So they're increasing the active users rather than actually an increasing advance going -- average advance going out to customers. And at the same time, as you saw in that first slide, our loan defaults continue to flatten. So we're down to 1.1% for FY '25, which is down from 1.4% in FY '24. So it's continuing -- you can see that line just continuing to flatten out, both from a gross defaults and a net default perspective. And of course, it's a seasonal business, so that obviously continues to flatten. We continue to optimize our models. I'll just hand it over to Shreya.

Thanks, Laavanya, and hello, everyone. Just to continue what Laavanya was saying as well, other contributors to net transaction margin include funding costs as well as direct service costs. I'll talk through funding costs first. Assuming 26 days as our average loan duration, which is typically where it tends to be as well as a 15.31% in cost of external debt facility and a maximum loan-to-value ratio of 80%, our effective third-party funding cost is around 87 basis points. Now our actual cost is in fact a lot lower, and that's really a function of how we utilize the debt facility. What this shows is our cost of funding is, in fact, quite efficient relative to the size and nature of our product. And in addition to this, we also paid down a portion of our debt and set up a $7.5 million revolving supplement within our existing facility. And that really gives us more flexibility with our funding, allowing us to deploy excess cash when available to grow the loan book. Moving on to direct service costs. What we see here is our direct service costs are really costs to facilitate the advance, including things like KYC checks, bank transaction scraping, direct debits or credits, et cetera. And over the past 3 years, we've seen a consistent decline in these costs going from about $1.80 in the first half of FY '22 all the way to just $0.73 in half 2 FY '25. So what we see is that with increasing volumes, we've, in fact, been able to spread our fixed costs instead of having them increase. In the next couple of slides, I'll keep it quite brief just focusing on key metrics. So as Laavanya and Jamie mentioned, our net transaction margin for the year was $25 million. Looking at that a different way. The group charges 5% for our advances, and that's our revenue. When we deduct our net defaults of 1.1%, our funding costs of 0.6% and direct costs of 0.2%, that gives us a net transaction margin percentage of 3.1%. Once we deduct our operating expenses, we have a profit for the year of $6.7 million, and that compares to $3.9 million in the previous year, which is a 74% increase. The business continues to rest on a strong balance sheet. We had assets of $76 million and an equity position of $39.3 million. On the debt facility, as at 30th of June, we had drawn $30.9 million and had an undrawn balance of $24.1 million. Moving on to cash flows. We had positive net operating cash flows of $4.9 million for the year compared to negative $4 million in the previous year. And we ended the year with a cash at bank balance of $14 million, and that excludes $5.2 million in our third-party settlement accounts as well. So the total cash balance for FY '25 was, in fact, $19.2 million.

And it's worth noting that the decrease in cash from FY '24 to '25 reflects the repayment of $7.5 million in debt with the deduction of our [indiscernible]. So thank you, Shreya, and thank you, Laavanya. So coming back to where to from here. So we've had a really remarkable and very strong year again. When we close the books on the year, we high five. We go out for drinks and then we think, okay, how do we deliver an even better FY '26 and where to from here. So looking forward, I think if I look at our business across the 3 different things that we do Pay Advance, include Tax Refund Advance, Personal Loans and Carrington Labs, on the Pay Advance side, that business is working well. I think we understand how to deliver that product in a responsible and positive unit economic way. We will continue to optimize it. There is always so much more that we can do. We look forward to continuing to grow that. We look forward to continue to invest in the risk models, in particular. We are still finding so many things we can do to get better at risk. I think that's something I think is really baked into the DNA of our company and doing that in a very cost-efficient way. As you've seen, we genuinely deliver the pay advances with a fixed cost model, and as a result, we think that while we will continue to invest in the overall business, particularly with the new initiatives, we can continue to get good very strong unit economics out of Pay Advance while we look to continue to grow it. I think just to put a bit of an advanced notice on this, I think as we think more about the economics of the Pay Advance business, we do talk about that active user number. What we're finding is the economics are really driven by sort of upper third or so of that future base. So one of the things we're thinking about more is that value per user number. And so we're probably less focused on the smaller dollar end of that base, the active user number, which is heavily influenced by that as well. So we are thinking about that whole value approach to the Pay Advance business and probably less about that headline user number over time. On the Personal Loan side, so we launched this last year, and we've had a period where we've tested it and gotten it operationally robust and worked through the risk, reflecting data on performance. I think FY '26 is going to be our year of personal loans. We think that we're going to shortly be in a position to start growing that business more. And I think when we look at the size and scale of the Personal Loan business in Australia, that's a very significant market. It's got attractive economics. And we think we have a very strong right to play and win in that space. We know a very large portion of people taking out personal loans in Australia already. As you would have seen on that highlight slide, we have more than 1.7 million registered accounts. And we see a significant portion of this volume already through the transaction data that we collect. So we think that we have good access to that market. And then we are very strong at risk. We think that we are in an excellent position to put in place compelling offers based on our sharp understanding of customer risk behaviors. So Personal Loans, I think FY '26 is absolutely going to be our year. And then Carrington Labs continues to go from strength to strength. The challenge that we've had, as I've said a number of times, is the pipeline is, if anything, almost too full. And the thing that holds us back there is mostly just the decision-making and documentation time lines of our prospective clients, which can be lengthy, especially for banking clients and other heavily regulated lenders. But that pipeline looks very strong. And again, I never want to -- it's hard to predict when any particular opportunity will come out the other end, and some of the opportunities may be announceable and some may not. But we continue to feel very, very confident about this business. So I think we will continue to see good momentum there. I think the heavy lifting that we've done on the delivery of the product and that kind of sharp product proposition and of course, there will always be more work to do, but I think we have set ourselves as well in that product as well. So if I think about the year we had in FY '25, I mean, that was self-evidently a fantastic year for us, and I'm really pleased with that. We are now very focused on FY '26 and delivering a fantastic outcome there. I think across all 3 lines of our business, we are well set up to do that. So thank you very much. And with that, we're happy to move to your questions.

Thank you, Jamie. We will now move to the Q&A session. [Operator Instructions] Okay. We've got a couple of questions from Larry Gandler at Shaw and Partners. They're both financial questions. The first question is maybe a little bit of clarity around the movement in the other receivables, please.

Yes. So by other receivables, Larry, I assume you mean the noncustomer receivables. So in the receivables line, we have, I want to say it's 58, of which 53 is the gross customer advance book and 48 is the net. And then there's about 5 and change of other receivables. That's mostly our cash that sits in a third-party account that we -- from an accounting standpoint of view, we don't count as cash, but we do include it in total cash. So when you hear us talk about that total cash number, it's a non-GAAP noninterest term. Usually, the difference between that and cash at bank will be at other receivables. It mostly sits in that third-party settlement account, which is the account that we use. That's where money is sitting, waiting to go out to customers when we generate a payment instruction upon a new personal loan or pay advance being issued. And we also do have some in a separate bank, which is used to true up the balances, the net balances we have with our lenders, reflecting the advance rate of that facility as well.

Thank you. The next question from Larry is there...

Can move around at any given point in time. This is just what happens to be going on at that moment and when we take that snapshot for quarter end and year-end.

Yes. Thank you, Jamie. The next question from Larry is around how you calculate cash NPAT. So there's a little few moving parts here. So the cash NPAT of $12.2 million, is it as follows: you take the $44.1 million in receipts from income, you subtract payments to suppliers of $18 million, you subtract net interest of $4.7 million and you subtract loans written off of $9.2 million to get to that $12.2 million? Can you maybe clarify that, please?

So did we publish at $12.2 million or is that the EBITDA number?

That's an EBITDA number.

Yes. Shreya, do you have any comment on that?

No, I think Larry is on the right track in that, yes, it is indeed receipts less operating expenses, so that includes payment to our suppliers, employee costs, et cetera, as well as other nonoperating expenses, and that really gives us that $12 million in EBITDA.

Great. Okay. The next question is around Carrington Labs. Can you maybe talk to Carrington Labs' revenue profile in the future and what the pipeline looks like?

Yes. So let me just start by talking a little bit about how we charge clients. And of course, these are -- can be quite large enterprise contracts, so things may vary and often there's a negotiation into this. But this is a Software-as-a-Service, a SaaS product. So we primarily charge off the back of usage. So -- and again, there may be custom setups for various reasons, but essentially, you're our client, we have trained a model on your data. We then are hosting that model in the cloud. And when you say you're using it for a loan application recommendation, you will call our API. You will send data to us. We will run that and then send that back. And essentially, the pricing model is we will charge you a certain amount of money for each one of those decisions. There are other uses that we may do ongoing portfolio monitoring. That's charged on a per user per month kind of basis. We do often charge implementation fees. We may charge proof-of-concept fees depending on the nature of that relationship. We generally look to have some sort of monthly minimum in place, reflecting what we think the volume will be, but just to make sure that actually the usage levels land where we think they will be. But essentially, it's that volume-based charging. I think in terms of the revenue profile, so it's always going to look quite sort of -- it's going to be quite lumpy or maybe a bit of a staircase look because these are individual clients, and some will be midsized. Some could be very large indeed. And so it's a bit hard. I think with the Pay Advance business, if you look at the revenue over time, it's a very smooth straight line, and this will be more of that staircase pattern. And so it's a bit -- when clients sign and when clients tend to turn go live, which again is more a reflection of their internal documentation processes and approvals and technical implementation, that's a bit unpredictable. But we do expect that -- based on the health of the pipeline, we do expect that there will be more continued good news in terms of -- again, some we'll announce. Some we won't. But that will flow through, obviously, to the revenue profile as those clients go live.

Okay. The next question is around the Personal Loans product. So can you maybe provide a little bit more clarity around the existing users and the volumes?

Yes. So the existing users are current Beforepay Pay Advance customer -- or sorry, for people who have already taken out a Pay Advance Beforepay and then repaid. And the reason we do that is we just understand those customers, their data and their risk very, very well. So we haven't yet opened it up externally. We haven't yet even opened it up to everybody internally. We're still looking at a subset of our user base. Look, I think in terms of volumes and numbers, the way we think about that to this point has been more along the lines of what's the number of loans that we should be issuing in order to get a better sense of the credit performance and to the extent to which elements and variables that are predictive for the Pay Advance are also predictive for Personal Loan. Obviously, we would like to really get that credit right before we start scaling. So the numbers are not especially high today, but in large part, that's because we haven't been driving personal loans with that direct commercial outcome in mind. We've been driving it with much more of a data collection outcome in mind. We'll see what happens when we turn it on more fully and start to scale that and indeed, when we start to look at longer durations and larger limits. We do expect it will be material to the overall group at full strength once it gets its momentum going.

Okay. [Operator Instructions] We've got 2 further questions. The first one is around the net defaults. So how do you balance the very low default rate against customer growth?

Yes. I -- so I'm going to interpret this question as an excuse for me to go down a little bit of a data science rabbit hole, which is something I look for in every results announcement. So I'd like to [indiscernible] whoever asked that and apologize to everybody else. So I'd answer that question in 2 parts. The first one is we try to say yes as much as we can to both existing and new users. So we are very much a mission-driven organization, and the goal of having this lending product out there is to give people a better alternative. So whenever we can issue an advance to somebody in a way that feels aligned with our values, which ultimately means it has to be helpful to them, so it has to be something we think that's going to leave them in a better place than if they [ had it ] and of course, it's sustainable for us. So the expected value of that is hopefully positive and if not tolerably small, we would like to do so. Now in order to be able to do that, we vary the size of those advances quite dramatically. So as many of you know, the maximum advance size is $2,000. The minimum is $50. So we will absolutely advance somebody $50. They will pay a flat fee of $2.50 for that. And I think, again, very much aligned with our mission but also in line with getting people in the door. And if they can demonstrate good repayment behaviors on that and check some others, then that limit may go up over time. So the reason I mentioned that is when we think about the default rate and customer growth, we generally find -- we don't turn away that many customers off the back of risk considerations. We turn a lot away on an eligibility basis. So either -- I mean, first of all, if you haven't completed the process, you haven't synced with your bank account, been through KYC and so on, obviously, you won't be able to use the product. We do have some eligibility around you have to be employed. The majority of your income has to come from employment. You have to be in Australia, over 18, all those sorts of things. We also do have some values-based eligibility rules as well around levels of gambling and levels of debt to make sure that, again, this product is helping. So many people get screened out in that eligibility stage. But the significant majority of people, if they are eligible, will receive something even if it's only that minimum $50 advance. So I don't think kind of risk settings have a huge impact on that customer growth number. Now as I said a little while ago, when we think about kind of the economics of the business and the momentum of the business, I think that overall customer growth numbers, that active user number, that feels less important actually than kind of that core of how many of those less risky customers who have the $500, $1,000 and $1,500 advances, like that's actually what really drives the economics of the business. And there, we think very explicitly about the trade-off between credit risk and essentially average advance size and revenue growth, net transaction margin growth, in particular. The way that we do that is we have -- we do a lot of quite sophisticated modeling around -- for each customer who's in front of us at this point in time, whether it's because they're coming in as a new customer or for existing customers, we'll just rescore them in the background every week for the most part and recalculate the limit. So if they come back in, we've got the limit ready to go. We will look at their expected elasticity of default, by which I mean, if we give them a small advance of $50, what do we expect their default rate would be based on the statistical modeling that we do or all the way through kind of the other end, those very large advances. And generally, we find that that's monotonically increasing. So the larger the advance, the higher the default rate. And then when we map the marginal unit economics on top of that, the variable contribution, we usually find usually sort of kind of an arc like this, where at the small end, low default rates but not very much revenue. At the big end, lots of revenue but high default rates, and somewhere in the middle will be that global maximum and subject to kind of our values, constraints, some of our servicing rules and other things, that's the limit they will get. In general, as the model becomes more powerful, we will, therefore, take some of that benefit in -- we should take some of that benefit in the form of lower defaults and some of it in the form of higher limits. I think if you look back over the last year, I think our risk modeling has significantly outperformed our expectations in terms of the quality of power. And so as you can see from what Laavanya was saying earlier, the net default rate fell quite substantially and the average advance size stayed roughly constant. So if I look forward, I would say we should probably -- we probably actually -- we performed better than we thought we would, and as a result, we probably under loaned to many of those customers. And we look forward to FY '26. That's something we'll be taking a look at, is what is that balance between not so much customer numbers but more kind of that average advance size for those customers against net default rate. And we don't have a target in mind for either of those numbers. We target the highest possible variable contribution on each loan based on default risks.

Very comprehensive, Jamie, and thank you for that. There's actually been a follow-up question on that default rate. And I think you've answered the first part of the question around do you think the 1.1% default rate is too low. But the follow-up question is can you amend the Carrington Labs program to increase this.