Thank you, Danny, and thank you, everybody, for joining us today. As Danny said, I'm Jamie Twiss, the CEO of Beforepay Group Limited, and joined by our Chief Financial Officer, Laavanya Pari. I just want to start by apologizing for the somewhat nonstandard setup. As Danny mentioned, I am in the U.S. right now, and as a result, we're not able to both be in the same room together. Thank you again for joining us. Laavanya will take us through the numbers from the first quarter in just a bit, but I just wanted to start off by saying that we've had a number of strong quarters in a row now. This quarter, obviously, is a very remarkable quarter and a very special quarter in terms of the results that we delivered. The profit number that we've been able to put up this quarter, $3.4 million, is by far the strongest quarter we've had, and I think a real testament to the quality and the performance of the business. Indeed, a lot of the things that we've been working on for really the last few years are all really coming to fruition and delivering that really remarkable result. Just to pull back and talk about how we got to that really remarkable profit number in that quarter, I'd start by saying that, as many of you will remember from the full year, we talked about how we're thinking about the trade-off between user numbers, customer value, and focusing more and more on the overall top-line growth and revenue figures, focusing more on higher value, less risky customers, and focusing less on active users as a metric, just given the very wide range of values that we see in different customers. The results of that, I think, are very clear in this quarter, and indeed are what led to that $3.4 million profit number. Active users, obviously, slightly down because we've been focusing less on acquiring those lower value customers, and as a result of that, a very significant increase in the average advance size. Because those average advances have gone to, on average, the less risky, higher value customers, that's what we've seen driving that very significant top-line growth and that very, very significant profit outcome. I'm happy to talk more about how those different metrics interact and how that's also contributed to the very strong cost control that we saw this quarter as well. I think the key thing there is that that shift towards focusing more and more on customer value is a tremendous driver of that profit outcome. The other drivers, of course, being there is some seasonality in that. Q1 is traditionally a very good quarter from credit outcome, and that credit outcome is quite remarkable, that sub-0.7% net default rate, especially in light of that higher average advance size. Between that top-line growth, those higher value customer focus, and that good credit outcome, that's what's driven the very, very strong profit outcome. I'll hand it over now to Laavanya to take us through some of the specific numbers before talking about some of our growth initiatives after that. Laavanya, over to you. Thanks, Jamie. As Jamie said, a very solid result on advances has contributed to an increase in revenue to $11.3 million for the quarter, which is a 20% increase compared to the prior year. The net defaults, again, as Jamie said, a very, very impressive result at 0.67%, which has contributed to a net transaction margin increase of 19% up to $8.4 million. The operating expenses, again, very tight cost control, as well as lower digital media spend, which has given us an improvement of 12% compared to the prior year, giving us a very solid result when it comes to our net profit before tax, an improvement of 98%. That increase in revenue, the 19%, combined with the improvement in the operating expenses, has contributed to the very solid result, improvement on last year, improvement on also the prior quarter, which is a very, very impressive result for us. When you look at our balance sheet, again, very solid from our side. We've got a cash balance of $19.2 million, which is flat compared to the prior quarter. As per usual, we've got the $5.1 million as part of our cash balance, which is held and set aside. From a balance sheet perspective, again, very solid results. I'll hand it back over to Jamie to talk about our Carrington Labs business and our personal loans. Yeah, thanks, Laavanya. Starting with personal loans, we've put some numbers in this quarterly release noting that as of the end of September, we'd written almost 1,500 personal loans, totaling about $4 million. As we've said in previous quarters, obviously, without giving that level of specificity, we're very pleased with how that's going. I think the default rate is where we expected it to be and is such that we'll be able to make that a very successful product. We're looking forward to scaling that in due course. As I said at the full year, FY 2026 will be the year of the personal loan. We think there's quite a bit of upside for us there, and that's obviously a very large market. Very pleased with how that's going. That's just a quiet, steady performer, and we'll continue to roll that out. On the Carrington Labs side, we had another two announcements this quarter with Taran and Digifi. Very pleased to have both of those. I think with those two falling into place, we now have partnerships with a very significant portion of what I'd call the Carrington Labs adjacent loan decisioning, loan origination space. This has a lot of benefits in terms of enabling clients to integrate with us more easily and as a result for us to speed up those go-to-market times and those onboarding times. Really pleased with how we're going there. As always, we feel very confident about that business. I wouldn't be in the U.S. right now. We wouldn't be talking about it nearly as much if we didn't see very blue skies on the Carrington Labs side. When we have additional announcements, of course, we will make those when we have them. Very pleased with both growth initiatives. It's great to be able to reach that milestone of starting to put out personal loan numbers and report on the progress there as well. In terms of trading to date so far in Q2, I'd say it's as we expected. Q2 traditionally is where we start to see defaults tick up into the holiday season. Usually, that starts a little bit later in the quarter than we are now. Often early November is when you start to see the early signs of that. We haven't really seen that yet, but everything we've seen is broadly in line with the way we would expect it to be. We've had such good success with this focus on the higher value customers. I think we expect to continue that. While I'm not saying that the average advance size will necessarily continue to increase, I think that focus on maintaining that average advance size above $400, and to the extent that that means we have fewer of those higher risk, lower value customers, I think that's been working very well for us. We expect that the normal seasonal patterns will continue with that adjustment, and I think we've got a sharper way of thinking about value going forward. Just to finish where I began, again, really record-setting quarter for us. Great top-line growth, fantastic profitability, both in terms of unit economics, but particularly on that bottom line number of $3.4 million. Really pleased with all the things that we've been doing that have enabled that very strong result. As you'll note from the release, the year-on-year profit change is up 98%. I wish we'd been up 100%. I was hoping we'd find an extra few tens of thousands of dollars to get there. If we can keep growing at 98%, that would be a pretty satisfying outcome. Really pleased with that profit outcome. $3.4 million. It's worth noting that in FY 2024, the full year profit was only slightly higher than that. We've been able to do in one quarter what recently took us a full year to achieve in terms of profit outcomes. Very pleased where we're at. It's been a great quarter for the business, and I think we continue to be firing on all cylinders and very, very confident about the future across both that existing pay advance business and the growth initiatives that are underway. Thank you for that. With that, happy to take your questions. Thank you, Jamie. Thank you, Laavanya. As Jamie said, we'll now move to the Q&A session. Once again, if you have any questions, please type them into the Q&A box at the bottom of your screen. We do have several questions coming through. I see that Larry Gandler from Shaw and Partners had his hand up to be allowed to talk. I'll allow Larry to put you on if you have any questions to ask. I do, Danny. Thank you very much. Jamie and Laavanya, congrats on a fantastic result. Jamie, I was cheering for the Mariners the whole way, but disappointing news there. It was an anomalous pitch. I'm sure you can appreciate that. Just in terms of this quarter, one of the things that I like to draw investors' attention to and your attention to is your loan facilities, which is line 7.1 in the 4C, stayed exactly the same, the amount drawn at $30.9 million, ending fourth quarter and ending first quarter. In other words, you didn't draw down a cent from your loan facilities to fund that quarter. The business looks to be self-funding. Any sort of comments there? Is that sort of the correct observation? Yes, that is the correct observation. First of all, thank you for your condolences for everybody online. Larry is talking about the baseball, and my team had an unfortunate exit from the playoffs. Yes, we were self-funding this quarter with positive operating cash flow, which obviously was very gratifying, especially in a high revenue, high lending quarter, which is, of course, when it tends to put the most strain on the cash balances just because of the volume of advances going out the door. We do feel very comfortable with that funding position. I think, obviously, the debt facility is still very important to us, and we have drawn in order to fund the existing book, but we do feel well positioned going forward. As the personal loan book grows, obviously, as and when it makes sense to think about that position, and presumably, if that book grows to be several hundred million dollars, for example, then, of course, we would not be self-funding to that extent. That would be a good problem to have, and we would, of course, only expand those debt facilities to the extent that it was clearly value creating to do so. Okay, excellent. Thank you, Jamie. One other question from me, then I'll leave it to others. You've got three customers for Carrington Labs in the U.S. that have been signed on as customers. Can you give us a sort of status update as to where each one's at? Are you writing revenue with any of them? Are they onboarded? Let me start by saying that we have three announced customers in the U.S. I don't want to commit that every customer will always be announced, f or various reasons, we may not always do that. The ones we have, look, they're in different places. We do have some clients that are paying bills right now, loans in production, and we have others that are at different stages of onboarding. Our experience is generally that our side of getting a client up and running once they've signed is very quick, and we're generally ready in good order. It's more a question of the client's ability to work through their side and then push the button to turn things on. In terms of being up and running, yes, we do have revenue coming in right now. Other clients are either there or close to it. If there's work to be done on their side, that's usually the thing that takes the longest. OK, thank you. Thank you for your questions, Larry. Moving on to other investor questions. As expected, there's quite a few on Carrington Labs. I'll pull those together in a minute or so. Before we get to Carrington Labs, Jamie and Laavanya, the first question is, what caused the large increase in the average size? I noticed it was flat for a few weeks. The follow-up question to that is, how much has the H&R tax refund contributed to this increase? Should we expect the average size to remain at this level next quarter? Why don't I take that, and then, Laavanya, feel free to jump in. I'll start with the second part. H&R Block, that's a tremendous partner. We really enjoy that relationship quite a bit, and it's great working with them. It was great to see that we were able to jointly promote the tax refund advance more. Many of you would have seen the sandwich board signs outside H&R Block offices noting it. I think because Q1 often is a little bit slower, ironically, because of tax refunds, while H&R Block, that tax refund advance is a great product, and we're delighted to have it, I wouldn't say that Q1 certainly doesn't get an unfair advantage overall because of the H&R Block relationship and the tax refund advance. I think of that more as partially compensating for the natural dip in demand that we see in that July-August time frame. Coming back then to the average advance size, this comes back to what I was saying earlier about the increasing focus on higher value customers. There are a lot of moving parts in terms of how we do that. A lot of that is around how we think about customer acquisition. Part of it is how we think about credit criteria and limit setting and a number of other moving parts. Essentially, to oversimplify, you can think of that pay advance business as having, it's really sort of probably more like three customer segments, but let's say it's two. You've got the kind of the least risky, highest value, 30%- 40%. Those are the people getting the $1,000, $1,200, $1,500, $2,000 advances with very low default rates. They really drive the economics of the business. The significant majority of our net transaction margin in dollar terms comes out of that lower risk, higher value segment. You've got a bunch of customers, and they're good people, and they're often highly disciplined and financially literate and just have a different financial situation. They tend to be getting much lower limits. A lot of them are getting $50 limits, $100 limits, and the default rates tend to be noticeably higher. Between those higher default rates and those lower limits, their collective contribution to our economics is not that great. One of the things that we're thinking about quite a bit is how do we, while we're always happy to serve any customer who's eligible and passes our credit assessment, our focus, I think, is how do we think about that 30%, 40% that are driving the economics. When we do that, the customer mix shifts towards those higher value segments. Two things happen. One is the active user number, which includes, obviously, just everybody kind of equally weighted. With that mix shift, some of those lower value users, you may not have as many of them. The active user number, I mean, it may go up or it may go down. In this case, it went down slightly, mostly at that end. When that mix shifts, the other side, of course, is that if you're writing more $1,000, $1,500, $2,000 advances, that will bring the average up as well. It's quite hard to forecast average advance size. There are a lot of moving parts that affect it. I'd say, while we don't target an average advance size, we set our limits based on how we think about the highest possible expected value for each customer for all the different possible limits based on our understanding of their elasticity of default. As we focus on those higher value customers, you wouldn't expect that average advance size to necessarily revert right back down to where it has been for the last couple of years or so. You might expect it to stay maybe here, maybe a bit lower, who knows, but kind of at that north of $400 level, most likely. That was a very comprehensive answer, Jamie. Thanks for that. It's sort of taken out another question or two, but I'll ask it anyway. It's in a similar sort of vein. It's from Phil. Well done, team. How are you seeing the repeat customer usage with that greater focus on the higher value customers? We think about repeat customer usage primarily, so there are two ways to think about it. One is just how many times are people using the product in any given year or any period of time. The other way is, are customers who are repaying their loans and thus remaining eligible, are they coming back regardless of time, whether it's a month later or a year later? We think more about that second category, which has a lot to do with our mission-driven roots. We don't want to try to get people to borrow if they don't have that need. If someone only has the need once a year, then that's fantastic. What we want is if they have the need, they think of us and come to us. I think shifting towards higher value customers doesn't really affect that very much because those customers are very loyal as well. They do generally have the need somewhat less often. We see the highest frequencies of usage at that riskier end and the lower frequencies at that higher end. If you're measuring the other way I talked about thinking about this, the average number of uses per year, you would expect that as that mix shifts, there's a slight drop-off in frequency of usage. Indeed, if you look back over the last really several years, there's been a slow but noticeable decrease from rough numbers, more like 8x a year to more like seven-ish times a year over that kind of two or three-year period, primarily because of that mix shift. There is a slight decrease as a result, which we think actually is good news for those people if it means that they actually just have the need less often. Okay, the next question is about OpEx. What's caused the drop of $0.5 million in operating expenses? Are we approaching marketing differently now? I can grab that one. On the digital media spend, we have very sophisticated modeling in terms of how we think about our spend. The more we look at the lifetime value of our customers and make sure that our media spend is appropriate, it does go up and down quarter- on- quarter. In this particular quarter, it did go down, and that contributed to that decrease. You can see that it didn't impact our overall advances in revenues. They continue to be solid. It kind of will dip up and down, and that isn't necessarily a reflection of us making that specific decision, but more the way that the modeling comes out. In terms of our cost control, we are very tight on our spend over here and make sure that we keep on top of that. Of course, that will change quarter- on- quarter. As the business continues to evolve and change, and we work more on our Carrington Labs business, that will obviously change over time. It's not to say that for going forward, we expect it to stay at this very particular low level, but that's why we're sitting on that OpEx spend. Okay, moving across to personal loans. How much of the personal loans volume disclosed was written in the second quarter? I don't have the exact breakdown. Sorry, I assume the question is first quarter. First quarter. Yeah, with the one we just finished, I don't have the exact breakdown. Reasonably significant because the first few quarters, we launched it in Q2 of last year, and the first couple of quarters were really sort of just doing different things and testing and trying this and trying a bit of that. Q2 and Q3 were by design very thin volume as we worked that out. Q4 and Q1, we've really picked that up. The significant majority would be Q4 and Q1. I don't know the exact breakdown between four and one. Okay, we'll go across now to Carrington Labs. There are several questions. We'll start with maybe this one. Are you able to give us any indication of the dollar quantum of Carrington Labs partnerships and how they might be progressing? We haven't obviously released revenue numbers around Carrington Labs. I think at this point, it's a little hard to tell until we've kind of seen some more of those kind of contracts come through, go live. As you can imagine, especially very early in the piece, we're writing fairly commercially attractive rates, which we think in order to help gain that traction. As evidenced by the fact that we haven't broken those numbers out, we have said they're not material to the overall group at this point in terms of what clients are actually paying us live in production right now. Okay, there's a follow-up question, a similar sort of question, Jamie, maybe pushing the point a little bit further. When do you expect to see significant revenues come through? I think we feel like we have built out the product. It is ready, it is live, i t is market. We have clients running on this right now. As I said in the past, we have a, if anything, overly full pipeline. The reason I've been kind of jetting around the U.S. to sort of a large number of different cities is for exactly that reason. Our experience has been, which probably won't surprise anybody, that our clients tend to be often potentially quite large banks and non-bank lenders. They have slow sales cycles, but also quite unpredictable sales cycles. There are a number of opportunities that could close very, very soon or might take quite a while to close or might not close at all. The short answer is I genuinely don't know how many clients we'll have signed in FY 2026. I think we obviously are looking forward to that just as avidly as you are. If we didn't think it was going to happen, then, of course, we wouldn't be talking about it nearly this much. Okay, there are a couple of questions about the pipeline, but you've just answered that and how close you are to signing, which you've answered. How do you see the credit score product for Carrington Labs that was recently released going? Will this help you onboard customers quicker? Yeah, it's a great question. If I think about the evolution of the Carrington Labs product over the last year, at the core of what we do, which is the ability to build a tailored credit risk model very quickly because we have a heavily automated training pipeline and have it reflect the individual product segment lending experience of that lender, that's been rock solid really since launch and continues to be so. We continue to invest in and upgrade it. If I think about what we've been doing over the last year, one of the big learnings for us, I think, has been focusing on ways to make it easier for clients to say yes and get started. The product is not difficult. I mean, it's quite extremely difficult and complicated and sophisticated. From the client's point of view, we have a simple set of APIs, and they call the API. Especially some of these older lenders with kind of legacy technology stacks, it can be difficult for them to extract the data. Sometimes they kind of just want, is there something we can do simply? Their decision processes are hard anyway. The modular cash flow score product, I think our flagship will continue to be those custom-trained models. The nice thing about the modular cash flow score is you can call it without having to do any other work aside from obviously reaching some sort of commercial agreement with us and get a result back. I think that's an easier thing for some clients to say yes to and to get started with. Maybe they stick with that forever or they move towards that higher-end customized model. Yes, I think we think that'll be quite valuable in helping clients get their heads around the product or get their heads around the onboarding onto one of our products more quickly and easily. Just further on that cash flow scoring feature, Jamie, is the intent to sell this as a separate product to your credit decisioning model, and is it similar or superior to the FICO score? Sorry, the second question. Yep. Yes. Now, coming back to the first question, sorry, a bit more on that. Yes, I think, look, credit scores that are traditionally done, they were invented in the 1980s. For various reasons having to do with kind of how they've gotten entrenched in the financial system in the U.S. in particular, but really most developed markets, they haven't moved very far. They're based on a very narrow set of data. I encourage those of you who are interested in this, get a copy of your credit report, which is easy to do, and look at it. You'll find very little useful information on it. Banks are looking at this and trying to figure out, should I lend this person $5,000, or $10,000, or $50,000? The answer is, I have no idea. There's just very little information in there. The credit score itself is pretty questionable. I keep track of my own credit score. What happens is your utilization of your credit line is a big factor in that. Every time I go overseas, I use my U.S. credit card, which gets reported to the bureau. My utilization goes up, and my credit score falls for a month. I come home, and it goes back up. I mean, this is all nonsense. A cash flow score is a significant improvement on a FICO score or any other traditional score. The question about whether we're selling it separately. You wouldn't use our cash flow score and our tailored risk score for the same client for the same product because really the tailored risk score is just a fine-tuned version of that that reflects your lending experience. We do have a range of different products. Except for that tailored credit risk model, all of them kind of line up quite neatly in a modular fashion with both the credit score and the tailored credit risk model. Okay, maybe a final question on Carrington Labs. What are some of the barriers to adoption by large established lenders in the U.S.? The biggest one is just really the internal decision cycles of really large financial institutions. This is not a criticism of them. I've worked in large banks, and it just is hard. There's heavy governance around change. Internally, it can be difficult to get things done. There's a lot of regulatory scrutiny. We are quite good, I think, at kind of sitting alongside the existing approaches and processes in a very sort of light way and then kind of dialing up that weighting and that impact over time. Usually, it's just the fact that these are very big, often sprawling organizations that it just takes a while for them to make a decision. The second barrier, we have plenty of capacity in terms of analytics. Because we have such an automated way of training these models and deploying these models, and everything's done on a kind of a fully kind of cloud-based, pop-it-up-quickly basis, the actual analytic side, the onboarding side, all of our capacity there is very high. Where our capacity is constrained is capacity around talking to prospective clients and kind of holding their hand through the process and then the contracting and procurement processes. Where we are definitely capacity constrained is the ability to kind of negotiate with many different teams of lawyers about the contract and things like that. Sometimes we are the only place where the stumbling block potentially is going through some of these kind of very heavy procurement processes as well. Okay, we've got three more questions. Just a reminder, your last chance. If you've got any questions to ask, please type them into the Q&A box at the bottom of the screen. The next question, maybe one for you, Laavanya. In the 4C, admin and corporate costs were $1.9 million for the quarter. In past quarters, it was between $250,000 and $734,000. Has there been a reclassification of the cash flow items or something unusual in this quarter? We did have some, it's actually more a comparative correction. On that side, there was a reclassification based on share-based payments, and that's created that deviation. Thank you. Ok, we've got a couple more questions and follow-ups on Carrington Labs, as expected. The first one is, is the fee you charge for the credit score product in line with what other customers charge in the U.S.? Is the tailored risk score therefore a higher fee to your customers? I assume the question means compared to competitors in the U.S., not customers in the U.S. Look, everything we do is a premium product. I think we can very clearly prove to a client that this is potentially, I mean, some of these clients, we're in a position to save them tens of millions of dollars. I think our goal is always to price this in a way that what you are paying us is completely irrelevant compared to the benefit that you're receiving. As a result, it might still be higher than what you're paying for a credit score or an income verification or a KYC check or something like that. I don't think we make any apologies around that. In general, all else being equal, we would charge more for the tailored risk score than tailored risk model than for the cash flow score. The difference is not necessarily going to be that great, again, because the actual work that we do to train a model for you is very heavily automated. Really, the thing that's harder about those tailored models is more kind of working with you to get the data from you that we need to train the model. All else being equal, yeah, it would probably be more expensive than the cash flow score on a like-for-like basis. Ok, the next question on Carrington Labs is, I don't think this one's been asked before. How much partnership opportunity exists domestically for Carrington Labs? Do you see this as an equally important contributor for the bottom line as well as a reference point for international partnerships? That is an interesting question. I think if the question means kind of in terms of clients in Australia as opposed to more distribution partners, look, I certainly think there is potential here. I think the U.S., obviously, a far, far larger market. I think just given the nature of that market, often kind of easier to have a conversation about cutting-edge ways of doing things like credit decisioning. I think we're, of course, open to doing more business in Australia. I expect we do that on a slightly more opportunistic basis, given that we already have great traction in the U.S. If anything, I think the U.S. might be the reference point for Australian clients who might potentially be interested in it. Ok, we've got another question from Lloyd, and it's probably a good one to end up on. Congratulations on the great results. You've really hit the ball out of the park. The final question from Lloyd is, he'd be keen to get your view on how the lending industry is likely to adopt AI. What discussions or themes have you heard from the conferences you've attended? That's a really interesting question. This is a topic I actually speak on at a lot of conferences. I'm not on this trip, but my previous trip in September, I was speaking at one of the big conferences in New York on exactly this. As everybody knows, I think we're quite bullish on AI. We tend not to kind of talk about it too much just because I think everybody's talking about it so much. In some ways, we prefer to just kind of keep our heads down and kind of just do the good work and make people see the results, both in terms of our lending performance as well as the quality of the Carrington Labs product. AI can and will change lending quite a bit. I wouldn't say that it's really done so yet. I think it does so in two ways, again, at a very high level. One is that there is still a tremendous amount, tremendous administrative burden, I mean, not at Beforepay, but at most traditional lenders around scanned bank statements and really kind of data entry type things and manual processes. AI, I think, will help clean a lot of that up. Ultimately, people shouldn't be using large language models to transcribe phone calls where a loan officer is asking questions. They should be doing what we do, which is write code that fully automates the process so that the humans don't have to touch it, and it just goes straight through in a deterministic fashion. Because that's a heavy lift for most people, I think AI will do a lot of that work. The other area where I think it is genuinely disruptive and is a big part of what we do, especially on the Carrington Labs side, is I don't think you'll ever want to make individual risk assessments or look at an individual loan using any kind of neural network-based large language model generative AI type technology because they're black boxes. They are non-deterministic. They can produce 10 different results on 10 different runs of the same data. We see this with hallucinations. They can produce unexpected results that can be quite unhelpful if you're a financial institution. What we do is we use a lot of different techniques, including AI and other things, in the upstream process of creating those individual tailored models. The models themselves are fully deterministic, statistically-based machine learning models where you know exactly how they're producing the answer. You can look at and audit every single factor that's in them. They will produce the same result 100x in a row with the same data. How you get to that deterministic model object, you can do all sorts of things with because you can inspect that at what we call the control point. I think that upstream process of building these models, we've found the reason we can kind of mass tailor these things and do them so quickly and efficiently, there are a number of technologies that go into that, of which AI is a significant portion. Ok, that concludes the Q&A session. Thank you very much. I will now hand back to Jamie for any closing remarks. Thank you, Danny, and thank you, Laavanya, and thank you all for joining us. The business continues to thrive. I think the core business on pay advance is doing well. We feel excited about personal loans. Of course, we think Carrington Labs has almost unlimited potential. It is gratifying to see results that are really reflecting all that hard work and all that capability that's gone into building the business out for the last several years. Many of you have been on the journey with us for quite some time, and we appreciate all of you. I know that many of you were patient with us through a period of time when the path ahead may have not looked as bright as it does today. I hope you all feel suitably rewarded for that patience. It's terrific having all of you on the journey, both old friends and recent holders as well. We look forward to seeing you at the next quarterly, and we look forward to the journey ahead. Thank you very much. Thank you, Jamie. Thank you, Lavanya. Thank you to all the participants. You may now disconnect. Thank you.