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On Wednesday, 04 June 2025, Riskified Ltd (NYSE:RSKD) participated in the 45th Annual William Blair Growth Stock Conference, highlighting its strategic focus on fraud management solutions for e-commerce. Led by CEO Iddo Gal, the company showcased its commitment to leveraging data and machine learning to enhance profitability and growth. While the company emphasized its innovations, it also acknowledged the challenges of adapting to new verticals and geographies.
Key Takeaways
- Riskified’s GMV exceeded $140 billion in the past year, showcasing strong growth.
- New products, particularly Policy Protect, achieved 90% year-over-year growth.
- The company is focusing on diversifying across industries and geographies.
- Riskified aims to leverage its data network and machine learning for further product development.
- A strategic emphasis on profitability and scalability was highlighted.
Financial Results
- Riskified reported over $140 billion in GMV through its system last year.
- New products saw a 90% year-over-year growth, with Policy Protect leading the charge.
- Revenue from new products is projected to reach low double-digit millions in 2025.
- Top 10 accounts experienced a 30% cost reduction and a high single-digit increase in approval rates since the IPO.
- Adjusted EBITDA improvements were noted, reflecting a focus on profitability.
- The company anticipates generating over $30 million in cash flow this year, slightly less than the previous year.
Operational Updates
- Expansion beyond chargeback guarantees includes adaptive checkout and chargeback management.
- Policy Protect is the fastest-growing product, addressing policy abuse.
- Riskified leverages its data capture capabilities for customized fraud prevention solutions.
- Identity graph technology helps prevent policy abuse by clustering related accounts.
- Autonomous model training enables scalable model development and deployment.
- Diversification efforts focus on nondiscretionary categories and growth in APAC and LATAM.
- Success noted in Women’s Categories and Food Delivery, with Tickets & Travel as the largest category.
- Fashion and Luxury businesses decreased to a third or less of total business.
Future Outlook
- Growth opportunities through same-store sales increases and geographic expansion.
- $350 billion in white space GMV available for upsell within the existing client base.
- 80% of the top 30 clients have had at least one upsell in the past two years.
- APAC and LATAM regions expected to drive significant growth.
- Continued strong cash flow generation anticipated.
- New products leveraging machine learning and data networks are in development.
Q&A Highlights
- CTB differences in new verticals start higher but improve over time.
- New products have SaaS-like margins without chargeback components.
- Merchant-specific data training is crucial for accurate fraud prevention.
For the full transcript of Riskified’s participation in the conference, please refer to the detailed transcript provided below.
Full transcript - 45th Annual William Blair Growth Stock Conference:
Chris Kennedy, Research Analyst, William Blair: In person and online. My name is Chris Kennedy. I’m a research analyst at William Blair covering the fintech and payment space. For a complete list of research disclosures and or potential conflicts of interest, please visit our website at WilliamBlair.com. Next up, we have Riskified.
This is the second time they’ve attended our conference, so we’re pleased to have them again here. From the company, we have the cofounder and CEO, Ito Gal. Riskified was founded in 2013. They came public in 2021. And at the core, they provide fraud management solutions that help their customers grow while managing risk.
The chargeback guarantee product was really the core product, but they’ve been expanding into new areas, which I’m sure we’re gonna hear about. With that, let me pass it over.
Iddo Gal, Cofounder and CEO, Riskified: Thanks, Chris. That introduction was my first slide, so I could skip it as well. Alright. So nice to meet everyone. Iddo, cofounder, CEO.
By the way, just a bit of background. I in the Israeli intelligence community working on online identities. Afterwards, worked for a company called Fraud Sciences in the world of ecommerce and fraud prevention. That company was acquired by PayPal, so that’s how I kinda got into this world and had some domain knowledge. Being from Tel Aviv, worked in another startup on kind of more on analytics machine learning side, which is where I met my cofounder Asaf.
And at some point, I said, hey. You know, I know that there’s this problem with ecommerce fraud management. It’s really challenging and costly for merchants. They unfortunately accept bad transactions, and that results in a lot of floss that they need to pay out because they’re liable in that event. It also causes them to turn away a lot of great transactions.
And I think that if we build, you know, kind of a a machine learning based solution to tackle ecommerce fraud, we can provide them a lot of value. So I’ve said, you know what? That sounds like a really great idea, and that’s how we started to to build and ramp Riskify. And fast forward to today, that was early twenty thirteen. We built an ecommerce fraud prevention company focused on enterprise merchants, went public a few years ago.
Really proud of the fact that we work with over 50 publicly traded companies managing that entire process for them. And if you think about the GMV flowing through our system last year, it was over a hundred and 40,000,000,000. So if you were just to think about us like an as as an acquirer, we would we would actually be one of the largest acquirers just based on that, you know, definitely within the top 10. And focus on working with large strategic accounts. We believe that large enterprises choose best of breed solutions.
They are able to go from kind of a more standard platform approach towards really focusing on what is the best solution for the in this specific category. Our pricing is risk adjusted. That helps us work across different industries and geographies that have different risk profiles. So you can see someone like a I’m just orienting myself around this. Like a Blackhawk Networks, which I’ll you know, a kind of digital gift cards preloaded with cash, obviously, extremely high risk towards someone like an Allbirds, which is, you know, we would consider a lower risk category.
The fact that we do risk adjusted pricing really means that there’s value for everyone here. So let me take a, you know, a step back. When you think about the cost structure of an ecommerce merchant for managing fraud, okay, the basis would be the first and most important piece is the cost of chargebacks. And just to give you kind of sample numbers, let’s say that’s 25 basis points. That basically means that for all the revenue that that merchant is experiencing, they need to pay out in chargebacks 25 bps, and that could be a very substantial amount.
And that’s because merchants have liability in the case of a chargeback for an ecommerce transaction. So if I steal Chet’s credit card and place an order online with one of these merchants, they accept that transaction because they don’t know it’s a stolen credit card. They ship me the goods. Chet sees it on his statement. He calls Allbirds and say sorry.
He calls Visa or his issuing bank and says, you know what? Someone stole my credit card. It wasn’t me. Chet gets refunded. He’s protected as as a consumer if you’ve ever filed a chargeback.
And now the merchant is liable. So when we say that’s their cost structure, the 25 bps, that’s the amount that they’re paying out because they have liability, because they accepted fraudulent transaction. So that’s, let’s say, 25 bps on average. What they pay for tools traditionally was much lower than that. Okay.
It was, let’s say, in the range of two bps. You would pay a few cents per transaction for someone to screen that transaction, recommend, accept, decline, or allow you to do a review process or to do something with the transaction. And you would have a team, and that team would cost three bps. Your overall cost structure is 30 bps. Most of it goes to paying back chargebacks, small amount to the service and the tools that you’re using, and some of it for staffing.
When we started Riskified, okay, we kinda said, look. We don’t wanna be that two bips small solution. We actually think we can provide more value, be extremely accurate, and provide guaranteed results to our merchants. So we said we’re not just gonna provide them a recommendation and take two basis points. We think we can be an end to end solution and guarantee it.
So we come to our merchants and say, hey. If your cost structure is 30 bps, our fee is gonna be lower. It’s gonna be a discount to that. It’s gonna be 20 bps, 25 bps, and we’re gonna guarantee the transactions in case there is a chargeback. Okay?
So now what happens when you start risking using Riskified is you’re gonna be paying us less than your existing cost structure of managing fraud. We’re so much more accurate that even while we’re providing the merchants a discount, k, we still have, you know, kind of very positive ROI and margins on that account. And we’re also providing them much more value. When you talk to our merchants, they sometimes says, look. I don’t want a solution that’s you know, where I wake up coming to work after the weekend, and suddenly there was a model drift, and there was an issue, and I just lost $5,000,000.
I want a responsible partner that’s gonna manage this part of the business so that I don’t need to focus on it. And that’s the great thing about the guaranteed performance and results that we provide. We don’t just guarantee the cost side of the equation. We also guarantee the approval rate. Okay?
Because one of the easiest things sorry. Let me come back to one of the easiest ways to reduce the fraud is just to reduce the approval rates. Right? That then it’s no problem. And, obviously, all these merchants proactively renew and continue to use Riskify because we actually help them increase their top line as well.
So the dynamic is you have the cost, and we said that’s 30 bps. And for that 30 bps, merchants are approving, let’s say, 90% of incoming transactions. They proactively turn away 10% of volume, again, average made up numbers, because they fear that might be fraudulent. So when Riskified also guarantees, you know, the price, the cost, we also guarantee a higher approval rate. And it can vary between, you know, how much savings versus how much incremental lift, and it varies by merchant.
It varies based on their margin profile. But when we IPO ed, we looked at the top 10 accounts, and we saw that on average, they were experiencing 30% reduction in cost and I think high single digit increase in in overall approval rates. So very meaningful ROI. And, obviously, we operate in the ecommerce market. And while we’re proud of the hundred and 40,000,000,000 in GMV that we’ve already done, still tiny compared to the ecommerce 6,000,000,000,000 kind of TAM.
I would say probably about three years ago is when we started expanding from a single product into more of our platform. So, really, the order approval and fraud review is where we started, really looking at transactions that come to the merchant after they’ve passed through the entire payment funnel. For those of you who are, you know, kind of versed in payments, that means that there’s been an auth request, and now the merchant has the ability to capture them. And we were talking to our merchants, and they said, you know what? That’s great.
But, actually, we think you can influence the bank payment authorization rate as well. Started collaborating with them as well, and now we have something called adaptive checkout, whereas we can send enriched data to participating issuing banks and card issuers. Okay? And we do this pre auth. Using that data, the issuers are able to provide a higher auth rate for our customers because they have more enriched data that we share with them.
And there are we’re able to screen fraudulent transactions before they go into the payment stream, and that helps our merchants have a a better and more sophisticated MID, so merchants can enjoy a better end to end conversion rate. So that’s number one. Number two is our Policy Protect product. This has this is our fastest growing additional product. We shared a 90% year over year growth in our new products, q one to q one.
Most of that is coming from PolicyProtect. And while it’s still a small base, it is growing relatively rapidly. So new products grew at, you know, kinda mid to low single digit millions in ’24, and we’re anticipating them to be low double digit millions in ’25. And most of it is from policy. And what policy does is that we’ve seen that, you know, I don’t need to steal again, let’s use Chet Chet’s credit card.
I can just call the retailer and say, well, I never received my package, or I received the wrong color or the wrong size. Give me a refund. Send me a new pair. And nine times out of 10, a retailer say, you know, fine. Of course.
You know, packages get lost. I wanna create, an amazing customer experience, and there’s a ton of fraud there. There’s a ton of fraud. And we found that leveraging the same engine and data, we can run our models to find that type of policy abuse. So for some of our merchants now, we’re analyzing when someone initiates a refund and return request.
We’re analyzing that request and blocking, or allowing. Right? And we’ve seen instances in most instances, we’ve been able to block upwards of 10% of refund requests without incremental false positives as the merchant measures it. Usually, it’s callback or complaints or subsequent chargebacks, and that’s a massive increase in revenue for the client. Right?
They used to just pay out 10% of these refund requests, which are actually fraudulent. So that’s one use case of the policy product. Other use cases could be around item limits, resellers, item launches. Item launches are big, whether it’s a sneaker merchant that wants to make sure that a bot is not purchasing all the inventory, or it can be a concert where you wanna make sure that, you know, you have good distribution about who you’re selling into the policy. Product allows merchants to create rules and logics to manage their business.
They can also do positive differentiations here. It’s not just about negative and finding fraud. So some of our clients are using the policy product to look at who the best customer is and not just, you know, for the abusive customers, not provide them a refund, but for the best customers, provide them an instant refund. Don’t wait until the package comes back to the fulfillment center and then issue a refund. Issue a refund at that point when they click refund.
So it’s a much better experience for your best customers. Some of our customers are leveraging it to provide free shipping and returns for best customers. Other customers might have a restocking fee. So it’s a way to differentiate your customers, leveraging our network and machine learning, and provide a different experience while stopping abuse and fraud. So policy is, again, fastest growing from our newer products in our platform.
Chargerback management, once a chargeback comes in, there’s an entire process of disputing the chargeback with the bank. And our chargeback management software is a workflow tool that allows fraud teams to manage all chargebacks, fraud and nonfraud, with the bank. It can be set up either completely automatically. It can be semimanual. A nice story is that, you know, as management team meetings, we sometimes bring in customers to talk about, you know, their experience with using Riskified.
You know, we recently had StockX, which is a a long standing Riskified customer that’s now on the entire platform. So we asked, you know, from an ROI perspective, what what product from the stack provides the most value? He said, well, you know, chargeback guarantee, the fraud review piece, obviously, that’s kind of the biggest component and the most ROI. But for me and my team, the p the the part that we use on a day to day basis and love the most is the chargeback management. So we really love to hear that, and we think that as we become more and more of a merchant’s workflow and not just part of a decisioning engine, that is very powerful just from a usage and retention perspective.
So we think that’s really great. And then things on the product platform, account creation. So, really, ATOs are an increasing fraud vector, and this looks at when you’re creating an account logging in, and it helps, you know, kinda block bad actors from accessing that information and leveraging that to create additional transactions. So that’s the product platform today. Seeing a lot of traction.
We discussed the 90% kinda year over year growth. A lot of the on the q one call, we mentioned kinda that we’re we’re at a a record pipeline, and we’re seeing increasing win rates. A lot of some of that or a lot of that is attributable to this kind of, product platform. It used to be that we would pitch merchants on the chargeback guarantee fraud prevention. They would say, well, you know, it’s interesting, but I’m an ecommerce merchant.
I have 10 other priorities. I have, you know, resources to integrate three of them, and you’re number four. Now that we’re able to solve more problems for the business, it helps facilitate additional conversations, additional ROI to get the integration resources, and we’re really excited about that. So how do we do it? Why are we more accurate than merchants, than other systems?
So number one is obviously the the network effect, the flywheel effect, the more data, the more merchants, the more capabilities, the more products. It just helps us generate more more and more ROI. But aside from that, I think there are a few specific and unique capabilities that I wanna call out. Number one is the level of data capture that we have per transaction. So when you think about what Riskifyd sees, we see when when you’re browsing on a merchant’s website, we have a beacon that collects that information, and we see the products and your behavior on the website.
We see how you’re logging in, creating an account because of our account secure product. We receive the checkout information as you check out. We also receive all the information as you contact the support system and request an address change or anything like that. If you were to file a chargeback for fraud or non fraud reason codes, we would receive that as well. If you initiate a refund or return request, we would receive that as well.
So, basically, the level of data capture is significantly higher than a payment network, a payment gateway, any one of the other players that we know in the ecosystem. And that deep data capture enables us to create a lot of very unique and compelling features. The fact that we focus on enterprises and we means we can also have very specific and targeted models. So for some of the large clients that we serve, we would have custom models with custom features that are trained on their data, and we have an autonomous training platform that helps us train and deploy models at scale. So if you think about a mid market provider that needs to have a generic electronics model that works for, you know, a hundred thousand merchants, we actually have the ability to tailor a a model to a merchant specific data and to do it automatically.
And that just helps provide a higher level of performance, especially with the the data capture that we have. Obviously, enterprise scalability and everything that enterprises need. Identity graphs are probably one of the newer capabilities that we’ve developed over the past two years. This has been especially helpful in the policy product. So our identity graph does a lot of clustering.
It helps us understand that someone is saying, you know, emergency is a new customer initiating a return. But our clustering and identity technology actually says, well, this is a cluster that belongs to five different accounts across three different merchants, and it’s the same identity. It’s an unsupervised learning machine learning piece, very unique and powerful, and we think it’s the the core component of the policy product. So that’s been gaining a lot of interest from merchants as well. Obviously, everything is AI or, as we used to call it in 2013, machine learning.
But that was really the original thesis and genesis of the company, and, you know, we we continue to be focused on innovating there. Big part of the team in Tel Aviv is around data science and analytics, and and that’s the core culture. And this is just a nice visualization of kind of the unsupervised learning that that helps us understand the graph. You can see kind of the various accounts, but how we tie them into a single identity. And, again, obviously, a merchant perspective, both because of the the network capabilities that we have, but also building this level of graph is is somewhat challenging.
It’s not something that’s accessible to them. And here, you can see some of the outputs of our autonomous training. Right? So we just understood that at some point, as you train and create new models that are customized to newer fraud trends, to new features, to new events in a merchant’s life cycle, you end up getting good results. But it used to take, you know, just a few weeks of actual data science time to train and validate and create and deploy these models.
So our new kind of autonomous training hub allows us to do this at mass scale, and it’s been helpful in driving performance as well. Great thing about the newer products, the platform, the things that we have in mind coming up as well is that they all sit under the same data structure and merchant network. So, you know, if we think about the cost to build the policy product or the dispute product, obviously, meaningfully lower than what it cost us to build the chargeback guarantee and great network effect capability. So as we think about how we envision expanding a platform, we say, hey. We have a a deep integrated relationship with, you know, 50 publicly traded companies, and that number is increasing.
What other problems can we solve for them leveraging our machine learning capabilities and the data network that we’ve already developed? And that’s the type of products that we continue to develop. And here, you can just see some of the the results. Like, the the audited s one material that we shared was 30% reduction in cost, 7% increase in approval rates on average for top clients. So the ROI, very meaningful.
No. I would say the journey that we’ve had post IPO, we’ve really focused on improving profitability, have actually reduced OpEx to I think this year, it’s flat on the midpoint. But prior years, we’ve been able to reduce OpEx, so basically flowing through a % of incremental kinda gross profit dollars to the bottom line, showing the the scale, the leverage, the automation in the business, have continued to we have a large cash balance, have been very active in in buybacks and, you know, anticipate to continue strong activity at similar levels, at the same time, showing great growth and diversity across regions. And I think you can see that historically, we’ve been, you know, kinda more successful in some discretionary categories like fashion and and luxury. Over the past few years, we felt that, you know, that’s been hurting us from a same store sales perspective, so really focused on diversifying the business and going after nondiscretionary categories.
So we’ve been able to call out good success in both the women’s categories, the food delivery business. So we think we’re building a more diversified and, you know, base that can withstand various economic conditions. Also seeing, obviously, a lot of growth outside of The United States. I’ll call out, you know, kind of APAC and LATAM as big anticipated growth drivers for us. And if it used to be that a few years ago, the fashion luxury business was 45% of the business.
It’s now a third or even less than a third. Tickets and travel is right now the largest category, but we continue to anticipate strong growth in newer categories. And here, you can just see some of the adjusted EBITDA improvements that we’ve achieved. I would say that around the type of the IPO, it’s also when we decided to re to go into an investment cycle around some of the global go to market and the product platform. So you can see that increase in spend, but also kind of the improvements made there since.
Seasonally, q four is just seasonally a much stronger quarter because of returning buyer activity. That’s why you see some of that. And very strong cash flow, you know, generated, at guide over 30,000,000 this year, slightly more last year, and anticipating continued strong momentum there. So one thing I wanna call out that’s important, you know, we talked about the fact that we the chargeback guarantee model, basically, it means that the biggest component of our cost of sales are the chargebacks, the amount that we pay back because of the mistakes that we made, and that’s the biggest impact in our, gross margin. That’s why it’s hovering in the 50 plus percent range and not kinda more traditional SaaS like.
The the puts the gives and takes there is that we see continuous improvements because of the machine learning, because of the model, because of everything in in cohorts over time. And you can see that, basically, in virtually all cohorts, pretty consistently, they improve. And that’s offset by the addition of newer cohorts, new clients, new business, new geographies that tend to start at a at a higher CTP rate. Okay? So that’s how we would anticipate the business continue to behave on the chargeback guarantee side.
Core machine learning improvements, improving cohorts offset by the addition of new business and newer cohorts coming in at slightly higher. To make it simple, if we go into Brazil and we have a remittance merchant there for the first time, we would expect to start at a high CTB, but to improve it over time. But we think that’s, you know, kinda worthwhile because long term, it’s a great geography and industry for us to be in. We consistently gain more accurate become more accurate as our system evolves, And that accuracy, we can take it into two different directions. Number one is we can you know, for the same approval rate, we can improve our margins, receive less chargebacks because you’re more accurate.
The second thing you can do is you can keep the chargebacks where they are and increase approval rates and provide more value to the merchant. So that’s usually some of the kind of give and takes that we have, and we need to make business decisions. Are we gonna improve our margins right now, or are we gonna provide more value to the merchant? And it’s really kind of a business decision. And that’s it.
As we think about kind of the opportunities for growth, obviously, there are same store sales increases in growth with our merchants. We take a usage based model. We’ve had a lot of success. Almost all of the growth growth has come from winning new merchants over the past few years. We have a lot of expansion opportunities within our existing clients.
I think we’ve shared that for the hundred and 40,000,000,000 of GMV that we’ve done in ’24. We actually have another 350,000,000,000 in white space GMV available to upsell. We’ve shared that over the past two years from the top 30 clients, 8080% have had at least one upsell. So it’s kind of a a recurring process that we go through. You know, started building a a sales team in areas like, Brazil and Latin America and Japan about two years ago.
In most cases, these are starting to ramp and and provide kinda more pipeline and revenue, so that’s on the geographic expansion, continuously targeting new categories. Example is, but we still have kind of a long list of things to focus on. And the platform sale has also been helping drive growth. And that’s what we’re focused on right now. Alright?
Thank you for your time. And, Chris, I think I’ll Yeah.
Chris Kennedy, Research Analyst, William Blair: Well, thank you for that, and we will take questions here. So don’t be shy. But when you think about some of the newer verticals or geographies that you’ve entered into, can you just talk about kind of how the the CTB kind of differs, you know, initially versus and how you manage that?
Iddo Gal, Cofounder and CEO, Riskified: Sure. So so it does start higher, and we would anticipate that there are potentially more nuanced views on fraud within a new category, or there could be slightly different fraud patterns within a new geography that are unique. So that’s why we tend to anticipate a higher CTB there. And even when we think about, like, our CTB budget for the year, we know that our existing clients and what improvements we’ll see, and we usually peg, like, the new business to come in at a higher rate than that. And those are some of the reasons.
And then it would take a process of a few months, which we tend to try and drive that, you know, kinda quicker as much as we can, where we see the improvements. And I think just the the CTB charts that we shared and are also available on the supplemental information that we have on our website are the best indication of where it starts and how quickly it trends towards a different place.
Chris Kennedy, Research Analyst, William Blair: Great. Thank you for that. And then just talk about the margin profile of some of your newer products and how that compares to.
Iddo Gal, Cofounder and CEO, Riskified: So that’s more traditional SaaS like margins. They don’t have the chargeback component. It’s, usually priced on a recurring revenue base, with a fee being based on the merchant, tier or size. So that can definitely be long term, you know, kinda margin accretive. Yeah.
Correct. So you’re you’re. I wouldn’t criminals people more likely to submit a to file a chargeback on specific types of transactions. Yes. It’s very.
Again, I wouldn’t say trade, but these characteristics of a of a transaction are well, no. And what constitutes a good transaction in domestic US transaction for a low dollar amount groceries is not the same profile as what constitutes a good transaction on a global, you know, $10,000 luxury handbag, and that can create different model scores in different perspectives. Right? That’s why also it’s important to train data on an individual and merchant specific way because a lot of times you’re looking at variations from things that are considered normal within a specific account. Unfortunately, there’s always gonna be false positives in our system and in any type of system.
Usually, you get to a level where the you know, if you get to a ratio where, let’s say, one out of three transactions in this band of approval rates let’s say you get to 99% approval rate. And above 99, you know that your recall rate is gonna be, like, 30%, so you’re gonna have a false positive ratio of, two to three. It basically means that you’re incurring a very meaningful loss for each subsequent transaction in order to get to, like, one good transaction. And you always try to to minimize that. Right?
And you also like to do use that as a training set to to better improve your models, but there’s always gonna be some false positives for sure.
Chris Kennedy, Research Analyst, William Blair: Great. With that, we’re gonna have to wrap it up there. We do have the breakout session.
Iddo Gal, Cofounder and CEO, Riskified: Alright. Thanks, Chris. Thanks, everyone. Appreciate it. This presentation has now finished.
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