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The core of the card payments system as we know it today has been around for many decades. This system has a number of major shortcomings, not least of which is the 45-character limited established by the ISO 8583 standard. That may have been enough characters back in the 1980s but it is severely limiting in today’s complex world. But problems like these create opportunities for creative minds.
My next guest on the Fintech One-on-One podcast is Oban MacTavish, the CEO and Co-Founder of Spade. They have built a system for real-time intelligence of card merchants which is impressive in and of itself. But what I found most fascinating is they have built one of the richest data sets in country on credit card merchants. With coverage that is second to none.
In this podcast you will learn:
- The realization about archaic card payments that led to the founding of Spade.
- How the card payments value chain works today.
- The main thing that really needs to improve with card payments.
- How Spade is able to add more context to card payments.
- The disparate data sources they use to feed their system.
- How they update their merchant data.
- Who buys their data.
- The enriched data they send back to their clients.
- The main use cases for their data.
- Why they are not working with personal financial management tools.
- How they are working with Corpay, the corporate payments company.
- How they can help merchant acquirers.
- Oban’s vision for Spade.
Read a transcription of our conversation below.
FINTECH ONE-ON-ONE PODCAST NO. 495 – OBAN MACTAVISH
Peter Renton 00:00
Welcome to the Fintech One-on-One podcast. This is Peter Renton, Co-Founder of Fintech Nexus and now the CEO of the fintech consulting company, Renton and Co. I’ve been doing this show since 2013, which makes this the longest running one on one interview show in all of fintech. Thank you so much for joining me on this journey. Now, let’s get on with the show.
Peter Renton 00:23
Today on the show I’m delighted to welcome Oban McTavish. He is the CEO and Co-Founder of Spade. Now Spade is in the data enrichment business for card transactions. Did you know that when you’re doing a card transaction, only 45 characters of data can be sent from the merchant that is supposed to give all the information about that transaction. And that’s a problem that has existed for decades since this standard was created back in the 1980s. There is a better way and that’s what Spade is really doing here. They’ve figured out a way to really enrich the data, take those 45 characters, and provide a really rich data set back to whoever needs it. Obviously that is great for anti-fraud, but also for all kinds of use cases, which we do get into in some depth in this episode. Really interesting. I learned a lot about credit card data in this episode, and I’m sure you will, too. It was a fascinating discussion. Hope you enjoy the show.
Peter Renton 01:38
Welcome to the podcast. Oban.
Oban MacTavish 01:40
Thank you. Great to meet you, Peter.
Peter Renton 01:42
Good to meet you as well. So let’s kick it off by giving the listeners a little bit of background. Why don’t you tell us some of the highlights of your career to date?
Oban MacTavish 01:51
My career?
Peter Renton 01:52
Your storied and long career yes. I know you’re pretty young.
Oban MacTavish 01:55
I studied finance. I fell in love with investing when I was really young and invested my way through college. And then when I spent my summers working at a SaaS company in sort of like an FP&A, a finance type intern role, I did a little bit of sales when I was there, too. It was just a company in the Bay Area. And I think I really kind of fell in love with the magic of Silicon Valley. And I say that living in New York now, so I guess that says something. But I think there’s something really cool about being at a tech company. And as I graduated, I was really trying to decide between an investment banking path, the very standard finance graduate idea of what a career looks like, or something a little different. And I started my first company right out of college, I was one of the cofounders of a business called Hubly. We built efficiency technology for wealth managers. So think like vertical SaaS, before vertical SaaS had a huge moment. It was really challenging. I learned a lot about what not to do with your first company. And I wasn’t the CEO, but I spent most of my time trying to figure out how we made money in vertical SaaS. So very much like we sign a financial advisory firm, what are all the ways you can actually generate revenue from working with them? And then COVID-19 happened as we were raising a seed, we had raised about $400k, or something like that $400k – $600k. And we had no money left in the bank. And I decided, you know, the Cofounder sat down, we had to lay everyone off, and we were trying to figure out what we want to do next. And we tried to decide on a direction to go and I was looking at it being like, I just don’t, I don’t know, I didn’t see it anymore. I didn’t have that spark.
Peter Renton 03:26
Right.
Oban MacTavish 03:26
So I left. And it was really hard decision, but I left and I started figuring out what I wanted to do with my life. I briefly flirted with the idea of going to an investment bank or hedge fund or something like that. And then actually, I sort of caught the bug again, and I saw what I thought was, and still believe is one of the biggest opportunities in fintech and that would later become Spade. So that’s, that’s the SparkNotes version of my career and a very, very short little blip. But you know, that’s where we are today.
Peter Renton 03:52
Okay, so let’s dig into the decision to start Spade. What did you see, what was the huge opportunity that you wanted to pursue?
Oban MacTavish 04:00
Yeah, so I really recognized that there was something fundamentally wrong with transaction data, and I focused on the card universe, in that I realized, if you think about it, you know, card payments aren’t going away. We have these magical things in our wallet that get this instant access to debt You can buy something pretty much anywhere in the entire world. The fact of the matter is banks are still communicating on a protocol that was really designed in like the 60s through 80s, and has been only changed a couple of times called the ISO 8583. And the vast majority of card data is transmitted along this protocol. There’s a slightly more modernized version, but it didn’t really fix the fundamental problem, which is that this protocol was designed before we had e-commerce. It was designed in a time when a POS system was probably the best way for you to identify business because you walk into a mom and pop shop, you have this single POS system that’s representative but the reality is is that the merchant ID that the networks give you is a single POS system, which means somebody like a Walmart has 50,000 plus IDs, which makes banks have a really hard time to do a lot of the stuff we assume they’re able to do: understand where their customers are spending their money, give you the reward money that you’re owed, because they’re promising you 5% cashback on gas. And what I realized was that, I felt like it was just such a no brainer. Why don’t we have better data about card payments? As I started digging into it, I realized it’s a really complicated problem to solve. And there were people trying to solve it in what I felt was the wrong way. And I was like, you know, if we could help every single card payment be that much better, and help them unlock what I would describe as mission critical and really high value use cases for their customers, that would be an enormous business. If you’re helping every single bank, every single card payment on the planet, and you’re attaching your card to a horse that is moving very, very quickly and up into the right, it was a really compelling opportunity, because you’d be solving like a really critical problem for one of the biggest markets in the planet, which is financial services.
Peter Renton 06:03
That makes sense to me. But maybe before we dive into the details, I would like you to explain how the the card payments value chain works. Take us through the steps involved from when you swipe your card or tap your card, all the way through to getting your statement.
Oban MacTavish 06:22
Yeah, this is obviously a really complicated process with a lot of different players involved, and there’s also like interchangeability, right? Some people use somebody like Square or another who technically has somebody sitting underneath them, and there’s acquiring banks and everything like this. There’s a lot of complexity here. So I have a simplified version of this in my head that when we think about it, there’s a consumer and a merchant. And the simplest way to think about it is a consumer has a card, they tap their card at the POS system, or when you type your card in online, the decision has to be made, the merchant says, “Is this a legitimate customer? Did they put in their PIN rght? If it’s online, did they put in their address correctly?” so that the the people on the payment processing side say, “yeah, we’re good to go.” Stripe says, “okay, the record that we have for this consumer is their address, that’s all good.” Then the merchant says, “we’re good to go,” they pass along the data. So they’re sending a payload to the network. The network, sends it to the right bank, the issuing bank, who gave you your card, you know, Chase. And now Chase says, “okay, does Peter have enough money in his bank account? Is Peter overdrawn? Is this behavior normal for Peter? Does he normally spend this much money online?” What have you, right? They say good to go. Then the money gets pulled and everything happens after that. But the really important facet of this is that the moment you swipe a card, a lot of people have to say, “yes, this is good, yes, this is a legitimate transaction”. And I think a really important note of that is that there’s actually really different incentives from all sides. The merchant, they make a lot of money. If they’re a 90%, gross margin business, like software, and you’re putting in your card to buy $10,000 worth of Figma for your company, they’re making 90% gross margin on this. They’re very happy. They’re like, what are the odds that is really fraud? And how much is fraud really going to cost us? So we’re gonna unwind this whole thing and there’s some cost there. Banks, on the other hand, are actually really disincentivized. They’re like, oh my gosh, are we sure that this is really Peter? The trust lost if your bank authorizes a transaction that wasn’t you, that’s really, really painful. And they’re still making that really critical decision, like, do I want to authorize this transaction or not? But I guess the way you can think about it is that data goes from the merchant and the payment processor to the network, the network gives them scores. They, say, “hey, do we think this is pretty good?” They pass it along to the bank, who then says, “yes, we’re good to go on this transaction”, and then all the money starts changing hands. And that’s a really simplified version of it all. But that’s sort of how I like to think about it. It’s almost like a little big communication merkos, merchant payment processor > network > issuing bank.
Peter Renton 08:58
Right. So obviously, this is a mature system. You said yourself, the standards were created decades ago.
Oban MacTavish 09:06
Yeah.
Peter Renton 09:06
But it works for the most part. I mean, everyone’s swiping their card left, right and center. So what’s the problem? What did you identify as being the thing that really needs to improve?
Oban MacTavish 09:18
Yeah, so the concepts, I guess, if you think about it, banks used to, you know, it’s quite simple. They’re like “hey, we’re just going to move your money, we’re going to hold your money, we’re going to lend you some money, and everything’s going to be good”. But, you know, financial needs have gotten more complicated. The quality and the products that we’re trying to launch are far more interesting. Maybe we want to understand where our customer spends their money and give them rewards. Maybe we want to be better at stopping fraud because now the way people attack banks, from a fraud perspective is just dramatically different. And I think I recognize that if you look at the standard, you have around 45 characters, to tell the bank everything they could ever want to know about a merchant. That’s it, 45 characters.
Peter Renton 10:01
Right.
Oban MacTavish 10:02
And then you get a four digit MCC code, merchant category code. And that’s crazy. We can just stop there for a second and say that doesn’t make any sense. Forty-five characters is nowhere near enough context in the world we live in today, when anyone can pretty much spin up a virtual POS system. There’s websites, there’s phone numbers, there’s contact numbers. What did the person buy? Why isn’t there a receipt? What does this business even do? And we’re gonna put them into an MCC code that is a standard that is completely unregulated. There’s no reason you have to and POS providers and payment service providers, the Squares and Stripes and stuff will do their best to try to make sure you’re putting in the right one. But how do they know? You know? And the lines are pretty blurry. You know, ours is a cannabis dispensary. Again, there is no MCC code for cannabis, there is an MCC code for a pharmacy though. So you have these situations where not only is this just a very small amount of context, you also have very little verification of the data. That’s all the bank has to work with. And then you get a MID, which is the Merchant ID, which is a 15 character alphanumeric code that’s supposed to represent the business. This is sort of like a classic move that banks leverage where they’re like, if they see a lot of disputes on a MID, they’re like, we’re gonna block it. And that used to work. That used to work when you were a store and maybe you were stealing cards, and then you would swipe them at your store to take some cash out of the system. Problem is, oh, your MID gets blocked? You’re just going to create a new one by opening a new payment account with a new online platform. And so you’re really looking at a system where there’s very little verification, you have very little ability to add additional context. And it’s all being relied on for really mission critical processes like fraud detection, authorization, rewards, etc. And I think we fundamentally recognize that you need to be able to add context. And context is what sort of frees that business to do these really, really interesting things. It’s how you could do really targeted advertising to your customer, or a really detailed partnership, geographic-based authorization, better fraud detection, and all of this stuff is just consumer benefit. And banks are sort of hamstrung by the underlying data they rely on, and it’s why you look at something like a Mint that doesn’t even — that was via Plaid, notoriously horrible categorization.
Peter Renton 10:02
Yeah.
Oban MacTavish 10:08
Like notoriously bad. Everyone can say that. But we have a customer who leverages our data to make accounting decisions for their customers, and they have less than a 0.01% recategorization rate. And I’m just so proud of that, because then the amount of time that saves and the beauty of that product is driven by our underlying data. I think that’s a great example of why you need more context and more accurate, verified data about businesses.
Peter Renton 12:38
Right. More context would be great, and then if you were designing a system from scratch today, you would not design something for 45 characters. But back in the 60s, when this was being started, I imagine that was as much as the systems can handle back then. What does Spade do exactly? How are you able to add some of the enhancements you’ve already talked about?
Oban MacTavish 13:00
In essence, we’ve built a data network of merchant information. We’ve covered 99.9% of card accepting businesses in the United States. Then so with this massive, verified, structured database of businesses, and then we combine that with a layer of matching technology that we’ve built in-house, where you send us the payloads you get from the ISO 8583, those like 45 characters, the MID, the amount of information, the MCC code, all those really bad pieces of information. And then in real time, and I say when I say real time, I mean, truly real time, we have a P 99 of under 40 milliseconds, we will send you back all of this really detailed firmographic and insights-driven data. So if you think of us like a data company, we combine best in class data sources, information we get from our customers, and all sorts of other data to create this merchant profile that we then match transactions to, and then we throw all that data at you, when we give you all of the context you could ever want about a business. Rather than, there’s lots of people who will give you lots of information about your customers, they’ll tell you who they are, their behaviors, they’ll put an SD cam in an iPhone and get some other information. We’re really focused on that merchant context. Because we think that’s a massive block and a lot of what traditional fraud approaches and rewards programs are overly driven by like, how can we increase merchant information?
Peter Renton 14:13
Right, right. So 99.9%, that’s a pretty high percentage. You’re still a fairly young company, how are you able to do that? I’m just so curious to know that this is something that doesn’t sound like a trivial problem when I’m just thinking about it.
Oban MacTavish 14:31
So I’ve thought about this. And I think we were uniquely lucky in that, oftentimes, I think problems are best solved by small companies who pose no threat to anybody.
Oban MacTavish 14:41
I think sometimes when you’re big it becomes very difficult to solve data problems, because there’s always some type of competition. There are places you can acquire data, whether that be like someone who normally sells data, someone who doesn’t normally sell data for your use cases. But it becomes very compelling to a person to say, “hey, like, who are we? We’re a small company, we’ve raised almost no money,” you know, you can get yourself into these sort of high leverage situations where you don’t pose a threat to anybody. And you become in some ways a category defining use case for this data. And whether that be people like POI data providers, data from within the payments value chain itself, data from businesses who collect merchant data and are a place that merchants go to put information about themselves, state registries. The reality is that taking it all in and finding that data was hard, but the real challenge, I think what we did that was unique was the ability to stick it all together in a way that’s digestible infrastructure, that’s performant. And I wish I could be like, “oh, you know, it was this like, special trick,” but really, it was just like, banging on every door and asking everyone I knew and hustling and trying to find someone who knew somebody who knew somebody and finding the right person at that organization, who was saying, “yeah, you know what, okay, we’ll sign a deal, we’ll sign it for a long time, and you know we’re not going to worry about it.”
Peter Renton 14:41
Right.
Peter Renton 15:56
This is obviously a very dynamic database, there’s businesses being opened up every day, there’s businesses shutting down every day.
Oban MacTavish 16:02
Yeah.
Peter Renton 16:02
Is this like an API driven process where you’re updating the merchant database in real time?
Oban MacTavish 16:09
Well, we update it constantly, but it’s not API driven. So one of the approaches we took was that many of these businesses that we worked with, API’s weren’t the first choice of delivery, there was flat files. And then additionally, leveraging other people’s API’s sort of puts in points of failure. And if you look at these very robust systems, even bank tech stacks, like most of the time, your bank actually doesn’t just go down, you know what I mean?
Peter Renton 16:35
Right.
Oban MacTavish 16:36
Because there’s very few points of failure, they oftentimes have a mainframe sitting somewhere, all the technology is in one place, it’s, you know, they’re not calling out to API’s all the time. And so we don’t leverage any API’s in any of our enrichment. We have our own database, we receive flat files of data, and we process it all in-house. And then there’s no other points of failure beyond our own infrastructure. So we’re constantly updating the data, whether that be from data vendors, whether that be our own data sources, whether that be feedback from customers, and we’re so lucky to have customers who send us information about their transactions, whether we get it right or wrong, and say, “hey, this is where this actually took place”. And we can fix that. And it’s funny, I think we probably have the most accurate latitudes and longitudes of businesses on the planet. There’s often like, Google Maps says one thing, and we’re like, we know that’s not true, we’ve actually gone back to our customers and been like, I promise you, this is wrong. I know an analyst on your team pulled up Google Maps and said Spade was wrong, but I promise you, we have like truck data, we know where the truck physically was. And it was on top of this pin. And that’s like, a very unique business to be in.
Peter Renton 17:40
Okay, so then, can you explain who are you selling to? And what are you actually selling? Maybe take us through some of the use cases.
Oban MacTavish 17:48
So we can sell to anyone who issues a card, or really provide services to people who issue cards. So that means it could be banks, fintech companies, those are probably the two buckets but think about anyone who issues a card. In terms of what we do we provide an API that allows you to send us the bad data, and we send you good data and insight. So there’s both firmographic data, things like factual information about a business, where does it exist? What do they do? How long have they been around? And all sorts of facts like that. And then there’s insights-driven things, which is a new area for us, where we’re starting to provide our customers generally risk-focused insights, like, how long has the business been around and been operating? You know, do they have a long history of accepting payments? What’s their website say? Is their website covered in stuff that makes it really correlated with disputes? So there’s those two buckets, it’s a very simple API: you send us data, we send you stuff back in real time instantaneously. And then in terms of customers, I mean, we work with people like S&P 500 companies like Corpay, or I guess, formerly known as FLEETCOR. They do a lot of work in the logistics and trucking space, they also do corporate cars, and a whole host of other issuing types of use cases. And then we work with some people like Stripe, which is obviously a really exciting person to be working with. And then we work with some up and coming fintech people who are doing really great in their own right, people like Mercury, Unit, and others. So we really run the gamut of if you issue a card, you probably need better data, and you should be working with us.
Peter Renton 19:16
Is fraud your number one use case? Or what are the top use cases?
Oban MacTavish 19:21
So I like to bucket them into a couple places. There’s authorization, attribution, and then everything else. And fraud generally falls into the authorization side. So it’s by far our largest use case for our customers is whether that be you want to run a fraud model and you’re plugging in a bunch of data into a model and the model is spitting out Yes/No, whether that be in the trucking space, geographic-based authorization, where you, in real time, compare truck GPS location data, or driver location data to where Spade is telling you this business is located. So you just measure the distance. So you know for a fact the driver is there, the truck is there. That’s an incredible superpower. That stops account takeover, someone stealing someone’s card. It stops drivers potentially abusing their fuel card and fueling up their own personal vehicles. And that’s a big use case for us as well. Fraud usually falls into that bucket. And then there’s attribution, think like rewards programs and merchant payouts. So, rewards are becoming increasingly complicated and the exciting part about it is that as a consumer, you can do all sorts of stuff. I think everyone loves Chase and AmEx, they are really on the cutting edge where you click on your web portal, and you’re like, I want 5% cashback at this or I want $20 back here. Everyone has those stories of that not working. And that’s not because your bank is trying to scam you. It’s because your bank genuinely doesn’t know whether or not you’ve transacted with that merchant, which is a crazy idea. So we help our customers have very accurate rewards rebate programs with merchants, whether that be helping them say, “hey, this is cash back, or, hey, this merchant owes me money”. Because you know, whether it be things like, you know, BNPL or other use cases where you’re essentially driving volume towards merchants, they owe you based on transaction volume, you push your way, and they’re not just gonna pay you. So you have to prove that to them. And that’s a big use case for us as well. And lastly, user experience, sort of unique one off products. AI and analytics really fall into this bucket of whether you want to put a logo on your transaction feed or whether you want to do real time P&L categorization, there’s a whole host of use cases like that, that are really product driven. Or maybe you want to allow your customer to block certain merchants and only allow a card being spent on place. We don’t like to put that in the bucket with fraud. But you know, it’s very much product driven. We work really closely with product teams to execute there as well.
Peter Renton 21:38
What about the companies that are doing this categorization? Like you mentioned Mint, which obviously is not really around any more. Rocket Money does a does a pretty decent job. And there’s lots of these different companies, Personal Capital or Empower now.
Oban MacTavish 21:52
Yeah.
Peter Renton 21:53
Are you helping those types of companies? Because it’s, as you say, it’s not easy to categorize this stuff properly?
Oban MacTavish 22:00
We aren’t. I think one of the challenges as a startup is always the choosing; choose the biggest opportunities and tackle what you can. And I think, in the future, we’d love to work with people like that. I think the reality is economics become quite restrictive in those businesses. And it creates a dynamic where I think it becomes quite a commoditized product. And if all you’re really doing for someone is helping them categorize like, let’s call it 20% better, 10% better, they’re like, “yeah, what’s the ROI on that?” Versus I go to a bank, and I’m like, “hey, I’ll help you stop 10%, 15%, 20% more fraud. The economic value you created is just enormous. And I think the other part of that is many of these businesses rely on infrastructure players like Plaid, or MX or Finicity, who’ve built walled gardens. Like I think they’ve built an incredible product. But they’ve made decisions to essentially limit the data that’s being shared and the cleanliness, and they have their own enrichment products. And they’ve made decisions that they’re essentially going to be restricting the raw data from leaving their four walls.
Peter Renton 23:04
Right. Unless you pay.
Oban MacTavish 23:07
Yeah, exactly. And that just means that for vendors like us who rely on that raw data, it means we can’t work with those customers. And I hope one day to do that and be in an environment where, you know, that’s why I think I’m really excited about some of the open banking grid coming down the pipeline, because my hope would be that with common sense regulation of an industry like this, that the data being shared will be the raw data, and it will help open up that market for us in a really exciting way because we’re not going to rely on a party who is incentivized to prevent competitive products from being able to operate within their own market?
Peter Renton 23:39
No, I could see that could be a huge unlock for you guys.
Oban MacTavish 23:42
Absolutely.
Peter Renton 23:43
Maybe you can explain what you’re doing with Stripe exactly. What’s the basic deal there?
Oban MacTavish 23:48
Unfortunately, I actually can’t.
Peter Renton 23:50
You can’t, okay.
Oban MacTavish 23:51
I’m happy to talk about Corpay or a number of other customers. But as of right now, we can’t dive too deep into that. But more to come from that side, unfortunately.
Peter Renton 24:00
Okay, Corpay, what are you doing for those guys?
Oban MacTavish 24:02
So I think Corpay is one of the most interesting because they have a very broad number of use cases. Their organization is sprawling, they offer both cards designed for fuel corporate spend, they have a sprawling network of merchants. And I think one of the big things we would help them with and work with them on is on the attribution side and helping people like Corpay better understand where their customers are spending. Such that they can provide rewards accurately and ensure that their customers are essentially getting the rebates and things like that, that they rely on. Because the low quality of this data makes situations very difficult. I think they do $120 billion dollars of payments a year. It’s an enormous pool of data and trying to figure out which merchant all these transactions belongs to to ensure that everyone is properly getting the discount that they’re owed is an incredibly challenging problem and one we’re really excited to be working with, an incredibly large company, to help solve. On top of that, there’s also very exciting opportunities and things like proximity authorization, there’s different parts of their infrastructure where they leverage business data to present opportunities of where you should be spending Gas. And that’s an area I know they’re excited about innovating and with an organization like Corpay, they really have both the fraud prevention universe and the proximity authorization as well as things on the on the actual attribution side where we’re able to help find revenue, we’re able to help ensure that your customer is getting that experience they were promised. I think they’re a great example of why the enterprise is such an exciting place for us to work because we’re a trusted data partner. When you’re a big enterprise, the question is often build versus buy. And if you have essentially almost unlimited money, you can hire a lot of people, you have a lot of dollars to move. The question is who’s the partner I can trust to help me deploy quickly, get a product in market and provide a best in class service. And that’s really what we provide for them across a variety of use cases.
Peter Renton 24:24
And what about the the merchant acquirers? Because you said, you really focus on the merchant side of the data. There’s all sorts of analytics that could help drive their business or whatever. What are you doing on that side?
Oban MacTavish 26:16
I’m glad you asked. I think it’s the area I get really excited about in the future of Spade, I think we’re building something very unique. And then we’re building a pipe, an alternative pipe, directly into the authorization decision of a bank. And we’re already working with some folks in the acquiring universe, I can’t say who to help transport data to improve the merchant outcome. Essentially, say, “hey, how can we improve authorization rates for your merchants? Is there data we can share? Are there novel products that Spade can build to help your merchants get better outcomes on the bank side?” Because oftentimes, the actual the people in the acquiring side have a lot more information, they just don’t have a way to share it. And we work really closely with some folks there, to take that data transport in the ?? and improve authorization outcomes. And I don’t want to get into that, you know the acquiring universe is enormous. And there’s countless things we could do there. But I think we’re really excited to play an in-between the issuing and the acquiring universe in a really unique way. I don’t know if I want to build another competitive KYB product, there’s a lot of people doing that. And it’s a huge opportunity. But I think we’re really excited about playing in the middle here and saying, “hey, some big merchants have really muscled their way into relationships with issuers that have been super accretive to the bottom line by saying we can increase authorization rates, we can reduce false declines, we can reduce disputes. And I think there’s a world where every merchant should have the right to make sure that data, if you’re a high quality merchant, that should be recognized by the issuing bank. You shouldn’t be playing catch up just because you’re a mom and pop shop who just opened e-commerce. If you’ve been processing payments on your same WorldPay POS system that’s been around and gone through all those acquisitions and take privates, you should get credit for that. You’re not as risky as a random e-commerce business who popped up with a website that was generated yesterday. And banks just aren’t aware. And I think we can play a really valuable role in helping these merchants increase acceptance and reduce false declines, because that really hurts smaller businesses.
Peter Renton 28:12
Right, so you’re saying that right now the status quo is that the established merchant processing payments of 30 years versus the guy who launched a system yesterday, and they’re starting to process payments — they could be legitimate, obviously, lots of business to get started every day. But you’re saying right now that there’s no differentiation between those two types of businesses?
Oban MacTavish 28:31
I think there’s some level of differentiation. But it’s clearly not enough. Because I think everyone who’s ever spun up a POS system and tried to accept payments via card has higher decline rates. I think decline rates in certain industries are higher. And it’s actually sprung up a whole world of payment optimization platforms who are really in an arms race with banks trying to trick them, maybe it’s, what if we tried a different MCC code? What if we tried a new MID? We’re going to put the good customers on this MID and the bad customers on this MID. That’s like trying to trick someone to let you in the door. And I think one of the challenges is that there is no effective and consistent standard to allow for broader communication, that’s actually wide enough to allow you to stick more information in there that you wish you could send, because otherwise, the bank won’t even see it. And I think the problem is, is that even if a bank has never seen a merchant ID like a MID before, they don’t know if it’s Target opening a new location in New York, or if it’s a mom and pop shop. They don’t know that. They can look at a descriptor, but there’s no control over what you put in those 45 characters. And the reality is that you’re being treated the same way. And I think that is just such a fundamental problem that there isn’t a way to consistently create these IDs. And that’s why we actually created what we call a Counterparty ID, which is designed to be a permanent ID for a merchant regardless of what POS system they’re using and our customers. leverage that and say, “hey, this is Target, you can be super confident this is Target even if it’s the 50,001 merchant ID versus someone trying to pretend to be Target.
Peter Renton 30:10
So last question, then. You’ve touched on it, but I’d like you to just maybe spend a little bit more time on it. What’s your vision for Spade? Where are you taking it?
Peter Renton 30:18
Okay, well, we’ll have to leave it there. Really great to chat with you Oban. It’s a fascinating space, and you’re well on the way to being a major player here, so thanks again for coming on the show.
Oban MacTavish 30:18
I get really excited by the idea of helping merchants and banks improve their payment acceptance and increase authorization and really build an alternative to the ISO 8583. Not that you can replace the card networks. That’s not what I’m in the business of doing. You know, I think both Visa and MasterCard are built. They’re legendary businesses, they provide a real service for people. But I do think there needs to be room. And I almost think of it like there’s a big oil pipe, and there’s a parallel pipe that is smaller running next to it, that allows people, whether it be merchants, whether it be PSPs, to pass more information, to pass additional context to really help drive payment acceptance. And I really want to end up in a world where not only are we improving transactions with data that we’re bringing to the table, insights we’re bringing to the table, but there’s an opportunity for other people to contribute to that as well. And if you’re a merchant, you should be able to get credit for the work you’re doing for the fraud systems you’re leveraging, for the scoring you’re doing on your transactions to find the good guys. And if Spade is successful in the end, the only transactions that are being blocked are fraudulent transactions and all the good ones are being let in. And I think hopefully, we can play a really key role in ensuring that banks have the most accurate verified data on every single card payment. And if that’s coming from us, if that’s coming from merchants, or PSPs, or whomever there should be a place to do that, and I’d like that to be Spade.
Oban MacTavish 31:53
Thanks for having me, Peter. Really appreciate it.
Peter Renton 31:57
Well, I hope you enjoyed the show. Thank you so much for listening. Please go ahead and give the show a review on the podcast platform of your choice and go tell your friends and colleagues about it. Anyway, on that note, I will sign off. I very much appreciate your listening, and I’ll catch you next time. Bye.