Latest / The Payments Shed / Ep. 29 - AI, Data and the Entrepreneurial Edge with Dwayne Gefferie

Transcript

Ep. 29 - AI, Data and the Entrepreneurial Edge with Dwayne Gefferie · The Payments Shed

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Welcome to the Payment Shed Podcast, the weekly podcast that
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dives into the big topics, trends and people shaping the
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world's of payments, fintech and business leadership Of your two
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Co hosts, myself Grant Evans and Justin Hanna.
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Welcome to another episode of the Payment Shed Podcast.
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Today we're joined by Dwayne Gaffrey, founder at Gaffrey and
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payment strategist. Welcome Dwayne.
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We have working microphones for you this time.
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Thank you. Thank you.
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Appreciate it always, Always great to be back.
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Yeah, long time friend of the show.
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Obviously we had a bit of a technical clash you in Amsterdam
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last time we tried to do this. So we're we're back to give it
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another go. Thanks for coming in.
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Please do introduce yourself. Absolutely.
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Well, my name is Dwayne Jeffrey. Nowadays I'm a, I'm a payment
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strategist. Think I'm going to hyphen that
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and just sort of start adding data scientists to it as well.
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Because for the last 20-3 years, I've done everything from the
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data side, from data analysts all the way to VP of data
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science and analytics, ran a data science and analytics
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consultancy firm. But nowadays I, I spend most of
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my time really diving deep into payments companies, what's
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happening within payments and trying to make that much more
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digestible. One, through my LinkedIn
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content, which has grown quite significantly.
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And two, to my newsletter, the payment strategy breakdown,
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where I sort of like always try to take very complex topics and
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break them down for the audience.
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We have well over 7000 people now subscribe to it.
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So that's why I spend my time. And other than that, I advise a
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lot of different payment startups as well.
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And from time to time I take on some bigger consulting,
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consulting jobs as building like a new neo bank or trying to help
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a company really revolutionize their, their, their data
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strategy and and really figure things out.
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And you found the companies in the past and you know, you've
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built this reputation and, you know, as a, a beacon of
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knowledge in AI and data particularly.
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But what started you on that entrepreneurial journey to kind
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of step away from being, you know, employed?
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You've worked for Audi and, and payments companies such as that?
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Like what, what was the thing that trigger you to go?
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Do you know what? I'm going to go and do this on
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my own. Yeah, so it actually started way
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earlier, right? I'm, I'm one of those who at an
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early stage was always trying to figure out a way to make a
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couple of extra bucks right in the beginning was to to buy some
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candy or anything like that. And I think it was 1515 when I
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started my first company, which was actually an entertainment
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company where we were organizing high school events, what we
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called it multi high school events.
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So everybody from all the different high schools would
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come and we would just rent out a a club and then got some DJs
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and that became a business. And I ran there for about 3
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years before I sold it. Then I started a marketing
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company, sold that when I was 21.
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Then I even had a Domino's Pizza.
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So I had a lot of different entrepreneurial things
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throughout the years and my payments career has always been
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parallel to that. So it's always been
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entrepreneurial and having a, you know, a steady job or a car
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corporate job and stuff. And I think all of that sort of
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like MERS and came to an end when I left checkout.com back in
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2018-2019 and set up Databrite and that's sort of like got
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everything together and then spent most of my time now.
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So it's been ever since 2019 that I've been fully just
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entrepreneurial, not working at a company, just having my own
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company and not doing that. But it was the merging of those
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two paths into that one and and that's what I continue to do
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now, So. Lots of different industries
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there and obviously it sounds like success in most of them,
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which which is great. What do you think is the kind of
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the biggest challenge you've you found as a founder kind of doing
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it on your own? I think the biggest challenge
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for me is always one is finding great partners, right?
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And I think nowadays I would probably be much more critical
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and much more skeptical of who I get into business with because
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at that time it's who's your best mate, who's your best
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friend, who, who came up with the ID, right?
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And then you divide, you know, the stock 5050 and you said,
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let's just go for it only to figure out that you have the
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same skill set or you have, you know, you're not able to divide
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work the right way or you clash on certain things like when the
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money actually comes in and how should we invest in and how
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should we reinvest it? Should we hire people?
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Should we grow the business or should we pay ourselves a bigger
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dividend and stuff. So I think that's probably one
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of the most critical things with within that debt that I found.
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And how's your perspective developed over time and you
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think about building businesses? Well, I think you're, well, this
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sounds a little bit philosophical.
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No, not far philosophical, but I think my, the, the thing that
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really changed for me is that what am I trying to achieve in
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the long run, Right. So 5 to 10 years down the road,
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I think now I'm much more at a place where, yes, I could go and
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build a massive company, raise a lot of money, go at it and just
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really hunker down and stuff. But I always said like, OK, is
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this what I would truly want to do?
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Is it making me happy? Am I enjoying it?
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I now currently enjoy being able to have a lot of freedom to read
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a lot of stuff, to talk to a lot of different people, to not feel
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like the other side is saying, well, you know, we can't share
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information with him because he might, you know, take it and,
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and put it into his own business.
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So I like that part where I'm just much more free and, and I
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can do a lot of things. So I call myself a solo founder,
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solo advisor, solo consultant because I don't want anybody to
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have that perception that I'm trying to take anything away
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from them. I want to give as much, right?
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So through my writing, everything that I do, most of
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the things that I do are actually for free.
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And that creates the opportunities.
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Whereas, you know, if you, if you would build something and,
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and go raise capital, you're hunkered down, you focus on that
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and a lot of those opportunities disappear.
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So for me, it's really about what do I enjoy nice and giving
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me happiness? Am I able to combine things like
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travel and meeting people and networking and creating content
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and all of those other things? And does that does that fulfill
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me and stuff? And I'm definitely at a point
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where I'm like, it's great. But there's there's definitely
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more, but I'm I don't have to rush it.
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Nice talk. Not a day goes by where I don't
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see something around AI or data across the industry in the
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payment and fintechs world. When we speak to businesses from
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your perspective, where do you see AI having the biggest
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immediate impact in payments right now?
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I think it's productivity, right?
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I think we, we look at what some of the bigger companies are
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doing and what you're seeing of course, is they, they're doing
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amazing things. We have a foundational model and
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we can connect every data point from the browser all the way to
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settlement and stuff. But if you go look at it, a lot
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of those companies were on that journey 10 years ago, right?
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I started many of those journeys when I was at Adgen.
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We had a PostgreSQL database and I came up with a lot of those
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things where I had a lot of resistance.
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A lot of people were saying like, well, you know, it is all
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about data, but we don't need to, you know, get data breaks
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and all these other things in there.
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We just need to take it one step at a time.
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But those companies have had 10 years to build on top of that
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and, you know, brought in smarter people and, and, and,
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and engineers and other people to come into that.
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But I think what we're seeing right now is just that, yes, AI
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is everywhere, but productivity is probably the first thing to
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do. It's do you want to give of your
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staff, do you want to give the people that you're working with
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AI as a productivity tool? Why?
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Because if, if within the company, we can actually say,
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hey, if I'm writing emails, if I'm making proposals, if I'm
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doing a presentation, I can do all of that.
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But there are simple tools I can use a gamma for my presentation.
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I don't have to spend the next 3-4 hours making a presentation
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because I have a meeting tomorrow, right?
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You can and get a lot of productivity back by enabling
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that. And I think that's the first
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level. Go look at your engineers as
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well. I love like I'm, you know, I'm
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not really a developer, but I'm a data scientist who done a lot
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of you know, I programmed in R, which was a statistical language
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was much more about numbers, Python as well.
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But I've never really developed production level code that got
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published and stuff. But now with ChatGPT or codecs
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or with cursor and their cloud code, what you're seeing now is
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that even somebody like me who has the product mindset, who has
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the data science mindset, who knows how to work with data, I
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can come up with little things that I can create as little
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MVPS. They might not still be
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production ready. But instead of just having my ID
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sit there or having to write out a document and having to
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convince everybody, I can spend much more time on developing an
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MVP in a much shorter time frame by really being very focused
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focus and just giving it and then presenting that and saying,
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hey, this is my ID and this is my MVP for it.
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And there you go. And if people don't like it, we
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say, OK, let's develop this into an actual thing.
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So I think that will create more productivity with the product
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people and then the engineers let them enjoy or let them use a
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copilot. Like it doesn't matter if it's
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Microsoft or cloud code or codecs or anything like that.
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That's the first level. If we get a little bit of more
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productivity out of the people that we have, that's where it's
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gonna start. And then all of the other things
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will just be, yeah, compounding on that will be the next steps
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that we can go into. Do you think most financial
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institutions are still under utilizing the data that they
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have at their fingertips? And is AI helping them to unlock
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some of that additional data that maybe has been neglected
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previously to drive more value to, you know, the round table
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with a load of CF OS recently? And they were talking about how
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they use AI to help in their world.
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And you've got merchant application stuff, you've got
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obviously optimization of authorization rates.
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But like, is enough being done with that data still at this
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moment in time? Or is there a lot further we can
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go? I think there's a lot further
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that we can go absolutely because like I said, for some
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companies it's been a 10 year journey into doing that, right.
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That first foundation is actually saying, are we
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capturing enough data? And it's my sound like, well, we
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already have so much data, but are you capturing enough?
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A lot of the companies that I've worked with when I was doing a
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lot of this consulting, we were doing a lot of on premise into
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the cloud. And sometimes I had to break it
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all the way down to what we call events, right?
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Like the actual log events that were happening.
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And what we were noticing is that sometimes sixty, 7080% of
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that data was being discarded, right?
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We were only taking like key variables and then putting those
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into the database. But a lot of it was in the event
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log data. So at my times, I was always
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trying to figure out what is actually that data that we're
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not utilizing. Can we put that into a database?
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I went through that journey at Agen.
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I went through that journey at a company called Dybox where we
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were doing this for other parties.
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I was doing that at checkout.com.
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And I believe that a lot of companies are still discarding a
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lot of that data, One, because they don't always own their own
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technology stack. So they're getting it from a
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third party and that third parties like this is all you
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need, but you don't know what's the raw data that I'm getting
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from a Monster guard or from a Visa or from an APM.
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Oi think that first step is you need to take control not just of
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your data, but also of that log data.
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And then go figure that out. And now with all of these
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machine learning capabilities that we have and the fact that
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if you're in the cloud, you can go and say, give me all that raw
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data, give me the petabytes of data.
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And now let me go figure out if there's things within that data
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that can actually make sense that we can help and optimize
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with. So I think that's the first
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foundation really getting control of your data,
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understanding how much data is actually there and should you
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enrich it, right? Should you be getting more
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browser based data? Should you be getting more log
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data in there? But we hear this all the time,
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right? And I especially the last few
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months is let's just get the AI to increase our conversion and
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that you mentioned before, optimize authorization rates.
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Like it's just this thing that you switch on and all of a
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sudden you, you can increase your acceptance rates.
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And I was actually having a conversation this morning with
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the guy whose boss said to him, right, let's get that AI and
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then we can increase conversion and we can charge our customers
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a little bit more. That was the exact conversation
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he had. But they've been mis sold
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though. Like there's so many AI
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companies out there, they're making it seem like it's so easy
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just to plug them in and it solves every problem you could
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ever. Imagine like Visa Mascot
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probably would have been looking at this already if we could just
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remove certain decline codes to increase conversion just like
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that I think. For me, it's more of an enabler
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for the orchestration layers that already exists and how they
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probably route traffic and do that in a smart way.
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Because, you know, we've got this economy now where, you
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know, smaller merchants and previously would have had
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multiple payment relationships, right?
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Because they've got the ability to do that for orchestration
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platforms now. And that for me seems like the
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greatest foundation point for utilizing AI that says this
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transaction originated here. We know we got a better success
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rate from the specific issuer with this acquire or whatever it
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might be. But like building that kind of
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flow into the the payment ecosystem seems like a positive
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way to go from I because to get your take on orchestration
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platforms won't really. Data Sciences.
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But I think I think the first step that everybody needs to
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understand is that every single company that's involved within
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payments, the issuer, the acquirer, the PSP, the fraud
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engines, because you're building technology and you're hopefully
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adding value to whatever that chain is.
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You're building your own code, you're building your own
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software and that creates, you know, the by product of that is
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data. And what happens is that all of
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those companies have their own databases and they store all
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that database. So a lot of them are seeing that
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as their mode. This is our mode, right?
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This is what keeps makes a special.
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But because everything is actually in data silos, you
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never fully get the the 360 pick.
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You never get the full story. So what you're seeing right now
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and like are just launched like their payments optimization
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platform, which is interesting because they're saying we have
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data that if we share that with you, you can make better
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decisions. So you have the likes of an Ad
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General will pay or a Neopay or those guys, they're
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collaborating because they're like a little bit more extra
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information will help us to to boost it up.
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The numbers look great, 9 to 15%.
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I'm like, OK, that's truly amazing.
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But then look at all of those other points where, where is
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data being left behind or created?
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That's in those data silos. So when a company says, hey, we
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should just plug this in, what you're basically saying is we
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have our own way and hopefully we're doing that in the best
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possible way to get as much optimization as possible.
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But it is true that if we can combine that with other sources
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of data and we can hopefully provide some smart AI
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capabilities, machine learning, whatever you need to do to then
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boost that authorization rate, that's where you're going to get
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the benefit. But we're also in a space where
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everybody's competing with each other.
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We're trying to be much more friendly.
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We're trying to create some partnerships, but people are
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still combining that. And it's also your own merchant
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portfolio, right? Your base of what you have is
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going to be different than the set up of the next company and
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the other companies. So doesn't matter what works for
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them, you still have to figure out what works for you, and
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that's what you need to focus. On for it to be truly
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transformative, the AI to really, really do it's job over
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the next kind of four or five years, what's the one thing you
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would say needs to be able to do?
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Standardization, I think standardization for me is
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probably the key, right? And it's something that
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everybody keeps on in avoiding is that one like even even this
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like I'm having a lot of conversations around payments
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protocols and you have different companies coming up with
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different protocols approaching different things, especially in
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the agentic commerce part. But if you go back 20-30 years
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ago, the way that transactions are being processed online as
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well as through the terminals are all based on ISO protocols
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that have actually defined the standards within the industry.
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Doesn't matter if it's card based, banking, bank transfers,
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ACH, all of those different things.
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But what we're now getting to is a place where everybody's again,
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very protective of their own data and the way that they look
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at data and how they categorize data.
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So a lot of the conversations that I'm having with especially
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young startups is saying, look, you need to unify that data.
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You need to create your own modeling layer on top of it to
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say, I know what this company is saying, which is the same as
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this company. They just have different names
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for it or they just have a different format from it.
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So I think there should be a place where we can say, hey, can
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we create more standards that everybody adopts to right?
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So that if we do get orchestrated, so we get other
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companies who are infusing or taking that data to themselves.
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They don't have to spend the next time figuring out how to
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extract, transform, loaded and and match it with other things
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before we can even get the value out of it.
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So things standardization across the industry just from a
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terminology perspective, the way that we're labeling data and
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what we can do with it would benefit everybody because then
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we can all get not just the productivity, but also the
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insights that we're all looking for and.
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What are the biggest misconceptions that people have
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about AI and fintech at the moment in your opinion?
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I think that is going to be plug and play, right?
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I think that if we really look at the research that large
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companies are doing, like the Mckinsey's, the BCG's, right,
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all of them, of course are trying to convince the fintechs
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and the financial service companies and the payments
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companies to adopt AI. What we're seeing is that the
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conversations are definitely taking place on AC level, right?
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I think seventy, 8090% numbers is what I've seen of people
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saying AI is going to have this transformational impact on
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payments or on financial services.
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But then you go look at what's actually happening and it's only
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at a very low level, you know, 345 percent of companies
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actually adopting that. So I think that's where we need
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to to, to get to a level where we say, OK, it's great to have
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the conversation. But again, are you in a place
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where you can actually apply AI on, on your business, right?
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Are you there, are you in a place where your data stored
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properly? Are you, do you have that in a
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data warehouse? You can't do it on everything
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all at the same time. But what's a use case that you
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can tackle with AI right now? Is it onboarding?
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Is it compliance, right? What are some of the simple
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things that you can can have so that you can convince the rest
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of the company this is working and this is how we continue to
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go forward. But I also think that the, the
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biggest challenge within that, and you know, to, to give credit
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to a lot of the sea levels is that we're in an industry where
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we have so much regulation around us that it is not as easy
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to be plug and play. But the pitch to us from the AI
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companies is that it's plug and play.
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Just plug in this LLM, just plug in just, you know, just this
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little AI model and we'll be fine.
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And that's not the case. You need to really be careful
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about how you build things out, how you leverage the data.
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And, and we deal with all of the regulatory things from GDPR,
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from APCI to, you know, all of those standards that you really
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have to, to work with so that you're allowed to work with that
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data and actually get those type of insights.
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And then risk factors that you mentioned there, obviously, are
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there sort of ethical concerns that exist with AI as well that
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particularly payments companies and fintechs need to bear in
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mind when they're telling their end customers, hey, we're using
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AI? Well, I don't know, big ethical
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things come to mind for me, right?
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I think, of course, everybody's making a lot of statements now,
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right? This much uplift and we're doing
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this and we're protecting you and, and all of those other
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things. There's nobody out there
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verifying or checking if that's actually true.
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But in a data sense, right, because people are precious
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about their data, particularly in the UK, right, we have a real
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thing about sharing our data or information and financial
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information is an even more to be thing, right.
19:51
Like what are you doing with my card details, right, my bank
19:54
details, all that sort of thing. If you're then saying, well of
19:57
letting AI access this because it can do this, this and this
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and help you get better rates here and do this, but you're
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ultimately sharing that data with something that there is a
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bit of distrust about for certain people.
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Absolutely. And I think companies should
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definitely be much more forward about sharing how they're doing
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that. I think we're getting into the
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space where proprietary models and all of those other things
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are overlapped now with open source, right?
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Like, hey, we should have this open source.
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I still think that a lot of people might not necessarily
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understand what's going on there, but you do have people
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who do understand it, who can explain it and can share what
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that is. Yeah, I think for me that
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question has always been very challenging.
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And because it's who actually owns the data, right.
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Like when you do a transaction and you're you're tapping your
20:45
card details, we have things like tokenization,
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anonymization, pseudo anonymization, all of those
20:51
different things that are protecting us because we have
20:53
the things like the GDPRS and the PC is of the world
20:56
protecting us. But then at the end of the day,
20:59
we're all looking for that result.
21:00
If I'm doing a payment, if I have the funds or I have the
21:03
credit available, I want the transaction to go through.
21:05
If it's me, then there's the obligation from the merchant and
21:09
the process to make sure that fraud doesn't happen or that I'm
21:12
protected against that front. So I'm always very yeah, who
21:16
owns that data? I think once you tap it, it's a
21:18
collaborative thing. You should share that
21:20
information. You can have terms and
21:22
conditions. You should from time to time
21:24
maybe educate the audience and the consumers on what that what
21:28
that needs to be. But I think we we all need to
21:31
see what are we trying to achieve.
21:33
And as long as we're trying to achieve that and we're open
21:36
about how we're achieving that, I think with AI, you just open
21:38
up this another conversation is that the more AI you apply, the
21:44
more it becomes this black box that we need explainable AI to
21:48
explain what is actually happening.
21:49
And I think that that just becomes a challenge on itself.
21:52
It feels like sometimes with AI that, that people don't fully
21:55
know how to use it just yet, right?
21:57
So here's all this AI and, and that's probably a risk because
22:00
people don't know what to do with it.
22:01
And then you mentioned before, so people are maybe not using it
22:04
properly. So you've got 100% of people
22:07
that have been given the ability to use AI, but only 5% are using
22:09
it correctly. The risk that, as you mentioned,
22:12
from an ethical perspective is the other 95% may be using it in
22:15
the wrong way. And that's then what we need
22:17
standardization really, is to make sure that there is a level
22:20
playing field, to make sure that no one's kind of overstepping
22:23
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Yeah. I think what we're seeing now or
23:01
the way that I'm looking at AI and how it's getting into the
23:05
consumers hand, right? So I think if we look at
23:07
companies who are succeeding on the consumer side of it, like
23:09
for me, open AI is probably one of the best examples.
23:12
Like especially the launch of SORA, right?
23:14
Where there used to be a time that if you wanted to have, you
23:18
had a creative ID, right? It was very difficult to bring
23:22
that to life because even if you wanted to make a movie or you
23:24
wanted to make like you wanted to make something, I was like,
23:27
oh, I got to go get a production, I got to go get
23:28
cameras, I got to do all of these different things.
23:31
I think what we're now seeing is that the consumer side of it
23:33
doesn't matter if it's a chat TBT or a Claude or a Sora, is
23:37
that whatever I can think of, whatever that ID is, whatever I
23:40
want to create from a text perspective, from an image
23:43
perspective, from a video perspective, where we're putting
23:46
AI in the hands of consumers who are saying I can get my IDs, my
23:50
productivity, all of those things I can put in there and it
23:53
comes out of it. I don't know how it works, but I
23:55
know that the end result is something that I'm I'm really
23:58
interested in and I definitely want to do more, right?
24:01
Like even me, like I'm, I'm my, my, I've been, I've been going
24:05
down this rabbit hole of AI music.
24:09
I love music. And for me it's that I've, I've
24:12
found a couple of channels who are taking certain aspects of
24:16
music and creating new AI music. And I think it's fascinating
24:19
that I even like the songs. I'm like, this should be an
24:21
actual song. So I think a lot of consumers
24:24
are consuming it in that way. It's like, hey, it's doing
24:27
something for me. I'm not going to judge it.
24:29
I think we still need to have those people who are going to be
24:32
critical of that. What am I putting in is that
24:35
being protected? Who's auditing the the AI
24:38
companies not to misuse it in that particular way, But we're
24:42
also getting insights that we've never seen about the creativity
24:45
and the things that people do want to see.
24:47
And I think that's sort of like a, a continuous conversation,
24:50
but also something where we need to need to make sure that maybe
24:52
we need we need ethical standards, Maybe we need to have
24:55
somebody govern it that. Is is regulation actually
24:59
keeping pace with the speed at which AI is developing?
25:03
Because you need to. We're auditing it and managing
25:05
in that way, but if if the regulation can't keep up then
25:08
we're we're heading into a slightly grey area, right?
25:11
Yeah. So I think what you're seeing
25:12
now is that the AI companies are trying to like, I think Entropic
25:17
is really one of those companies is like we're ethical, right.
25:19
Like they're really pushing that as in, as in go and let the
25:22
research is ethical and stuff. And I think, you know, we've
25:25
seen times where we've looked at the big companies like the, the
25:29
Fang, right, the, the Facebook, the Amazons and, and all of
25:33
those. And, and we've looked at them
25:34
and said like they're just doing everything for a profit and
25:36
they're trying to get to that point.
25:39
So of course, what we would perceive as ethical, they're not
25:42
doing at this point. I think at this level, because
25:47
AI is going at such a rapid pace, they need to be, they need
25:51
to be as transparent as possible.
25:54
Published models, probably that research, I think a lot of them
25:57
are doing that. Then we need to have people who
25:59
are actually reading that and providing commentary on that and
26:02
pushing back on it. But I don't think that it needs
26:06
to be, I don't think we need to over regulate it because
26:08
especially in Europe, we see that we're we're stifling
26:12
innovation by regulating things before they even happen, right?
26:15
Something comes out what we have to regulate it.
26:17
And I think we we need to find a better way of working together
26:21
between regulation and ethics and the innovation of the
26:25
companies. But I think AI is moving at such
26:28
a pace that doesn't matter what type of regulation you want to
26:30
throw at it, it's it's not going to be able to keep up at the
26:33
moment. Yeah.
26:34
And by the time you kind of got that regulation, AI is already
26:36
ahead of it, right? Then we're going to end up using
26:38
AI to help us build out and create a standard on how we use
26:42
AI. Absolutely.
26:44
It's kind of that chicken egg situation.
26:46
So we talk about obviously your experience, Dwayne, when it
26:49
comes from being a founder and living that founder life, kind
26:53
of in your journey, kind of what's been your proudest moment
26:56
you think as a founder? Oh, I think for me, my proudest
27:03
moment is getting up every time I fall down.
27:08
So I think whenever you talk about being a founder or having
27:11
set up different companies, I'm definitely not at a level where
27:14
I'm like, oh, you know, guys, I'm, I'm fully done.
27:17
I'm retired. I'm, I'm a billionaire nowhere
27:19
near that right? I've, I make mistakes every
27:22
single day. I, I don't invest in the
27:24
companies that I should have invested in.
27:27
I, I don't stick with things for, for the longest period of
27:30
time. I'm, I'm great at starting up
27:31
things and trying to figure out that zero to 1.
27:34
I love that particular space. But as I've been a part of
27:38
companies who are now worth 30, forty $50 billion and stuff.
27:41
So I, I have a nose for it as in where that's going, but it
27:46
doesn't necessarily mean that I'm successful.
27:48
But I think for me, what I'm most proud of is that even when
27:53
things get hard or even when things fall down or even when
27:56
you feel at something and I feel you can be different things,
27:58
right? I can have a company, we've made
28:00
a lot of money, but somebody else did it and they've made 100
28:04
times more money. It's like, have I filled?
28:05
No, I've been successful at my own thing, but it's not giving
28:08
up. I think even at this point is
28:11
that I've sold my previous company and I'm still winding
28:14
down a lot of different things around it.
28:16
But I don't feel like I've been I've succeeded in life or within
28:21
business. So I always feel like even
28:23
though I consider that a success, it could have been more
28:25
successful. But also, do I should I focus my
28:29
effort on different things? So I continue to get back up.
28:31
I continue to build new things. I continue to push myself just
28:36
by being a perpetual learner and just being somebody was always
28:38
looking for what's that next edge?
28:40
Because I think as long as you're alive and you're doing
28:43
great things and you're innovating and you're, you're,
28:45
you're challenging yourself and you're building things, I think
28:48
that's the probably the, the proudest moment.
28:49
So there, there isn't a, a check that has come in, there isn't a,
28:54
a deal that I've closed. And I'm like, oh, that's no, a
28:56
much more like all of the successful companies, quote UN
29:00
quote, successful companies, the companies who have made money,
29:02
have made a profit. I consider that a success.
29:04
Absolutely. But there's also been a lot of
29:06
companies that have filled in the in the meantime that haven't
29:09
gone off that that were never successful, that never close the
29:12
client that I had to shut down. And what's the hardest call that
29:15
you've had to make in that journey around that then?
29:18
Has it been a moment where you've had to make a really,
29:19
really tough decision? Won't be the one that kind of
29:22
sticks in your mind. I.
29:24
Would probably say, well, letting go of the the last
29:31
company was what was probably one of the hardest decisions.
29:36
So the previous wave of what we had with this whole data thing
29:40
was I called it going from on premise into the cloud, right?
29:44
So that was the the previous wave that we had where we had
29:46
all of these, especially within payments and within finance.
29:49
A lot of the servers were in the backroom, right?
29:51
Or it was on a we had an actual server room and a lot of
29:54
companies were working on that and especially in payments,
29:57
right? I can tell you stories of, of
29:59
payments companies that it was really just a rack in the back
30:01
of the room. And hopefully the Internet
30:03
doesn't go down and stuff, but that migration from building
30:08
things on premise into the cloud and helping companies on that
30:11
journey was definitely a great journey.
30:13
And through all of that, I was always telling companies about
30:17
the data science maturity model. That was one of the things that
30:19
I didn't invent it, but I was really sort of like, hey, you're
30:22
here right now. You want to be AI driven, you
30:25
want to have all of that. So the vision was there.
30:27
I was considering myself a data scientist way before the word
30:30
was even popularized and stuff. But then eventually you get to a
30:34
point where you've done a lot of those things and then a chat TBT
30:37
comes out and I'm not saying I didn't take it seriously, but
30:40
there was a new wave coming. And then you're figuring out is
30:43
do I stick on this wave or do I go on to the next wave?
30:46
And I could have easily taken that company and said, we're
30:49
just going to ride the next wave.
30:50
I'm just going to take that over.
30:51
And we're not going to do on Prem to cloud migration anymore.
30:54
We're only going to focus from, you know, how do you build on
30:56
that data warehouse and build AI stuff?
30:58
And I think letting go of that company when we got the offer
31:01
from like, Hey, we want to, we want to buy your company.
31:05
It was that was probably the hardest thing where we said, OK,
31:08
if we let this go, do does that mean letting go of everything?
31:11
And I think what helped me through all of that was actually
31:13
the fact that I said, OK, I'm going to sell it.
31:16
We ended up signing the deal. And then I started writing about
31:20
everything that I've done in the last 20-30 years, mostly 20
31:23
years about everything that I've done in payments.
31:26
And that was sort of like my cathartic process to deal with
31:29
all of that. And that led to the new
31:32
opportunity. So yeah.
31:34
How do you balance kind of that vision?
31:37
You mentioned that, but you want to see the future alongside
31:40
executing what you're currently trying to do now.
31:45
I think having for me, having a vision, I don't want to say that
31:50
I'm, I'm not one of those thinkers that I would sit down
31:52
and I have the vision. I think what we see now is that
31:55
there's so many great thinkers, There's so many, so many smart
31:58
in individuals who are now more open.
32:02
I know that's the right way, but they're open in a way that they
32:06
weren't open before. So if I think of an Elon Musk or
32:11
I think of a Sam Altman over, I think of the the people that we
32:14
now look up to as far as sort of like the geniuses of our
32:17
generation go thing back in the 80s and in the 90s and in the
32:23
2000s and stuff. Who were those people and were
32:25
they sharing at that level? Absolutely.
32:27
Not it's not building public thing, right?
32:29
Yeah, building public, right. Bill Gates wasn't having a, a, a
32:32
podcast. And it was like, hey, you guys,
32:34
we're building here at Microsoft or Steve Jobs wasn't doing it.
32:37
We, you know, like 20 years later, you hear the stories
32:40
about how Steve was working and his colleagues are telling all
32:43
of these other things and you're like, oh, you know, you hear all
32:45
of those things. But now we have the smartest
32:47
people building things, amazing things, launching those things
32:52
and then sharing what they've built or even open sourcing it
32:56
or giving away the patents like with the Elon I has done so that
32:59
others can build and actually become more innovative and
33:02
stuff. So for me, creating a vision is
33:04
not about convincing myself that I'm this.
33:07
Absolutely not. I'm just reading a lot.
33:10
I'm just seeing what others are doing.
33:11
I'm researching a lot and then I say, OK, through all of that,
33:15
what's my framework? What are the things that I think
33:18
I have enough experience in that I wanted to.
33:21
So what's my lens? And I look at that payments is
33:24
definitely that one lens. Again, not the smartest person
33:27
in payments, not by far. I can name you 50 people who are
33:30
way better at at the payments team, but payments is something
33:33
that I love and that I've enjoyed.
33:35
Strategy is one of those things that I've I've always loved and
33:39
always enjoyed, but I've had to learn how to, how do you
33:43
communicate strategy? How do you talk about that?
33:46
And then data and AI is also something that I love.
33:48
So I have these three lenses where I can go through and I can
33:51
say, OK, in the middle, where does the payment slash strategy
33:55
slash AI things come in? And if I would talk to myself
34:00
and I would explain it to myself, how would I do it?
34:02
So when I write about it, when I post about it, it's not even
34:06
about the audience, right? It's not saying, oh, I think my
34:08
audience need this. No, I'm saying I'm trying to
34:10
figure out tokenization. I'm trying to figure out agentic
34:12
commerce. I'm trying to figure out stable
34:15
coin infrastructure. Therefore, I read and and study
34:19
what others are doing and I'm summarizing it for myself.
34:22
And then I'm saying, OK, how do I feel about this?
34:24
Or it's not even about how do I feel?
34:26
What do I think it's going to happen in the future with this?
34:29
And then I communicate that again for myself more than for
34:32
the for the audience because I'm not trying to preach or say,
34:36
hey, this is what it's going to be.
34:38
But I'm sharing that. And then others can then take
34:40
from that whether I can be a part of their research.
34:43
But I'm expecting others to take it and then have their own
34:46
opinions about that. And we can have great
34:48
conversations about it. You see it?
34:49
Quite often though, is that you may have an individual that is
34:52
incredibly intelligent and they have a fantastic strategy in the
34:55
head where the business ends up maybe not performance where they
34:59
want the way they want it to be formed is they failed to
35:02
communicate that strategy, right?
35:04
And you have good products, but if you're not bringing in,
35:06
sometimes you need a team around you to help get that product out
35:08
there in the right way. The business is never really
35:11
going to reach that full potential.
35:12
Absolutely, Yeah, I think those are those are skills that
35:18
nowadays you need more than every than anything, right?
35:21
It's I think you guys do an amazing job.
35:23
It's like, how do you communicate not just the
35:25
conversations, but the thing, how do you dissect it and how do
35:28
you share that, right? It's are you able to take an ID?
35:32
Doesn't matter if it's your ID or somebody else's ID.
35:34
Are you able to take an ID, break it down to pieces and
35:37
communicate it with others, right?
35:39
I used to remember times where we would have presentations and
35:42
slide decks and I had to learn myself the skill of saying, am I
35:46
putting too much text on this slide?
35:49
What is the ID? What am I trying to convince?
35:50
What is my objective of what I'm trying to do?
35:53
And I think nowadays you can have technical skills, you can
35:56
have all these, you can have great degrees and stuff.
35:59
But in most companies it's about, hey, I'm, I think this is
36:03
a vision or the company has a vision.
36:04
I'm taking this particular part of it.
36:06
I have an ID of it or have multiple IDs over it.
36:10
And I want to share that. Can I break that down into a
36:12
presentation? Can I communicate that properly?
36:15
I've spent a lot of time trying to convince people in companies
36:18
that we should go in direct in a specific direction, and I filled
36:23
a lot of times, and that was for the lack of communication.
36:27
It wasn't for the lack of trying, but it was more like I
36:29
wasn't able to convince them. I didn't have the argument I
36:32
needed to go back. And I think nowadays we get
36:34
tools like an AI, Cha chi BT, all of those other things as
36:38
well as, you know, strategy handbooks and all those other
36:41
things that we can utilize to say, how do we bring those IDs
36:45
to life and, and share those? But also can we have other tools
36:48
where we can build things out to show people what we're trying to
36:51
do as well? And I think that's that's great
36:53
for. Everything.
36:53
So if you could give an early stage founder trying to break
36:57
into fintech one piece of advice, what would that one
36:59
piece of advice be as we'd head into 2026 imminently?
37:04
I think for any founder, especially, I think if you're
37:08
young, you're at such a great position, but even like don't be
37:12
afraid young. To me, it doesn't mean you have
37:14
to be 21. You're going to be 21 all the
37:15
way up to 60 and you can be considered young.
37:18
But I think going into 2026, it's, it's understanding the
37:24
history of Fintech financial services, but also understanding
37:28
where are we currently at, right?
37:30
So things like modular payments, the fact that people are doing
37:33
more and more partnerships, it's understanding the encroaching
37:36
technologies within our space, right?
37:38
What is coming in that's impacting the industry into
37:42
doing that. Once you have all of that, you
37:44
have that lay of the land, basically, you understand what's
37:46
going on. You did the technologies that
37:48
are taking place because there's so many different companies not
37:52
not just to mention the PS, PS and the acquirers and the fraud
37:55
engines. There's such a wide landscape.
37:57
It's what can you put together as a company that it's going to
38:02
create value for those who are either there for the consumers,
38:05
for the merchants, for the acquirers, for the issuers that
38:08
others are not working on. Why?
38:10
Because a lot of them might be legacy.
38:12
They might be, you know, it's very difficult for them to do
38:14
it. So I think if you're currently
38:16
working on in crouching technologies apply to the
38:19
payments ecosystem that produce a level of value for the
38:24
consumer, the merchants, the acquirer, the issuers, the
38:26
schemes, whatever that may be, then that's probably a great
38:30
foundation to start a company on.
38:33
Really good. You mentioned briefly A Gentech
38:36
commerce earlier. It's obviously very hot topic
38:39
right now and particularly poignant for the payments world
38:43
and how they maybe go about dealing with what a genteke
38:46
commerce might become. What's your thoughts on on that,
38:49
particularly with the payment lens of this?
38:50
This thing's coming, right? We're going to be telling AI to
38:53
go and do shopping for us to book a holiday to all this sort
38:55
of stuff. If you're a payments company
38:57
right now, how do you address the, I guess the security
39:02
challenges that might come with that for one?
39:04
And what would you be thinking about how you, you wrap it into
39:06
your ecosystem? Because for me, you're at risk
39:10
if you don't get it right early on, because the agentic commerce
39:13
will then start saying, hey, this payment provider didn't
39:15
like me last time. I'm not going back there again,
39:17
right? You have this finite window to
39:18
be like, we need to understand this thing and we need to be top
39:21
of that list for the agentic that's doing the shopping,
39:23
right. Yeah, absolutely.
39:25
I think the way that I'm currently looking at agentic
39:28
commerce is that indeed it's undeniable.
39:30
It's something that what we've seen with especially the LLMS
39:35
and AI and generative AI, sort of like going into their
39:39
creating their own ecosystem. They're doing things for the
39:42
consumer to, you know, do better search, do find products, find
39:47
services. And then that last step is of
39:49
course, doing the transaction. That's where we're in that space
39:52
as this is what agentic commerce is going to enable.
39:55
The biggest thing in that is indeed authentication, right?
39:58
How do I make sure that whoever is, whoever this bot is or
40:02
whoever this agent is. Do they have the, the actual
40:06
authority and the authentication to be able to do those
40:09
transactions? So as a payments company, I
40:12
wouldn't place all of my, I wouldn't put put, I wouldn't bet
40:16
on just one protocol being there.
40:18
I think now more than ever, you need to be on top of it.
40:22
You need to study what's happening.
40:23
You need to have an open mind as to who's introducing what.
40:28
And that's at all because it's this party, you know, it's we
40:31
shouldn't look at it. No, everything is going to be
40:33
out there. It's going to at the end of the
40:35
day going to be our consumers going to adopt to it.
40:38
I think probably in the next two to three years we'll see a nice
40:40
rise of adoption within that. Then it's going to be the
40:43
merchants. How are merchants going to react
40:45
to that? Yes, all of them wanted to
40:47
enable because everybody wants to make more, more money.
40:50
And if this will allow them to do it, absolutely.
40:53
We're seeing Visa introduce a standard that merchants will
40:56
have to adapt, right? Or will have to adapt, will most
40:58
likely adapt because it solves a problem, right?
41:01
How do I identify that this agent is actually a trusted
41:06
agent to actually come into my ecosystem and purchase
41:09
something? Then we're seeing the likes of a
41:10
Google with their protocols and building that out.
41:13
So I think as a payments company, you need to look at
41:15
each one of them. You need to look at the
41:17
different layers. You need to have a good
41:18
understanding of every single step of it.
41:21
You need to have people who who live and breathe that and are
41:24
trying to say, hey, we're going to make this work.
41:26
Because I think what a lot of what I think a lot of payments
41:29
companies don't understand is that just the way that and Apple
41:32
Pay came in, Apple Pay was something that a lot of people
41:35
like crazy. You know, it's happening, but it
41:38
still took a decade to really get to that maturity level.
41:42
But now it's non negotiable. There's still getaways out there
41:45
that don't accept Apple Pay. Absolutely right.
41:47
It's still, there's still resistance, right?
41:49
There's resistance and it's not even a, a, a merchant
41:52
resistance. It's oftentimes even an acquirer
41:54
or a PSP resistance that's, that's in there.
41:57
But that adoption took 10 years. Then if we look at things like
42:01
Soft Boss, right, which still took five years.
42:04
And now we're seeing tokenization, which has been
42:05
around for a very long time, but that that that we're now getting
42:08
to a place where it's going to be mandated.
42:10
I think with Agentic, what we're just going to see is that you
42:13
can't just say it's going to be this one solution that's going
42:16
to solve everything. Different players are going to
42:18
contribute to it. Open AI and Tropic, everybody's
42:22
introducing things within that space.
42:23
They're creating standards of creating protocols.
42:26
And instead of having this close mindset, you seeing that with
42:30
like Entropic launch MCPS, which is a model context protocol,
42:33
which then allows you to access different sources and different
42:37
data forms. And the whole industry went
42:39
great. ID let's go after it.
42:41
And I think what we're now just seeing is that a lot of these
42:43
companies who were always very proprietary, sort of like we
42:46
have this thing are now taking each part of it and trying to
42:50
contribute a protocol, of course, hoping that they can
42:53
claim that part. But if we, if you're a payments
42:56
company, you have to say, I have to work with everybody, right?
42:58
I like some companies approaches of the same.
43:00
We're in every conversation because every conversation
43:02
matters and you need to take that in.
43:05
What I think will happen is eventually we'll have companies
43:07
are just saying, look, that's too much effort.
43:09
So you'll have a company that say, hey, we're like an
43:11
orchestrator for all of these agentic protocols.
43:14
We're just going to build an SDK or everything on top of that
43:17
layer. You integrate with us and we'll
43:19
make sure that everything is there.
43:20
You'll you'll get those type of companies that are going to
43:23
succeed with that in doing that. And I guess then just a couple
43:29
of final questions as we we wrap up the next wave of AI driven
43:35
disruption, Where's it coming from?
43:38
Yeah. So I think the next wave of AI
43:41
and what we're seeing is I think Agentic commerce is definitely
43:50
that next wave, The next wave that what we're going to see is
43:54
that consumer adoption needs to take place, merchant adoption
43:57
needs to take place. It's going to be a multi wave
44:00
type of effect for it to really get to that point.
44:03
And even then, how far is that going to reach?
44:05
Right as I'm having conversation with people, not everybody is
44:08
willing to just say I'm just going to let my agent do all of
44:10
these things. Things have been introduced in
44:13
the last 12 to six months. New protocols are still coming
44:16
out every single day. So I think the gentic is
44:17
definitely there. But if I overlook at what, what
44:21
AI can actually do for us, I think if we're just looking at
44:26
payment specifically, we're going to have to deal with fraud
44:31
and risk a lot more because it's not just the gentic, it's it's
44:35
fraud attacks, it's impersonation, it's taken over,
44:40
it's take over accounts, it's all of those other things.
44:42
So I think AI will enable a lot of the fraudsters already right
44:47
to they will do a lot more fraud.
44:50
How do you protect against that? So there's going to be a lot
44:52
more on that. So I actually think machine
44:54
learning for me, right? So generative AI is one thing
44:57
that gentic is another thing, but I think machine learning
45:00
will have to also get much better.
45:02
And when I'm, when I'm looking at that, I'm seeing some
45:05
companies investing heavily now in their own GPU, like getting
45:11
NVIDIA, sort of like getting AWS and NVIDIA sort of to come in
45:15
and build out their own foundational models that go end
45:18
to end to be able to deal with risk, to deal with fraud, to
45:24
deal with dynamic 3DS and all those other things.
45:27
So deal with the TRA exemptions. So how do I authenticate this or
45:32
how do I authenticate this? And then also authorization and
45:37
dealing with that entire funnel end to end is where I think
45:40
machine learning is going to get this new wave of acceptance to
45:44
saying like if I'm a payments company and I'm just thinking
45:47
about the agentic side of it, that's not going to be enough.
45:50
Yeah, I think the fraud, that's really valid point.
45:53
You know, the new evolution of what fraud through AI looks like
45:58
in essence. Absolutely perfect.
46:00
Now, Dwayne, before we wrap up, we've got one of our favorite
46:03
parts of the show, the Shelf of Shame.
46:06
So we would like you to nominate something that maybe gives you a
46:09
little bit of frustration in the payments world.
46:12
And we agree if we put it on the shelf, never to be going into
46:15
the payments world again, or you take it with you, what would be
46:18
your ick in payments? Oh, that's a very challenging
46:24
one. Well, I have to say that the
46:26
thing that gives me a little bit of an ick is that we have this
46:34
tendency and especially like the more I talk to different
46:38
payments companies, they're very smart individuals inside of a
46:43
lot of these payment companies. You speak to them behind the
46:46
scenes, you have great conversations to go really in
46:48
depth and they really know their stuff.
46:52
But the ick that I have and I've experienced with myself that I
46:55
felt like I know this stuff and I can actually do something with
46:58
it. It's that we still have this
47:00
very banking hierarchical mindset of saying the only
47:05
person who can really say something for the sake of the
47:07
company is the C level executives, right?
47:10
We're just letting the C level is drafting blocks and having a
47:14
vision and all these other things.
47:15
And I as I'm reading it like you didn't write that any of that.
47:19
I think we need to get out of that space where great ideas,
47:25
intelligence, new concepts are wrapped within these companies
47:30
and individuals don't have their opportunity to share that.
47:34
I look at deck a lot where if you go look at it, it's not, I
47:38
see within tech, it's not the C level who creates things, right?
47:43
It's not the CEO. It's not, you know, you have
47:45
some, you have an Elon Musk, you might be an exceptional, but
47:47
even he gives more credit to his teams, people that you might not
47:51
know who are building things. I think payments needs to give a
47:54
lot more people the opportunity to not just to be able to talk
47:58
freely about things and come up with IDs and collaborate and
48:02
come. Up more, I think that
48:03
accessibility for people putting them obviously we we've been
48:06
fortunate the companies that we've worked, you know, we had
48:09
some friction with it early on, I think, but we've we've
48:12
generally been allowed to, to put our voice out there and you
48:16
know, we've obviously climbed up the the ladder from, you know,
48:19
the types of roles that we set in these days.
48:21
But certainly when we started this journey, we weren't like,
48:23
you know, in that kind of really senior position.
48:25
So, yeah, it's a great one to nominate.
48:27
Thanks for coming in, Dwayne, being great.
48:29
Where can people find you? Where can they get in touch?
48:31
Find out more. I think probably the the best
48:34
way if you if you want to learn more about what I'm doing at the
48:37
moment or the, the, the thoughts and IDs that I have, go to
48:40
LinkedIn. So Dwayne Jeffrey, you can just
48:43
easily find me as well. Follow, connect, DM me, whatever
48:47
you want to do. The second thing would
48:49
definitely be my my newsletter. It's a sub stack newsletter
48:52
called payment strategy breakdown.
48:55
Every week. I try to every week at least
48:57
post a, a very in depth type of topic.
49:00
I like to just sort of like give that as much as possible.
49:03
And then I try to publish where I'm going to be at what
49:05
conferences I'm going to be at as much as possible.
49:08
But I'm very approachable. If people, I, I talk to people
49:11
all the time. I probably have 10 to 20
49:13
different conversations with people from all parts of the
49:16
payments ecosystem who either want to collaborate or who want
49:19
to say, hey, we're working on the startup.
49:21
I want some of your advice or even larger companies are
49:24
saying, hey, we want you to work on our project with us.
49:27
And I tend to do that from time to time as well.
49:29
Well. Thanks for joining.
49:31
Thank you. Thank you.