Speaker 1: 0:00

got a fast connection, but at the end I can see how much percentage is uploaded. It basically uploads a perfect copy in the background as we're speaking.

Speaker 2: 0:08

Sounds good.

Speaker 1: 0:09

Okay, cool, I'm just going to bring up my script and we'll go from there Three, two, one. Today in the podcast. I'm delighted to welcome Shannon Scott, senior Vice President and Global Head of Product at Airwallex, one of the world's fastest growing fintech innovators. Shannon's journey is a fascinating one. With a background in computer science and mechatronic engineering, shannon brings a unique technical and strategic perspective to the world of global finance. At Airwallex, he's at the forefront of developing cutting edge AI and data driven solutions to transform how businesses manage international payments, compliance and financial operations. Today, we'll explore Shannon's career path, airwallex's vision and dive deep into the transformative role of a genting AI in financial services. Welcome, shannon.

Speaker 2: 0:56

Thanks, andrew, it's great to be here.

Speaker 1: 0:58

Nice to have a fellow Aussie on the podcast yet again. And we share similar traits because we both come from engineering backgrounds and I'd argue that engineers think in a slightly different way because we were taught to think of things from first principles. That might come out in a discussion, but perhaps you could walk us through your journey from growing up in rural Victoria to becoming SVP, global Head of Product at Airwallex.

Speaker 2: 1:20

Yeah, absolutely Like. I think it's been a wonderful journey. Actually, I did grow up in rural Victoria but had the opportunity to go to the University of Melbourne. I knew I was good at engineering. I was very interested in computer science. This is around the time of, I guess, the dot-com bubble bursting.

Speaker 2: 1:39

A lot of hype, a lot of tears perhaps for those already sort of in industry, but I sort of saw it as something that was going to be very exciting into the future. One thing that was very lucky for me as I went through my education is the graduate opportunity that I had following my university was actually a very small business and it was a combination of building software for the insurance industry and then also making sure we were working very closely with our insurance customers to ensure they got the most out of that software. And so it was a great combination of software development, understanding what's possible to build, understanding the models and how they work under the hood, but then also really understanding the customer experience and what it means to build software that they can actually understand and use. And I think over my career journey there's always been a software element, but it's often also been on the commercial side, on the product side, really thinking about how you can bring technology to consumers, to businesses, to users and make it as powerful as possible.

Speaker 1: 2:41

I had a chuckle there when you were talking about going to university during that dot-com era. I was at Telstra during the Telstra dot-com era when they were trying to become a Yahoo and of course they realised they were good at being a telco. So interesting times around the turn of the millennium. We talked about the fact that we're both engineers or come from an engineering background. How does your engineering background influence your product leadership at Airwallex?

Speaker 2: 3:10

I think your comment about first principles is actually a really important one. I think the better that you understand what's happening under the hood and can really reason with how this technology or service is operating, you can ultimately create a much better product or much better product experience. So you know to give an example, I think probably our wallet-extended financial services. We provide both acquiring and issuing, so both the card provider and, where you know, your audience, would enter those card details online to acquire the payment.

Speaker 2: 3:36

People don't often think about what's happening under the hood. There. They're just like okay, my money's been deducted from my bank account and it appears in the business that I'm purchasing from, but actually it's just a computer network. It has all the same sort of quirks that you might find if something doesn't necessarily always go right when you use your own computer. It has some strange behaviors that are there, maybe because that was a legacy of cards being a service that used to be not digital, and so there's some things that aren't perfect, but some things you take for granted how they work. If you really understand how it works under the hood, you can eliminate those things you're taking for granted or those assumptions and actually think about how can I make this better overall? If you don't have that depth of understanding, you're probably not going to question those assumptions or understand what can and can't be changed.

Speaker 1: 4:22

Now you're inarguably one of the world's fastest growing fintech companies, so maybe give us an overview of Airwallex's positioning in the global fintech market and your role scaling a company valued at $6.2 billion?

Speaker 2: 4:34

Yeah, so this is actually our 10th year. I think we're coming up for 10 years towards the end of the year. We've reached 1,800 employees. We're across 25 different offices. It was actually founded in Melbourne, but we've very much sort of grown out all over the world with a strong presence here in London, where I am today, and then a number of other. I think the sun never sets on our Airwallex offices.

Speaker 2: 4:56

The product itself is helping businesses who need to operate globally like really easily interact with their financial services, and so if you're a business in the UK, you have a customer in Singapore, for example, you want to receive pounds, but your customer actually wants to use a local payment method that is unique and understood in Singapore and they want to pay in Singapore dollars.

Speaker 2: 5:17

We make that an incredibly easy experience. It doesn't matter that you're operating in two different countries. Actually, both parties are getting the experience that they're used to, and this means a business doesn't need to worry about setting up different entities all around the world. They can just get started with Airwallex and they can pay suppliers internationally, they can hold different currencies and they can sell to customers all around the world, bringing together all of those different financial networks having the licenses to operate in all of those regions takes a huge amount of energy and effort, but the team has been working really diligently to just keep building out that network and making it as easy as possible for our customers to get access to those global services and I think that's been a testament in how the product's been adopted our customer growth over the last 10 years and now around $800 million in annual recurring revenue.

Speaker 1: 6:07

So we're recording this middle 2025, and I've spent the first half of this year talking about nothing but AI. So I feel like, almost contractually, we have to segue into the world of AI. So how is AI fundamentally reshaping financial services?

Speaker 2: 6:22

Yeah, I think there's probably one other point I should make about AirWallex itself, and I think one thing that's interesting is that financial services often are in aid of some other intent. Right, for example, a customer wants to purchase something from your website, or you want to pay a supplier who's providing services to you it could be maybe cloud services. It could be the materials that you then on-sell, be maybe cloud services, it could be the materials that you then on-sell, and so the payment is just a part of that intent to purchase from the supplier. Whatever that might be. Technology and fintechs are making it really easy to not just support the financial transaction, but also all of the workflows that businesses need or they potentially might do in a manual way. We support all of those workflows, from onboarding the vendor all the way to sort of making the payment and reconciling the tax aspects, and so when we think about technology transforming financial services, I think the first step is it's a lot more about business process and aiding that process than it is just the transaction. So when we think about AI, there's a lot of different ways AI can actually slot into both the financial service and then the business workflow.

Speaker 2: 7:30

On the financial service side. There's things that we've been doing for many years. So risk management, understanding your customer, fraud detection, fraud prevention, regulatory compliance all of those aspects I think have been using a variety of sort of modeling tools and then more modern AI solutions to support those traditional finance aspects. But one thing I find really cool is when I think about building a product that's a great product for our customers. How is that AI actually visible and supporting those customers? So often that workflow for onboarding a vendor all the way through to payment is performed by a financial operations team. It requires a lot of steps. It requires a lot of steps. It requires a lot of approval flows, and AI can actually fundamentally automate a lot of the things that used to be manual and what used to take hours or perhaps a lot of man hours to process many vendors actually comes down to just minutes.

Speaker 2: 8:19

So I think that's one of the really exciting applications of AI that customers directly benefit from. It's not just happening under the hood. One other thing I'll say about AI that I think is really exciting is it's changing the way that humans interact with computers. So your operating system is your iPhone and the apps on the iPhone it's your desktop, it's your browser, but it's increasingly becoming your chat interface and it's a very natural way for humans to interact with other humans. You know we're talking through chat now or using text on our phones More and more. I think you're going to use that as a channel to interact with the services that you want to. So we're going to start to see more and more services, including financial services, being accessed through these more natural language-based channels, and I'm really excited to see where that goes over the coming years.

Speaker 1: 9:06

And what I find interesting is that you know AI isn't new. It's been around for 75 years. Financial services have been using AI under the hood for years now and I'm sure you've been using it since day one. So, to cut through the hype, you know what transformative impact you think AI will Read it again what transformative impact will AI have in finance over, say, let's read that third time what transformative impact will AI have in finance over the next, say, five years?

Speaker 2: 9:35

I think it's going to be a much higher trust from customers. Even though financial services require pinpoint accuracy and certainty in what transaction I'm making or how the money's been received and the reconciliation, I think there's still going to be a huge delegation to AI to automate a lot of those different services. And so when I think about the way a lot of financial services are offered today, it's like, hey, this is a business product, I'm allowed many users on the platform. Those users might have different functions, like one might submit a request for a payment, another might set up the payment and a third party may even approve the payment to make sure it's legitimate and going to the right person. So I've got different users with different roles happening here and we provide a service that allows you to configure that in such a way. To say, if the price is, or the transfer amount is, greater than $500, then it must be signed off by two people, not just one. We have all the tooling that can do that, but we put the onus on the user to actually say you can set this up however you like. Ai is going to come in and not just automate a lot of those steps, but it's actually going to start to make suggestions and operate on your behalf.

Speaker 2: 10:41

To say this transaction looks very standard or legitimate. This is something that's very common by the requesting user. I've already been able to verify the account details for you and they match the actual user's bank account information, and we have to have checked the name against the third party database. Therefore, I think this is a very standard transaction. I'm going to recommend to auto approve this, for example, and just at some, at some stage or depending on the amount, it may simply actually auto-approve and off it goes. At other times, we may see that a user wants to just eyeball it and say, yes, that's great, but we effectively took what was a series of steps by many users and now it is just, in fact, one step or one click to verify that we have the right information, and so it's actually those financial operations that are going to be automated, collapse down and give businesses a lot of time back to focus on their core business.

Speaker 1: 11:31

I was talking to my friends at SAP Concur they do a lot of expense management and expense reporting and they were saying in the future we might have, rather than expense reports, we might have exception reports. And you kind of detailed that there so that if it looks normal and if AI can see the trend that it looks like a normal transaction, only the exception will go for human approval, which is going to save a lot of time than looking at every single transaction.

Speaker 2: 11:53

Yeah, absolutely. And even things like uploading the receipts or sort of understanding, like taking a photo of the bill that needs to be paid, for example, like that could already be automatically uploaded if we can sort of look at the profile of the image or the document and recognise that it's actually part of this business transaction and it goes straight in. So you don't even have to be doing the steps that upload the documents.

Speaker 1: 12:20

So the advantage for a company like yours is you're fairly young, so you've got a tech stack that's fairly new.

Speaker 2: 12:38

When we look at traditional banks that have got legacy systems. How should traditional banks adapt to this AI-driven FinTech competitors like yourself challenge they're going to find is not only are we relatively young, but I would say we are tech first businesses. We are not financial institutions, we are technology institutions, and I think, therefore, we understand those sort of emerging technologies much more effectively and are sort of bringing that into the financial service industry, Obviously not only being a financial institution, but being an incumbent and large institution for any business. I think when you're large, it is hard to actually, you know, sort of turn the ship towards these new technologies.

Speaker 2: 13:08

One thing that gives me a lot of confidence, though, is that a lot of the services that AI is enabling and, if we take customer support as an example, there's a plethora of great tech companies vying for your business to provide really wonderful customer support solutions. Right, and this includes the models and the languages that it uses to understand the customer and how to respond to them. It includes the case management and the triaging and the prompting. It includes the embedding of the chatbot into your website or the sort of call center or operations team that you have, and so traditional banks or traditional businesses they're not going to need to reinvent the wheel. I think a lot they're not going to need to reinvent the wheel. I think a lot of those technologies are going to come to them in packaged ways, just like they're using technology today to be able to access these services. So in that regard, I think they'll be able to be supported by a lot of other great tech companies to sort of carry them into the next era of finance and computing.

Speaker 1: 14:06

So what we've seen with the adoption of generative AI and also agentic AI. So what we've seen with the adoption of generative AI and what we'll talk about a bit later, which is agentic AI regulators are really scrambling to keep up because the technology is just moving so quickly. So what role does regulatory compliance play in AI adoption?

Speaker 2: 14:27

quickly. So what role does regulatory compliance play in AI adoption? Look, I think it's most certainly extremely important.

Speaker 2: 14:29

I actually feel very confident in our space in financial services that it's actually already very well regulated. There are very clear guardrails on how information should be utilised and stored, what types of customers that you can support, what types of sort of fraud systems that you have in place, and because all of this is quite well understood, I think you're creating a great framework and sort of set of guidance for how the agents, the models that you bring into the business, should be operating, and they can be quite small agents with very specific functions, so it's well understood what that function is within the sort of guardrails of regulatory compliance. I think there's a lot more interesting questions that actually sit probably outside of financial services. That may include access to public information, that may include, like IP and those types of issues where there can be some really interesting question marks for AI. But in financial services, I'm very confident that we already have the guardrails and a strong understanding of the current guardrails from which we can use AI to build on top of.

Speaker 1: 15:34

Now, the access to quality data has always been something that companies have needed to worry about, and when we move into this AI era, the quality of data becomes more important, and I've been talking about the need for good quality data for some time now. But how should companies leverage data as a strategic asset in the AI era?

Speaker 2: 15:52

It's a great question, and I think for data that is generally, you know, readily available or that is perhaps even generated by the AI. If we're looking at, you know, media use cases, for example, you know businesses are going to have access to that information, but so is every other business, and so is every other sort of AI product that's available. And so what is the data that you have? That is that you understand well, that is structured in a way that supports your business use case. That is perhaps proprietary data or sensitive data, certainly because your customers have trusted you with that information. Like for most financial services institutions, like you, have data that isn't necessarily available outside of that public realm, and so that gives you a great opportunity to, I think, build AI tools that provide a unique or value-added service that other parties aren't necessarily going to provide. So when I think about building new AI features or how Airwallex can really provide a great customer experience, it's like what am I actually offering that the customer can't get through some of the means, or that is actually going to genuinely move the needle for them in a way that's built into Airwallex rather than just something that they can do without necessarily the services of Airwallex.

Speaker 2: 17:06

There are, of course, a lot of different types of data sources. So you know, the financial data is very well structured and requires very high accuracy and very strong reconciliation. And then there are data sources where you're just trying to understand hey, what does my customer look like? What are the services that I should provide to them? Is this a legitimate customer? Do I understand their sort of business cases? And in that case you're sort of creating a synopsis of that customer to give them a great product or service. You know, once they're onboarded, often AI can create a better picture of who that customer is and what they need than a human can.

Speaker 1: 17:42

So let's talk from about future. Proofing. How would you build a data infrastructure that support both current and future AI needs when we actually don't know what the next AI state might look like in three or four years?

Speaker 2: 17:56

It's a really good question and I think that it would be um naive to have too strong an opinion here, because because you never know sort of how quickly things things move um and and sort of what services come down the road. I think we are seeing a consolidation of different services, and you mentioned agentic ai, and while ai has been around for a long time like that term is relatively new um, like mcp service, for example, and these are sort of the units or APIs that different AI agents interact with to perform a given service is also a relatively new concept, and you see the market solidifying around these things and then you can start to build on top of them and create really great solutions. I think any business should be thinking about experimenting and keeping up to date with the latest sort of AI technologies, but then also really thinking about where am I investing? Where am I using sort of best-in-breed products that I can easily absorb into my business or build tooling around? If that best-in-breed product actually a new version comes out in a year from now or it actually becomes even better in two years, can I swap that out quickly and bring in a new solution, to give you an example?

Speaker 2: 19:06

So, on that expense management side, we used to use a third-party provider to use OCR and entity extraction to understand the contents of the receipt. What were the items, how much was it, what was the total? What was the tax, what was the date? Um, it worked okay and it probably cost us about 20 cents per receipt to actually upload it. Um, in the last 12 months we swapped that out with google gemini. Um, the cost went down by about four orders of magnitude, so I think it was like 0.002 cents or something similar, and the accuracy was dramatically, dramatically better. The product was the same, you know, it was exactly what the users wanted. In fact, it was better because it was higher accuracy. But with the same intent, but because we'd built quite a good modular solution, we were able to swap in the latest technology and get a much better outcome with very little effort.

Speaker 1: 19:55

Just to jargon bust MCP. What does that stand for?

Speaker 2: 19:59

Model Context Protocol, I think, and I suspect that didn't really help you with any further information. Is that fair?

Speaker 1: 20:08

I'll look it up. I'll ask my AI friend and I'll look it up and I'll drop in the right term post.

Speaker 2: 20:15

I actually feel a bit it's a relatively new technology that I feel a little bit sheepish explaining to your audience and so I hope I don't get it wrong but AI agents, for example, those using chat they might have to call on an external service. So, for example, if you type into the chat hey, I'd like to make a transfer to Andrew for $100 AUD, please, please, make that transfer for me. In the background, the chat has interpreted that request and made a request to a different service to initiate the transfer or for that service to say okay, well, in order to do that transfer, I need you to enter your password, for example, and so that tooling that understands how to take that instruction and provide a response is effectively that MCP server, and these are different units that offer that different functional pieces of work.

Speaker 1: 21:13

Now, one thing I've been saying to audiences for a while that seems to resonate is rather than thinking of AI being artificial intelligence, they should think of it as augmented intelligence, because there's always got to be a human in the loop. So what role does human feedback play in AI systems, for example in fraud detection?

Speaker 2: 21:31

Yeah, fraud's a great example. I would argue that we don't always need a human in the loop, but I think it can actually always create a much better solution. So if we look at fraud, it is actually a heuristic. A transaction happens in real-time. Maybe you've made a purchase on your credit card, maybe somebody else is making a purchase on your stolen credit card and we have to make an assessment at the time. Do we approve the transaction? Do we decline it? We're looking at things like what was the amount and the nature of the purchase. Is this sort of typical of that particular individual? Do I have extra information, like the IP address of where the purchase was made, so I know if it's happening in a country that is different to where you are? We're using a number of these different factors to understand what's happening and we're making a heuristic decision that may or may not be correct.

Speaker 2: 22:19

Now there's a couple of reasons why you find out if you're correct, and feedback, whether it's automated or from a human, is going to continue to refine and improve the model.

Speaker 2: 22:29

So if you let it through and it turns out to be fraudulent, you're definitely going to find out about it, because the customer is going to let you know, and that would be sort of an unfortunate outcome.

Speaker 2: 22:42

However, what we've recently introduced at Airwallex in our product now is sometimes we might decline a transaction that we believe is fraudulent and it may not be, and the customer would be very frustrated if something's been declined when they were making the transaction. So in real time we send them a text message that actually just asked hey, this was the transfer, was it you? And if they say no, it wasn't, it's like cool, we blocked it. Or if we hadn't blocked it, we say actually, in that case we're going to cancel your card because somebody else tried to do it. And if it was you, we say thanks very much, please try the transfer again. We'll make sure we let it through the second time. And so not only is it a good customer experience, because they can sort of correct for an inaccuracy in the model, but it's giving us great feedback as to the quality of the model and we can use that to keep improving our fraud understanding.

Speaker 1: 23:27

So the other side of the coin is if we're going to trust the AI a little bit more, how do you ensure AI remains explainable and trustworthy for financial decisions?

Speaker 2: 23:39

Explainable and trustworthy. I think we should cover those concepts separately. I think actually explainability is a really interesting science because if we look at the fraud model, for example, we may be able to infer oh yeah, okay, the IP address was in a different country. The AI can sort of inform us the data that was used to make the decision and we might be able to sort of intuit. That's the understanding. But we're probably never going to know exactly all the different heuristics that the model used to determine that fraud, especially if it's getting this feedback from customers over time. And actually that's probably okay. That's probably okay as long as we also have a metric for what is the accuracy of our fraud system. Can we get a sort of general understanding that this heuristic is operating with 95% accuracy? You're catching the majority of the fraud, for example? So I think the explainability piece depends on the context. The second item you'll have to remind me, andrew.

Speaker 1: 24:39

Trustworthy. How do we ensure that AI remains trustworthy for financial decisions?

Speaker 2: 24:43

So if we thank you, if we look at a financial decision, so, for example, I said, hey, make this transfer for $100 to Andrew, and I suggested we might just do that in a chat bot, for example. Now that's a very important decision. It must have perfect accuracy and it must know exactly which Andrew I'm talking about and it would be really disappointing if it went to a different Andrew or the wrong currency or something similar. And I think, where that trust needs to be proven and based on the severity or importance of the request, it's relatively easy to prompt the user to say this is my understanding of what you've asked of me. Could you please verify that this is the case? And because we do have that two-way communication with the user, it's relatively easy to make that request and establish not only that trust but the verification.

Speaker 2: 25:36

I think again, the smaller things. Over time the trust will increase and we're going to let the agent do that for us. We're not even going to worry about it, we'll worry about exemptions only, like your example earlier. The bigger things we're always going to include, we're always going to include a verification step and if I think about the example around structuring receipts, it's actually probably going to take a while before we get to 100% accuracy. It's going to take a few years perhaps, but 90% accuracy with human verification is still dramatically faster than it was for a human to do this on their own, and so we're still getting like massive benefit, even if the system is not necessarily 100% accuracy. I think users can easily understand that that's a huge benefit and they're going to have that trust that the model is still giving them a lot of benefit.

Speaker 1: 26:25

So we've got ahead around generative AI and most of my friends are now talking about chat, gpt, as they used to when they were going to Google something, which is interesting. But now we've got this new concept agentic AI or AI agents. First of all, how do you define agentic AI, because everyone has a different explanation, I found yeah, so.

Speaker 2: 26:46

So when I think of a, I think actually it's a really great, great question and everyone probably should have a different opinion and and also think about the benefits of of those agents.

Speaker 2: 26:54

But, um, an agent is performing a work function that uh can benefit, benefit the user, and there might be a process that actually has many different functions and we can string together those agents to perform a more complex task with many smaller tasks.

Speaker 2: 27:12

So, if I take the receipt structuring, for example, there's going to be an agent whose role is to actually understand and decompose the contents of the receipt, and then we're probably going to have an agent that actually understands, uh, the math behind what's happened, going to happen next. So I have a. This agent has a understanding of what is an account number, what is a currency, what is a sort of financial amount, and then I'm going to package that up into an actual transaction object that I can take to the next step, and then another entity is actually going to perform the transaction itself, and so if we have these agents that are performing discrete units of work, we can both have AI that is composable and understandable and doesn't necessarily become this sort of behemoth system, but we can also achieve very complex tasks by bringing together many of these agents.

Speaker 1: 27:59

So how will a Gentig AI redefine financial workflows?

Speaker 2: 28:05

I think that example. Let's break it down. So I talked earlier about how finance is not just making the financial transaction, and so if I look at all the businesses today, it's like, okay, I need to decide which vendor I'm going to use to you know, be my new sort of it chat system. I'm going to verify that the vendor is able to perform the features I need. I am going to verify that the vendor has the right accreditation. I'm going to verify that we have the right information for that vendor to make to make a. I'm going to go through a process of approving that that vendor is actually the vendor that we chose. This is the right amount of money. When they send me an invoice at the end of the month. That is the right amount.

Speaker 2: 28:51

I'm going to verify the account details. I'm going to schedule a payment for the end of the month. That vendor happens to be in a different country, so I'm going to have to make an FX conversion from pounds to US dollars, for example. I'm not sure about currency fluctuations and I don't owe them until the end of the month, so I'm going to lock in a rate from pounds to dollars that is triggered at the end of the month. So I'm going to create certainty in the rate. That doesn't happen until the end of the month. I'm going to schedule a transfer. It's going to happen.

Speaker 2: 29:17

Then, once that transfer has actually taken place, I'm going to reconcile that with my accounting system, and so all of those steps require a really significant financial back office and a number of different players to perform that. Today we have a system that allows you to do it all in one place, which is very valuable. I can see the lineage all the way from the payment through to the vendor and who it was to and how it was signed off. That's very valuable. But utilizing AI and that energetic finance, we're actually going to see all of those things collapsed into a highly automated process that requires much less manual labor is actually going to increase a lot more trust. We are actually going to see the accuracy come up over time and I think it's going to massively redefine not only the amount of effort it takes for large businesses, but it's going to give small businesses effectively the muscle to be able to do this themselves or get that benefit when they otherwise wouldn't have had the resources to be able to perform this kind of financial due diligence on their vendors.

Speaker 1: 30:17

So I think you're right. We've just really only started to hear about agenda AI.

Speaker 2: 30:20

We need to scratch the surface, so maybe you could talk about some of the use cases where autonomous AI agents excite you the most also about the value of understanding your customer, and so there are very strict guidelines in the financial services industry about what types of customers you can support and what types of solutions you can actually offer them. Perhaps there is risk for those customers as well, like, for example, they might be targets for other fraudsters to be bad actors against their business. So we want to make sure that the financial services they have can be utilized responsibly and that they actually are not taking on too much risk, and so understanding who that customer is is a really important part of providing them with the right financial services. I think what we're seeing and what we've already implemented is what used to take perhaps the customer to try to explain to us themselves hey, tell us about your business and then write a few sentences on their business. Then we might do our own due diligence, which is like okay, let's look at your website, let's see what you're selling, let's um, maybe even we might visit the site at times if that's necessary, um, but actually, since we've replaced that with ai agents, we actually have seen that the agents themselves can go and crawl the website. They can go and learn more about the business, they can understand the language used by the business.

Speaker 2: 31:48

For example, we shouldn't provide financial services to in some regions, military goods provider. But if an e-commerce retailer is actually selling a military jacket because they're a clothing store, that type of thing used to trip up the sort of less sophisticated systems, but now AI agents can really understand. Yeah, this is definitely a clothing store. They're selling a variety of things that might include a champagne dress or a military jacket, but I'm not going to get tripped up by those types of keywords and therefore we have a much better understanding of the customer and we're finding it to be far more accurate than humans doing this themselves.

Speaker 1: 32:25

So you're playing globally. How will agentic AI transform customer interactions when it comes to global payments?

Speaker 2: 32:34

Great question. I gave an example earlier in our discussion around you wanting to, or a British company wanting to, actually sell to a customer in Singapore, and I talked about the benefit of that customer in Singapore using their own local payment method. Now, I don't necessarily want my British merchant to have to understand every type of payment method or say hey, customer, here's a selection of 100 payment methods. Why don't you pick the one that suits you? But using AI, we can look at the available payment methods that we offer. We can look at where the customer is, we can look at the currencies available and we can suggest to that user hey, we think that this would be the best payment method for you, or this would be the cheapest payment method, or this one would give you buyer protections and we can start to orchestrate the best ways to utilize financial services around the world.

Speaker 2: 33:27

And this is something that I think no business should have to worry about trying to get their head around, and no buyer should want to have to worry about it too. The buyer just wants it to be easy and intuitive to them. The business just wants to sell to the customer and provide a great service or solution, and so AI can actually help us all understand the complexity of the global financial services. And then, of course, things like regionalization and using internationalization of the products. So we're changing the different languages all of those transaction services and other things far easier and far faster to perform in AI than they were in the past.

Speaker 1: 34:05

So we talked about the fact that the technology is going a lot faster than regulators can keep up. So what challenges do you think will arise in deploying autonomous AI in regulated finance?

Speaker 2: 34:19

It's a good question.

Speaker 1: 34:21

Only ask good questions.

Speaker 2: 34:25

Again, this is already a very heavily regulated industry, and so I'm actually quite confident that we have a strong grasp of what are the regulations and how to stay within it. I think we're not going to deploy these agents irresponsibly. I think that we talked about trust earlier, and understanding your money and where it's going, and that all the numbers match up, I think, is one of the highest levels of trust required, and so I think we're going to approach this with the right level of responsibility, that we're not going to let those agents sort of go wild. When I think about other use cases, if you're a media agency or a marketing agency using AI to produce all of your different materials, it's probably not as consequential if you get it wrong, but I would certainly recommend that you don't sort of take it too far and end up producing content that doesn't make sense. But in that particular case, it might be okay to take some more liberties to take advantage of the latest technology or efficiencies.

Speaker 2: 35:24

I think in financial services, we're going to make sure that we're performing the right sort of regulatory responsibilities. There are going to be other solutions that come forward. If we look at cryptocurrencies and stable coins, for example, regulation is changing very quickly. Ai may actually be able to help us understand that regulation more readily. It may help us to actually adapt to changing regulation all over the world. So in a lot of ways AI can actually help us to make sure that we actually are being compliant with all the different regulatory bodies internationally.

Speaker 1: 35:57

So in all of my keynotes I'm trying to change the mindset of my clients to think of AI not just as a tool that can craft an email or review a document but how do you think AI, and I'll say that again. Or review a document, but how do you think AI? And I'll say that again. So in all of my talks I've been really trying to change the mindset of my clients, to get them to not think of AI as just a tool, something to craft an email or to check a proposal, but actually become a decision partner. So how will data or AI evolve from operational tools to become a strategic decision driver?

Speaker 2: 36:28

When we talked earlier about the benefit of, you know, setting up the right approval flows, for example for for an expense, and we talked about how it used to be, that I could configure this as a user and then I could actually have ai configure it for me. This, I think, is the interesting strategic opportunity, and so while we we provide a little tooling to say you can, for example, if the transaction is above $500, get your manager to sign off and then get the finance team to sign off, and if it's over $5,000, get the CEO to sign off, but now, with AI and as Airwallex has grown to support many customers, we can look at the entire corpus of our customers in an anonymous way and say hey, businesses like yours typically use this approval flow. If it's above $50, do X, if it's above $500, do Y, and if it's above $5,000, do something else. And so, instead of just giving them the tools to do it or automate the tools, we can actually suggest the right structures.

Speaker 2: 37:30

One thing that I've seen is there's a lot of challenges in the financial services industry today, and especially, it's true, in the UK, where people are tricked into sending money to the wrong place, for example. There's two things AI can do that can really help this situation. Firstly, they can actually try and prevent that because it's an unusual transaction and prompt the user to say, hey, I think this is probably not what you think it is. You should probably not proceed with this transaction. But if AI was to suggest approval flows for a business to say, hey, we think that you should always have two people check the transaction before it actually goes out the door. You can actually not only make it a more secure system for those users or make sure they have a lot of approval flows, but you can actually make it a more secure system for those users or make sure they have a lot of approval for those, but you can actually make it a more like fraud resilient system as well.

Speaker 2: 38:13

And so if AI can actually look at similar customers, look at their cash flows, look at their understanding of hey, you know, it seems that actually you have a lot of surplus capital and you've got more funds coming in than going out, why don't we suggest that we put that into a like asset management product for you so you can generate a rate of return on the funds that you hold in the interim? Or in the opposite case, would you like to provide lending services that we think are achievable and within your realm, to actually pay back but will really help you with cash flow? Those types of suggestions, I think, are going to become very synonymous with AI and therefore it's much easier for businesses like Airwallex or other service providers to create a much better user experience and influence the right decisions for our customers.

Speaker 1: 38:57

So final question before we go to the quickfire round what excites you the most about what you and the team are working on at Airwallex at the moment?

Speaker 2: 39:05

I think what I'm most excited about is many businesses don't have the manpower to have a financial operations team.

Speaker 2: 39:12

Large businesses might have dozens of people, small businesses probably it's just the owner of the business themselves doing a lot of this accounting, doing a lot of these transfers.

Speaker 2: 39:21

That represents your chief financial officer, that represents your financial operations team. That didn't previously exist. Then we're giving you access to something that you otherwise couldn't have had access to, and instead of you sort of having to do all the heavy lifting yourself and understand these systems, you have an agentic partner who's actually saying let me handle these transactions for you, let me close the books for you at the end of the month, let me handle these tax obligations, let me control the transactions for you, let me close the books for you at the end of the month, let me handle these tax obligations, let me control the card spend of your employees. I think that is a model that is going to be, or a future that is not very far away. That is going to be very powerful for businesses. It means all of their financial services are handled and they can actually just get on with doing their core business, which is not making transfers.

Speaker 1: 40:10

So we're almost out of time. Right to my favourite part of the show, the quickfire round, where we learn more about our guests iPhone or Android iPhone and I really wish I didn't have to say that I tried very hard with Android for many years. Window or aisle Window, your biggest hope for this year and next Window.

Speaker 2: 40:33

Your biggest hope for this year and next? It's not a hope, but I'm very excited about AI always continuing to scale over the next two years.

Speaker 1: 40:38

I wish that AI could do all of my.

Speaker 2: 40:45

Recruiting.

Speaker 1: 40:48

The app you use most on your phone Strava. The best you use most on your phone Strava the best advice you've ever received.

Speaker 2: 40:57

I think it was the recognition that some advice should be ignored.

Speaker 1: 41:06

Good point. What are you reading at the moment?

Speaker 2: 41:14

Great question, good point what are you?

Speaker 1: 41:17

reading at the moment. Uh, great question, can we skip that one? Yeah, sure, well, I can recommend a great book called digitally curious that I wrote, but uh, probably not. Won't get you one in time for that.

Speaker 2: 41:25

Uh, I think the reason why it's hard, if I may say. Actually, it's because I've been listening to. I've replaced books with podcasts, okay, and the podcast that I've been listening to relentlessly recently is acquired oh yes, I need to pick that one up.

Speaker 1: 41:40

So a follow-on question who should I invite you?

Speaker 2: 41:48

should talk to another ai founder. I have one in mind.

Speaker 1: 41:54

We can talk about it offline sure, how do you want to be remembered? I think, building great tech businesses so, as I'm the actionable futurist, what three actionable things should our audience do today to better understand how agentic AI will power the future of finance?

Speaker 2: 42:15

I think podcasts are a fantastic way to learn in sort of bite-sized information, which I think is why we've shifted from books to podcasts. I think setting up your habits so you don't just always go to Google, but you start actually spreading your questions around and learning what works for you in terms of the different AI services around. And the third advice I would be experimental, be experimental and always ask the question can this be done a better way?

Speaker 1: 42:50

Shannon, this has been a fascinating discussion.

Speaker 2: 42:57

How can we find out more about you and your work? I think Airwallex is very easy to find. We'd love you to go to the website, if you're a business, and learn about the services that we can offer you. It's a broad range of services there. My details are on LinkedIn and I talk a lot about Airwallex and technology there, so those would be great places to start.

Speaker 1: 43:14

Thanks so much for your time and safe travels.

Speaker 2: 43:16

Thanks, Andrew.