Join us on Leader’s
Perspective

Unlock the boundless opportunities presented by digital transformation and thrive in this era of rapid change.

IN THIS EPISODE

 In this podcast, we hear from an industry expert at The Money Cloud, about their experiences and insights on driving a data revolution in the cross-border payments market. The episode sheds light on how AI is transforming the landscape of international payments, providing clarity, and simplifying processes.AI’s revolutionary impact on data usage in international payments has opened up new possibilities that were once unimaginable or impractical. The advanced analytics provided by AI have enabled real-time risk assessment, fraud detection, and personalized customer experiences. These capabilities significantly benefit customers by ensuring secure and efficient transactions.

Learn how organizations can stay at the forefront of the AI-driven data revolution by fostering a culture of innovation, investing in research and development, and collaborating with cutting-edge technology partners. The key to maintaining a competitive edge is to adapt to the changing market demands and emerging AI trends.

GUEST

CEO and Co-founder of The Money Cloud Ltd, Emmanuel is a leading player in the Fintech market, leveraging AI and building scalable tools in the cross-border payment market. An excellent networker and communicator with first-class interpersonal skills, he is building a strong corporate partner innovation ecosystem delivering customer-focused solutions for the end user. He has integrated change across all areas of operations, leading teams to deliver industry-leading products, services, and sales.

EPISODE TRANSCRIPT

Ratnadeep: Welcome to another stimulating episode of “Leaders Perspective”, where we explore the insights and innovations that are shaping our world today. We are diving into the fast-paced intersection of AI and financial technology and there is no better person to guide us through this labyrinth than our esteemed guest Emmanuel Addy, a specialist in cross-border payments, and a vanguard in the fintech industry. 

Emmanuel is known for putting client-centric Solutions and Innovations at the forefront of his leadership with an impressive track record of integrating transformative change across operations. He has spearheaded the teams to deliver industry-leading products and services, is a master networker communicator, and is a voice in the national press including Radio 4’s The Money Box program. 

Emmanuel is also an inspiring lecturer at the Warwick Business School and the IESEG School of Management, Lille. His in-depth expertise and commitment to delivering customer-focused solutions are shaping the financial world as we know it. 

Today we’ll explore Emmanuel’s unique insights into how AI is revolutionizing the fintech space driving driving a data revolution that’s opening up new opportunities and redefining the way we think about money and finance. So buckle up dear listeners as we embark on a fascinating journey into the future of finance. Welcome Emmanuel Addy.

Emmanuel: Thank you for that introduction, buckle up indeed.

Ratnadeep: Great. So Emmanuel let me dive right into it. So throughout your impressive career, you have witnessed the rise of AI in the cross-border payments market. Could you walk us through some of the key moments or innovations that have truly stood out for you?

Emmanuel: Well, I think AI has been in the world of banking and finance for a while now, since the mid-80s. And the financial world is changing really quickly and institutions over the past decade would need to be very nimble to keep pace in cross-border payments as the world is increasingly connected. AI continues to play a significant role in making international payments managing fraud and detecting money laundering and I think there are essentially three main areas where AI plays an impressive. 

Role one is improved speed and accuracy. One of the primary benefits of AI in international payments is its ability to process payments faster and more accurately than traditional Payment Systems. They can take several days, sometimes several weeks because of human error but AI can process large amounts of data that automate the payment process and essentially minimize the risk of errors and increase the speed of transactions.

The second way we see how AI is involved in cross-border payments is by reducing fraud and cybercrime. So identifying suspicious transaction patterns, we see AI using Predictive Analytics to identify fraud risks before they occur and also by behavioral Biometrics to verify the identity of a person by analyzing their keystrokes Mouse movements, or human patterns. 

And then I think the third way is really obviously in compliance. The international payments are subject to strict KYC- know your customer and anti-money laundering rules. So AI has been used for many years in that compliance process, automating transaction screening, flagging breaches, and exceptional transactions. So I think AI continues to play a role in cross-border payments. AI’s role in those areas will continue to increase as the data increases as we get more usage, etc etc.

Ratnadeep: That’s quite interesting. I guess some of these have been worked on by us in our company as well, so I kind of understand where you’re going with this. With The Money Cloud, which is your company, you are demystifying international payments as we know it. Can you share how AI plays a role in this mission, particularly in managing the complex kind of data involved? How does it enhance the process in an absolute sense?

Emmanuel: So our company is a cross-border payments platform. We provide SMEs and internationals with a range of cross-border payment options. We aggregate and connect to different payment companies, FX brokers, and other types of payment companies. 

So for us, it’s all about improving customer experience; and so some of the ways that AI is helping us is in business that we’re implementing or in the process of things like digital personal assistance like Siri or Alexa, using conversational AI. And that process of simulating the experience of talking to a real person, customer care, service chatbots, and things like converting English language text into other languages and vice versa. 

Also one of the key things that we’re focusing on is how we provide customers with insights about their transactions. How can we manage their data, collect that data and then provide them with some insights that will help them with their future payments? Obviously, cyber security and fraud algorithms are another key thing. And then how do we communicate with our customers? 

We can use AI to automatically draft replies or messages to customers or partners. So those are the sort of key main areas that we’re trying to focus on that will enable us to improve our user experience and our engagement with our customers.

Ratnadeep: As far as I’m concerned, looking at the use cases that you mentioned for the Money Cloud, specifically, I’m fascinated by the way even other companies in your space are using AI as a transformational tool. So the way AI has transformed data usage in international payments is for everyone to see. Can you share some exciting or unexpected possibilities that probably AI has unlocked in this field, perhaps something that was once considered absolutely unthinkable?

Emmanuel: I think that’s an interesting question. Earlier this week, here in the UK, the Financial Conduct Authority has just launched (or will be launched on Monday) the biggest regulatory shake-up of the UK Retail Financial Services, in an attempt to identify and prevent customers from being ripped off by banks building society’s other financial services. It’s the biggest regulatory shake-up of the UK Financial Services sector for two decades and the changes are going to focus on consumer value for money in giving fair pricing to all customers. I think AI will play a huge role in that. 

And where you have changes in regulation, especially where they’re focusing on customers and giving fair pricing to customers, AI can play a role in that. So, for example, I think we’ll see AI used to automate currency conversions and optimize exchange rates improving the speed of accuracy of international payments even further. 

I think you will see AI-powered payment methods such as voice-activated payments. There’s potentially no reason why we can’t do things in the same way that we tell Alexa to do things. We can get to the stage where we can use voice-activated payments or biometric payments for even greater security and convenience. So I think those are probably the two main innovative areas where I see AI going. We’re obviously a little way off that but I know that companies are thinking about those sorts of transformative ways of making cross-border payments at the moment.

Download the complete transcript of this podcast

Ratnadeep: On the flip side, integrating AI must have its own set of challenges. What have been some of the hurdles within The Money Cloud or any other company? How have you managed to overcome those? I’m sure our listeners would love to learn from your experience specifically on this aspect. 

Emmanuel: I think integrating AI is an ongoing challenge. I think you have to understand your I.T landscape like ‘what are our data sources’, ‘what are our systems’, ‘what are our processes’, ‘who are the stakeholders involved in our IT landscape or our tech stack’ and then identifying the the gaps the pain points and the opportunities for improvement. 

And the flip side of that is understanding how are you going to use AI, how are we going to integrate AI into our tech stack so that it’s cost effective and it adds value to our business and increases our customer engagement and customer usage.

So it’s thinking about what are we using AI for and how do we want to leverage AI to enhance our business. One of the other things that we’ve had challenges with, it’s obviously a very technical issue, getting the right expertise in, getting the right people to provide expertise on the tech stack, the architecture, the solutions, there’s no point integrating technology or developing technology for the sake of technology.

Looking back at 2013, you think of Google Glasses that didn’t take off. Nobody wants to be walking down the street looking through their glasses reading emails or trying to make an international payment. So it’s about implementing technology that’s going to enhance your business, enhance the user experience. 

When it comes down to the technology, understanding the landscape ‘how are you going to integrate it into your business’ and ‘what are the costs associated with that’, ‘what do things like your data contracts look like ‘, ‘what’s the optimal way of managing your data and your data contracts’, so you’re getting the best value for money. So all of those things need to be thought about and you probably need to bring in the expertise to help you make those kind of decisions.

Ratnadeep: That’s completely understandable. But again the value of AI to customers must be very significant enough to look through these challenges and try to implement as many cases as possible, as far as AI is concerned. Could you share a real-world example where AI-driven data management within your platform or any other platform for that matter led to a standout user experience or a surprisingly positive outcome?

Emmanuel:  For us, we’ve been trying to think about fixing problems that have the greatest impact on the consumer experience by identifying high impact, sort of almost low effort opportunities, to allow optimal customer engagement. So for us, reducing customer churn through real-time offers that meet their specific needs, we’re trying to present more relevant information to specific customers that we see. 

Rather than just looking at a segmented customer base, we’re trying to even delve deeper into that and look at specific customers. We’re also looking at using natural language processing or we are using natural language processing intent signals predictive analytics to help consumers to get to the next best step that they want to get to.  

I think the fourth thing for us is internally using AI to predict which accounts are going to transact or or buy next so that our internal team (our sales team) can focus their efforts on them, whether that’s with information or natural language snippets orengagement with those types of customers. So yes, for us, the focus is all on customer engagement and how best can we engage with those customers.

Ratnadeep: That’s anyway the primary issue that AI potentially can solve. While AI has brought tremendous advantages to the financial services industry, as it’s already evident through many platforms and in many companies, it’s not without risks. You have to accept it now. Can you unpack some of these pros and cons for us? How do you balance these innovations with potential pitfalls?

Emmanuel: Well AI is good at some things. It’s good at natural language processing; you can ask it to summarize things, you can ask it to paraphrase things, it writes things in a particular way or a particular style, it’s very good at that those sorts of recall or remembering things but it’s not good at logical reasoning. It’s not good at abstract reasoning or problem solving. And I think that’s the next step where the air will be able to get onto the sort of computational problem solving.

We’ve got to remember that AI gets things wrong. It has no concept of the truth. The technology is really just trying to predict the next best word or the next best text. So I think in financial services, some of the risks are one of the big risks as far as I see. It is the potential for bias AI systems trained on specific set of data and they reflect that data that they’re trained on. If that data contains biases or discriminatory patterns you know that system may reproduce reproduce and amplify those biases.

Another challenge when it comes to finances, we may have gathered data historically over many years, but is that data significant? Is that data of significant quality to enable us to deliver the insights that we want to see? There are obviously security and data risks involved with that data. 

Ratnadeep: For me, one of the biggest challenges also is, as we automate everything, every processes, there’s a lack of human interaction and we’ve all had cases or examples, where we’re trying to do something. It would be great to speak to somebody or just say ‘I’m trying to do A, B and C. Can you help me with this?’ Or ‘How do I do it?’ And sometimes, that chatbot or automated process doesn’t quite hit the mark. So that limited human interaction has the potential to be a risk particularly in financial services. How do you manage those risks? 

Emmanuel: That’s a very good question. Some proponents in AI have said that we should really be pausing AI development for six months to allow regulation to catch up. Regulation around AI is very difficult. They’ve been thinking about that for the past couple of years, but really, what do you do? Everybody now has AI. How do you regulate it once the horse is bolted from the stable?

So some things have been proposed. We might see certification for large language models. I don’t know how do you actually do that. How do you actually certify that all of that data, that a large language model has been trained on, can you certify that? It’s fit for purpose. It won’t have any bias. These are the challenges. 

And also there are challenges around copyright. Large language models have been trained on huge amounts of data. Obviously they would have consumed some copyrighted material. Should owners and authors of that copyright material be compensated for use of that information? I think that’s certainly a a valid argument. If you’re an owner of that copyrighted information, all of a sudden it’s now available to everyone in the world. 

Where’s my compensation? But the flip side of the risks are the potential benefits. McKinsey says that productivity gains could increase from 2.6 trillion dollars to 4.4 trillion dollars annually. We’ll see it could add as much as 0.6 to labor productivity and it has the potential to automate over half of today’s workforce activity by 2045. 

Download the complete transcript of this podcast

Ratnadeep: Some of those are very good things. Some of those are alarming statistics but I’ll tell you what Emmanuel, this is a great segment for my next question. Everyone knows what the buzzword is, like ChatGPT, large language models, which you just touched upon in your previous answer- generative AI. Now speaking of these cutting-edge technologies, what’s your take on how do you see them shaping or probably even disrupting the industry you are deeply involved in, which is the financial technology industry.

Emmanuel: My take is, I always look at technology, not just in the financial services but other sectors as wel.l I think we have to remember that technologies are tools to enable and improve productivity. One of the problems I think with ChatGPT and AI generally is, over relianced people start to believe that everything that it says is true. But AI is a tool; it’s not a mind. 

You can’t therefore just get a contract drafted. And let’s say, that’s everything. I don’t need to apply my professional responsibility. So I think that is something that we have to definitely have to think about and bear in mind. For me broadly, to answer your question, AI will enable us as a business to stay relevant. That’s the important thing.

How do we use the technologies to stay relevant so that we can provide insights or improve user experience that will benefit our customers and enable us to stay at the forefront of their user expectations? So I’ve touched upon what some of the specifics that I think might happen. But all of those are about staying relevant. Improving automating processes, improving productivity and all of that is geared towards enhancing our user expectations and staying at the forefront of those.

Ratnadeep: That’s a better competition. Whatever you do absolutely depends on that. Whether we’re getting better or whether we are ahead of the competition. Essentially in technology right now, when I talk about AI, it has its own set of questions on ethics as well. You are talking about regulations, you are talking about compliances, what are your thoughts on ethics in AI, specifically in usage of AI?

Emmanuel: Well, let’s go back to the risks. I touched upon automating. 300 million or potentially 300 million people could lose their jobs, and if your job is to make a data-driven decision from content on a piece of paper, AI will threaten that. If you’re talking to a human being from a script, AI will potentially that. AI will threaten a number of jobs in different vertical sectors. 

Estimates are, it could lead to some 300 million jobs. But what we see from technology and our experience of technology that it creates up other opportunities. There are opportunities and other jobs that will be created from this new technology. It’s interesting that earlier this year, Sam Altman, the CEO of OpenAI, spoke about efforts by himself and his company to support universal basic income. So the idea behind the universal basic income is that governments would give every person a tax-free flat amount of income, regardless of their wealth or employment.

Let’s see what happens in that case, whether that comes to fruition. Here are privacy issues such as facial recognition. For me, one of the biggest risks is the altered reality, deep fake. yAI can generate this information to alter your viewpoint. So apparently ,it takes only three or four seconds of hearing somebody’s voice to reconstruct it. 

So you hear cases of where AI is cloned. A young person’s voice, teen girl’s voice, for one million dollar ransom. I think ethically, morally, there are all sorts of issues to consider. There are issues; algorithmic biases have been trained on a large language models, being trained on large sets of data that will have biases. And again, as I’ve spoken about over reliance, I think there are lots of issues. There are even environmental issues to consider as well. 

When you talk about ‘Where is all of our data stored and housed in data centers around the world?’ It is stored in data centers and apparently, the cost of training a large language model with all of that data is equivalent to the same amount of carbon footprint as 25 flights from New York to London. 

So I think there are lots of environmental issues, ethical issues. How do you solve those? I believe there is an event happening in London in October where they’re going to talk about AI and how to solve ethical and those issues, risks, and how do we manage AI. It’s a huge challenge. And I think it’s a huge problem that’s made even more difficult because everybody has it now. 

Ratnadeeep: Let’s deviate. Every time you talk about your guest lecture at the prestigious institutes like Warwick Business School and the IESEG School of Management, it must be really enlightening. What are the core messages you try to convey to students about AI’s role in today’s financial sector?

Emmanuel: That’s a very good question. For me, it’sabout thinking where AI and other technologies will play a role. It’s about ensuring that you use technology in the right way and ensure that it adds value to your business, and adds value to your customers. As I’ve said technology, and ChatGPT, and others, with over what 100 million users in the first few months with their tools.

Ratnadeep :So you can’t just rely on that tool. It is there to enhance productivity but I think you still need the human mind to obviously check it to ensure that it’s working in the right way. Don’t just delegate all of your responsibility to that tool which gets things wrong. And then secondly, think about how you’re integrating that technology. Think about what you’re using it for and as I’ve said before, how do you use that technology to stay relevant?

Emmanuel: For me, the emphasis like all technology when it’s come out over the years is what can I use it for how is it going to enhance my business and how is it going to enhance my customer and customer journe and help me stay ahead of the competition? So those are really the points that I try and emphasize when it comes to tapping into AI’s potential within the fintech industry. 

Ratnadeep: Going forward many are probably working on their own ideas or already have an idea, and they’re looking to build a product or are building a product. What’s sage advice can you offer? How can they best harness AI to revolutionize their, probably not just product making process, but also kind of decision making process?

Emmanuel: What sage advice can I offer? Well I’m not sure I can offer sage advice. I can talk about my or our experiences. I think the first thing you have to do is understand your IT landscape. What are the pain points, what are the gaps, how you’re integrating AI into your tech stack so that it’s cost effective and adds value? You need to understand what are the costs involved in integrating an AI solution and getting the right expertise involved. 

So you do set up things in the right way and integrate it. How do you integrate? Just for example, generative AI within your existing tech stack and develop a robust architecture so one understand the areas where it’s going to examine the financial costs, the usage, the vendor costs. Do you need to upskill or reskill any members of your staff or your team? Where you’re going to integrate it? Get the right people on board to help you do that integration. 

There are all sorts of things to consider with regards to that integration. You did not just integrate your data, but obviously the tech solution, the tech article, architecture. And then I think, one of the key things, especially for large businesses is, you need to clearly, define and communicate what’s your company-wide policy on using AI. What are those? What can I access? What are those guidelines to use these tools effectively and safely? 

Can I take a piece of company data inputted into ChatGPT and say produce me this? So you need to define and determine what those company policy guidelines are. Maybe you can’t take that data, maybe you can use it in a certain way. But I know some law firms that specifically aren’t using ChatGPT and are thinking about, ‘well how do we build our own so we can manage our own data and not just pass that on to a ChatGPT or a third party?’ How can you stay ahead of the competition? 

Ratnadeep: Now as a key leader (CEO) at The Money Cloud, how do you ensure that your team stays ahead of the curve in this dynamic AI-driven data world? What’s your secret to maintaining the Competitive Edge in this ever-evolving domain?

Emmanuel: I think when it comes to any technology, upskilling, reskilling, constantly learning, traveling around the world, seeing what people are doing and what companies are doing in different sectors that they’re all, there’s always things to learn from people’s experiences and companies experiences in different sectors that may be applicable to our sector in our business. 

So for me, I always encourage our staff to learn to generate ideas to. We think about internally every month or every couple of months things that we’ve seen, things that might be applicable. How can we use those? Are they relevant? Are they not relevant? So in this world of fast music, fast moving technology, where six months, nine months ago, we didn’t have ChatGPT, how do you keep abreast of that? It’s the challenge and that means that you stay relevant and your business is always relevant for customers need. 

Ratnadeep: So yes for me, it’s continuity. I can see a few books in your room. I don’t know whether you have already read them or are currently reading, but can you suggest a few books for our listeners, especially in terms of technology or technology adoption or ethics in technology in any particular topic related to technology, which might help our listeners? Can you suggest a few books, or probably even podcasts, or videos which you watch generally or actively listen?

Emmanuel: I am reading a book which is a platform strategy, which is specifically about platforms, by Laure Claire Reillier and Benoit Reillier, which is very insightful. What else? Specific podcasts- Lex Friedman does a podcast. I’d listen to stuff that Sam Altman says, CEO of OpenAI. I can’t think of any others but yes there’s plenty of stuff out there to listen to read. Just find something that appeals to you and greatly so you can learn it from.

Ratnadeep: And here we have it. I think it’s a whirlwind tour of the year guided by none other than Emmanuel, a leading expert in the field of cross-border payments. His insights into the transformation of the financial landscape integration of AI and the balancing act between innovation and risk have been nothing short of enlightening. Emmanuel’s leadership dedicated to client-centric solutions and ability to forge a strong corporate partner Innovation ecosystem stands as a testament to the incredible potential that lies at the nexus of technology and finance. 

Ratnadeep: Thank you Emmanuel for sharing your wisdom and experience with us today and thank you listeners for joining us on this inspiring episode of “Leader’s Perspective”. Whether you are an industry expert or just curious about the future of finance, I hope you leave with a newfound appreciation for the complex dynamic world of fintech. Stay tuned for more thought-provoking conversations and until next time, keep challenging the status quo and keep leading with perspective. Thank you.

Emmanuel: Thank you very much, Ratnadeep. It’s a pleasure to be here.

What AI Holds for the Future of FinTech?

Get tried and tested formulas from experts: Leverage AI to step up your FinTech firm

Download the free PDF here!

Don't forget to share the post!

About Leader's Perspective Series

Leader’s Perspective- a podcast to unearth insights from exceptional trailblazers who have been at the heart of  defining strategies and execution of Digital Transformation initiatives in their careers. As organisations scurry to implement the Next Big AI initiative, the cognitive perspectives of our guests help you to find the winning mantra, or what might just save you from a pitfall. Let’s aim for collective wellbeing and implement successful data initiatives.