Embrace change, take risks, and disrupt yourself

Hosted by top 5 banking and fintech influencer, Jim Marous, Banking Transformed highlights the challenges facing the banking industry. Featuring some of the top minds in business, this podcast explores how financial institutions can prepare for the future of banking.

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How AI is Transforming the Banking Industry

The banking sector has always been data intensive. Today, AI technology has the capability to improve efficiency, increase innovation, boost differentiation, manage risk and regulatory needs, and positively impact the customer experience.

While more than 80% of financial services AI adopters say that AI will be very or critically important to their business’s success in the next two years, it’s time for banks to move beyond the hype and consider practical applications of AI across the entire organization.

On today’s Banking Transformed podcast, we have Imtiaz Adam, global AI influencer and founder of Deep Learn Strategies Ltd. He will share why financial institutions must make adoption of AI technologies across the entire enterprise a strategic imperative.

Jim Marous:
Hello, and welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous, owner and CEO of the Digital Banking Report, and co-publisher The Financial Brand.

Jim Marous:
The banking sector has always been data-intensive. Today AI technology has the capability to improve efficiencies, increase innovation, boost differentiation, manage risk and regulatory needs, and positively impact the customer experience. With more than 80% of financial services AI adopters saying that AI will be very or critically important to their future business success in the next two years, it's time for banks to move beyond the hype and consider the practical applications of AI across an entire organization.

Jim Marous:
On today's show I have Imtiaz Adam, global AI influencer and founder of Deep Learn Strategies Limited. He will share why financial institutions must make the adoption of AI technologies across the entire organization a top priority and strategic imperative. Artificial intelligence technologies are increasingly integral to the financial services industry. Banks must deploy these technologies at speed and scale to remain relevant, realizing that AI technologies are the foundation for value propositions and distinctive customer experiences. Unfortunately, despite the importance of AI, banks have continued to struggle to move from testing and experimentation to select use cases and scaling AI technologies across the organization.

Jim Marous:
Welcome to the show, Imtiaz. Before we start, could you share some insights into your background?

Imtiaz Adam:
Well, thank you, Jim. It's a pleasure to be here. And my background entails both investment banking, where I served as an executive director of Morgan Stanley, where I turned it into global business, their climate finance and renewable energy financing business, covering debt, equity, project finance, et cetera, private equity.

Imtiaz Adam:
And thereafter I went on to complete a Master's in computer science, specialized in AI, artificial intelligence, covering machine learning, deep neural networks, and even good old classical AI. So I've had quite a rounded background.

Jim Marous:
It's interesting. You're obviously a very avid proponent of AI. You share some of the most interesting insights on the power of AI regularly on Twitter and LinkedIn. I suggest that everybody tries to follow him, because every week or every few days he's sharing a major insight that really can guide your thought pattern along the power of AI.

Jim Marous:
To articulate a clear vision and to develop a roadmap for the future, why must financial institutions become AI first or AI involved?

Imtiaz Adam:
Well, that's a great question, Jim. And what we've seen in recent events, if we look back over the last two to three years, we've had this terrible pandemic. And during that period, a lot of firms that were digital resistance, or digital resistant I should say, suffered. And it was those who were more digitally agile and digitally prepared who fared a lot better. And we had a step change during that period when the physical world stopped or slowed down materially. And even the older generation, for example, my mother, who'd been risk averse to using mobile banking and online financial services, made that transition. And now she's not going back. Now she's regularly using her mobile apps for banking, her online services for insurance and other things. And that's just one example.

Imtiaz Adam:
The world is increasingly digital, and where you get digital footprint, AI follows. AI thrives on the data that we create. And the more data we create, the more we can apply AI to enhance both the customer journey and personalized services, but also to manage risk, which is what financial institutions are all about.

Jim Marous:
It's interesting, when you look at AI, and you've been looking at it not only from the lens of the financial services industry but really from all industries, how would you describe AI maturity right now in financial services? How well have we transformed as an industry and how well are we embracing AI and machine learning, from your perspective?

Imtiaz Adam:
I think, Jim, that's again, a great observation because we've got real dichotomy. We've got the likes of the JP Morgans of this world, where Mr. Diamond or the CEO has been a major proponent of that, and maybe a couple of the Canadian banks like Toronto Dominion and a few others. And then the one or two emerging FinTechs, who'd been much more aggressive with AI and investment into AI, and in particular data. Because until you get the data right, it doesn't matter what AI algorithms you're using, because they'll be garbage in, garbage out. The AI will learn garbage and give you garbage.

Imtiaz Adam:
Getting the data right, getting the data captured, the data systems right, is the starting point of that journey. But we've seen, as I mentioned, the likes of JP Morgan, the likes of some of the Canadian banks and some FinTechs, doing some really interesting things there, but then unfortunately we've also seen some big laggards. And these are typically firms that are way behind on their investment into the data side as well.

Imtiaz Adam:
So if you one hasn't invested properly into data and data capture, and if they're still running on things like COBOL ... I mean, it's funny, isn't it, Jim, that there are studies out there that showed banks are paying fortunes to retired COBOL developers to come out of retirement to put patches on their systems, because the younger generation of coders don't learn COBOL, it's a language that they don't use anymore. And yet, it's not a sensible way to do things, is it?

Jim Marous:
Yeah. I mean, you can't build a brand new platform on an old foundation. I mean, it doesn't work in a house, it doesn't work in data, and it certainly doesn't work in AI.

Jim Marous:
In the early stages, and you referenced this earlier, in the early stages of AI development in banking, the focus was really on risk avoidance and creating efficiency for the back office. How do you see AI creating revenue? How do you see it building better models for revenue generation as opposed to cost containment?

Imtiaz Adam:
Yeah, and that's a great point again. And you're right, that financial institutions, and not just financial institutions, some other industries, started with the application of AI towards managing risk, reducing costs, finding those operational efficiencies. And that is an important part of the journey. And I think for a firm that's new to AI, start small. Learn how the technology works, make it work, prove its value, and then scale it from there.

Imtiaz Adam:
But as you point out, the holy grail if you want, that's a good analogy if you like, is really taking AIT, enhancing the customer experience, and generating revenue from it. And there across a good proxy will be the big tech companies, the likes of your Googles, the likes of your ByteDance or TikTok, the likes of your Microsoft, who own LinkedIn, et cetera. So social media giants, your Amazons, and you have the big eCommerce platforms, these are firms that have applied AI very effectively to targeting tailored experiences to customers.

Imtiaz Adam:
I think the challenge with banking of course is that the consequences are greater for things like banking and healthcare, relative to social media and digital media and eCommerce. Because if you get an algorithm gives a bad result, maybe because of some faulty data in the training, it recommends a shirt that you don't like, or it recommends jeans when you wanted a jacket or whatever. But no one dies and there's no financial loss. And of course, when you come to financial services or healthcare, there are real world consequences.

Imtiaz Adam:
So I understand why there's been a degree of risk aversion, but I think the next stage now is scaling AI, getting the data right, getting it ready to really grow into these customer services. As things like the metaverse develop, the metaverse, let's say two years from now, when glasses really take off as 5G scales, people are going to go for hyper personalized AI driven experiences. I think the next two years are going to be really exciting where we go into this world, as you point out, where AI becomes more and more about revenue generation and creating these personalized experiences to the customer.

Jim Marous:
Well, it's interesting because the whole concept of revenue generation from my perspective is really about value transfer. It's really about, if the financial institution can use AI to build better experiences, to build better recommendation engines, and can do what's being done in retail and travel and hospitality, two things happen. Number one, you're meeting customer expectations, because the consumer has seen it elsewhere, they expect it from their finance institution. But just as importantly, if the bank or credit union does this well, then the consumers can be more apt to pay more for those services, less likely to leave them, there's greater loyalty and greater revenue generation. Excuse me.

Jim Marous:
I think when we look at it in the context, there's a tremendous upside, that even the biggest financial institutions who we've had on the show before, say, "We're really lagging. We're not really where we need to be." Especially when it comes to recommendations engines, that you can look at what a consumer's done, not just transactionally, but in the whole of their life, from what you can get from the outside world. We're not really responding to that very well.

Jim Marous:
On the other side of that revenue generation is really looking at unrealized opportunities. How can AI help in the innovation field, help in the whole idea of both the speed and the deployment of insights for the innovation cycle?

Imtiaz Adam:
Again, great insights. I mean if you think about it, Jim, as you're putting it and where you're alluding to, is that financial institutions have a lot of potential understanding of their customers. They can see their customers from a 360 perspective. And what do I mean by that?

Imtiaz Adam:
I mean, they see their totality of what their customers are spending on, so they understand where their customers want to go and spend, what major items they're buying. And they can often start correlating and understanding what they might want to be buying next or what their missing things are. If they can see that somebody's buying an airline ticket, have they got the right insurance in place? If they can see that somebody's maybe spending a bit too much, they could offer some kind of financial health signals to them to make them aware of, "If you carry on at this rate, you're going to go broke or be in financial distress."

Imtiaz Adam:
There are a lot of ways that financial institutions can start using AI and predictive analytics from the AI side, forecasting the future, and trying to help the customer along the journey. That's one element. The other thing though, Jim, as you pointed out, is that whilst for some firms right now AI might be viewed as a nice-to-have, a luxury, two to three years from now it's going to be, if you don't have it, your customer probably won't be with you anymore. And especially as I said, as we go into the world where the metaverse really starts to scale and take off, and the IoT, the internet of things, et cetera, that people are going to come more and more custom to that. And they're going to start demanding that level of service.

Imtiaz Adam:
And you already see it today, Jim, over the last few years in the physical stores in retail, where the customer now doesn't want that experience in the '90s or early 2000s, which we will recall, when you might have stood at a checkout for a long, long time or waited for a shop assistant to come guide you around. You got a generation now who are really accustomed to the ease of service, of Google Search, of Amazon, et cetera, who are expecting that convenience in their retail life.

Imtiaz Adam:
And increasingly, apart from the retail banking side, it's also going to grow across even the corporate banking and the capital market side, where AI is only in its infancy in making inroads. So when you go all the way across the value chain of the financial services, it's going to go right through there and really affect the customer interaction.

Jim Marous:
We've talked about all these benefits of AI, yet the deployment and the application, the building of AI and machine learning platforms has really not kept pace. From your perspective, what obstacles are preventing banks from deploying AI at scale? What is getting in their way?

Imtiaz Adam:
Yeah, that's a very good point. And if you go back four or five years ago, around 2017, 2018, even 2016, I'll be honest with you, it was difficult to build these deep neural networks in large complex models. When we look at, for example, the customer experience, some of the important areas there are things like language, natural language, and the ability to do good things with text and with voice, et cetera. And there were a lot of time models out there and they seemed exciting, but they were hard to scale when you tried to put them into big production environments.

Imtiaz Adam:
And the other thing is with natural language, is that there are a lot of models out there that you can use, but they're trained on general language. But when you come to finance, you need to make them finance specific. For example, a lot of the work that I did with my team, and sometimes people say, "Oh, you disappear from social media for a while. What are you doing?" But that's because, as you know, Jim, I'm actually working with my team, building actual AI models, machine learning models or deep neural networks.

Imtiaz Adam:
And for example, we built something for finance where we tailored the language. And I had to sit with the team as a domain expert in finance, and we made some over 50,000 examples, sitting on the screen, labeling the relations for finance specific information. And that took two months to do 50,000 examples, and then train the machine on that. And now we've got something very bespoke and customized. We used a baseline model that was trained on general language. And then, without trying to get too technical, we did something called fine-tuning or transfer learning on a smaller data set that was specific to finance. But if I made bad errors on the inputs on the finance side, then the model was also going to learn garbage. So you had to be careful and keep on testing it again and again.

Imtiaz Adam:
It does require working closely with ... Bringing engineering experts in, data scientists, and finance expertise together, bringing that cross-purpose team in so that there's clarity on what the objective is, what you're trying to deliver, and what data you're going to work with and what you want to, as we said, your end goal is. And then working as a team to build that. And yeah, it is hard work. It's got better from where it was four years ago, but you still have to do a bit of hard work on that. Look, let's be realistic, at times it is slightly painful still, but it's a lot better than it was four years ago.

Jim Marous:
In our research also, we see that in many cases, financial institutions lack of clear strategy as to how to implement a data and AI strategy, just not having a strategy, it's obviously a big problem. Also, obviously, without anybody doubting this, the lack of a strong core technology and operating models that are outdated really work against you. Because if you can't build something from a strong foundation, again, your output, as you've mentioned a couple times here, is not going to be strong. And then on top of that, we have an inadequate talent supply. The reality is, it is very difficult to find the right talent available.

Jim Marous:
With these things in mind, should the majority of financial institutions be working to engage third party organizations to help with their data and AI strategy, that can really get them up to speed quickly, but also can transform them in an agile way so that they can keep up with what's going on in the marketplace?

Imtiaz Adam:
Yeah, again, fantastic insight. Let's start with the starting point, is that it all starts with the top management, but it's all about the C management and culture. Culture is the most powerful thing in a company. And it's often also it can be fundamental to the successful features of an organization, but it can also be fundamental to the barriers and the barriers to change for an organization.

Imtiaz Adam:
So if a firm, be it a credit union or a retail bank or an investment bank, is going to go down the pathway of becoming a data-driven, analytics-driven, AI-orientated company, that has to be sponsored from a very top management. From the CEO and the C team have to take the ownership and give that sponsorship through there. And they need to give that back into that team, invest in it and grow it. But that team also needs to be integrated with the business teams.

Imtiaz Adam:
When you look at Gartner and some of the reports they've done on other leading firms out there, and I think you are alluding to this, Jim, is that where AI machine learning or data science projects fail is this lack of clarity and objective. So your technical team can build something that's technically solid, but it doesn't solve any business or customer problem, because the business team and the technical team or the data science team didn't understand each other.

Imtiaz Adam:
If you don't have that cross-team cohesion and collaboration, then it's going to go wrong. It needs to be sponsored by the C team, and these teams need to work together. And they must not view each other as competitors arrivals, rather what the business team have to view and understand and the data science team have to also make the business team understand, is that they're there to help each other. That the data science team are there to give tools to the business unit that, as you pointed out, could generate enhancements in marketing, in customer retention, in customer growth, in revenue, but they have to understand each other. That's a key thing.

Imtiaz Adam:
Then, as you pointed out, the talent. Invest in people. It's not going to happen overnight. You're going to grow that team, nurture it, give it support, and then scale it internally. So these were important routes. Now if you want very quick fixes or if you want immediate solutions, then yes, you are going to have to look to third party solutions. And you probably are going to have to look to third party firms to come in and help you. But be careful who you go for, because again, a lot of people say they do AI, but when you look carefully at what they do it isn't really AI. Try to make sure that they have genuine expertise in the field. And also that they understand your domain, which here is finance. Can they bring solutions that are going to actually help you from the business side?

Imtiaz Adam:
And it's probably going to be a mixed approach, whereby some solutions you will go for external parties to come in and help you. And some you'll try to build internally and nurture that, but it all starts with the C team.

Jim Marous:
It's interesting, while you're based in London, you've really taken on a global view. You're an observer of basically all regions in the world around where they are as far as AI, which industries are doing the best, and what's going on in the financial services industry.

Jim Marous:
If you are going to give a couple examples of organizations that you believe, or financial institutions, that are really doing well in the utilization of data and AI, who would be a couple leaders? Because I get this asked all the time of me, where people say, "Okay, but who's doing this well?" And I take a stab at it, but you're really looking at it from a somewhat narrow lens, looking at who's doing the best in data and AI. What are some institutions that really stand out to you?

Imtiaz Adam:
I think a couple of the Canadian banks have really made big investment. They look to get away from the COBOL infrastructure to go for modern data infrastructure, and to really try to experiment with AI and have that creative element. And what they do is they create sandboxes, they create research teams, and they let them get on with the experimental side and then see how they can operationalize it later.

Imtiaz Adam:
And so there you're looking at the likes of Toronto Dominion, Royal Bank of Canada. I think those guys have done some really great work, so kudos to them. And then of course, Jamie Diamond, JP Morgan, has been a big, big advocate. Look, I mean, they are rumors sometimes of cultural issues, that the machine learning type of people sometimes get frustrated and leave. They prefer going back to more creative Silicon Valley mentality. But of course the bank, and fortunately or unfortunately, whichever way you look at it, is always going to be much more tightly regulated than a Silicon Valley major.

Imtiaz Adam:
So you have to be aware, if you are a machine learning, a data scientist expert, that if you go to someone like JP Morgan or a big Wall Street major, or even at a retail bank, but the regulatory requirements and rules are going to be a lot stricter than if you're in a fast growing Silicon Valley startup or a big tech major. That's just going to be part of the nature of the beast. So if you accept that, then you can understand that mentality.

Imtiaz Adam:
But at the same time, I think what the US banks and institutions, and indeed many others around the world, across Europe can do, is look at how Toronto Dominion, Royal Bank of Canada, I think maybe the likes of one or two of the Australian banks have also been adapting AI and trying to create a more creative environment where you can have the research side. Because the research side, to be honest with you, is not going to give you immediate wins. It's going to allow you to extend the potential of AI and see where the experimental side is. But then you can start learning from that what you can take away to operationalize.

Imtiaz Adam:
But then on the operational side, of course, and with any financial institution and especially with the Wall Street banks, you're under huge pressure to generate returns quickly, including on the data science side. What that means is that often people will go and grab the low hanging fruit, but it means the more racy, dare I say, sexy, exciting areas, which need maybe a bit more time and work, are perhaps going to be harder to make the investments into if you require immediate returns very quickly.

Imtiaz Adam:
It's about being realistic and for the bank's CEOs and C team to go, "Okay, let's stack these up and understand. For X, that's low hanging fruit and we can see the return on investment coming from that fairly quickly. But for Y, maybe that's going to take a couple of years to really build it up and get it to where we want it to be. And all right, we accept that and we're going to have this tailored approach for different projects."

Jim Marous:
Let's take a short break here and recognize this sponsors to this podcast.

Jim Marous:
Welcome back to Banking Transformed. I'm joined today by Imtiaz Adam, one of the foremost authorities on the deployment of AI in banking, we've been discussing how financial institutions can maximize the impact of data and AI across the entire organization.

Jim Marous:
Imtiaz, in speaking with financial institutions, they're often hesitant to engage in AI modeling because they see it as a security risk in sharing data. How does a company help to mitigate this risk, and how do they get to feel more comfortable about the use of data with the development of AI solutions?

Imtiaz Adam:
Yeah. No, again, this is a very important point, Jim, and there is a data barrier because financial services, like healthcare, have strict rules on data and privacy, et cetera. Of course, again, the C team and the regulatory team are going to be nervous or anxious to make sure that they're not breaching those rules. And that's been a similar barrier with healthcare.

Imtiaz Adam:
To really scale AI, in particular machine learning and deep neural networks, you need to grow the access to the data. How do we get around that? And I think there are three ways. One, again, as we mentioned, getting investment into more modern data systems, more modern languages, et cetera, and databases, so that we can understand what we have and efficiently scale it.

Imtiaz Adam:
The second is, there are new techniques, I don't want to get too technical here, but there's one known as federated learning with differential privacy, which I think is going to be very important in the future. And not just in banking and in healthcare, but also in things like the IoT and metaverse as it scales into our homes in the future. And maybe you don't want Mr. Zuckerberg, not saying he would do this, but you don't necessarily want him to see everything that you might be doing when you're wearing AI enabled augmented reality glasses, et cetera, mixed reality glasses in your home. Now these techniques learn, this technique of federated learning, learns locally and never removes the data. And then updates the technical side of the model globally, and then goes back out to everyone. So you get collaborative learning, but you manage to retain the data privacy at a simplistic level.

Imtiaz Adam:
The third is called synthetic data, and synthetic data I think will be very big with the metaverse because you'll be able to generate a lot of what's artificial data. But if that artificial data is good enough quality, then you can really start extending the capability of AI dramatically across financial services and other areas like healthcare. And indeed, autonomous driving, there's some startups now, AI startups, that are doing huge scaling using the metaverse and virtual reality to create a lot of new scenarios for autonomous vehicles and robots to get them to keep on learning dynamically new situations.

Jim Marous:
It's interesting, a lot of people believe that AI means replacing humans, but that's not really the case. I mean, you still need humans involved in the process. So what roles do humans play in the research and operation of AI?

Imtiaz Adam:
Well, again, Jim, it's a very good point. When you look at the first stories that, and I won't give the names, but some of the consulting firms were putting out in 2017, 2016, and 2018, around that time, on mass unemployment by 2020 from AI, didn't happen. We did have a spike in unemployment, that came from COVID, but we didn't have ... And the physical world shutting down for a while, but it wasn't AI-causing. Now many of the firms are saying, "Oh, well, yeah, AI will cause some job losses, but it will cause more of a net gain in jobs."

Imtiaz Adam:
So in actual fact, what it does require though, the points you are pointing at, which is investment into skills training. And it doesn't mean you turn everyone into coder, because a lot of the opportunities will be on digital marketing, digital media, the creative side. What AI does, what machine learning does, is it allows us to crunch the data in a way that even a huge army of human analysts will struggle, because we're creating so much data now with such high velocity and veracity, which is a different type of data, and volume. So it is very hard for humans to keep up to speed with that, even a army of humans.

Imtiaz Adam:
The machines are very good at doing that very quickly with AI and they can find hidden patterns in the data. And then you can give it to a marketing team, a digital team, a finance team, to go an action the findings and do the targeted interactions with a customer. These are just a few examples of how you can get machine-to-human collaboration for [inaudible 00:29:23] scenarios. Their techniques, again, I don't want to get too technical, but their is known as transformers, so transformers with self attention that are revolutionizing AI right now or deep neural networks. And that's why language has really extended dramatically from where it was three or four years ago. And why you're seeing big improvements in things like Google Translate, and some of the voice systems, and some of the translation services, and indeed text analytics from what there were three or four years ago. And now, as I say, it's about making it specific to areas like finance and healthcare so that you can scale them within those areas.

Jim Marous:
How will AI be a differentiator for those organizations that really embrace the concept? I mean, what is the biggest difference that you'll be able to see between those organizations that embraced AI and are on the front end, and those that are the laggards?

Imtiaz Adam:
Jim, you've already planned that out. The world that we'll be in two to three years from now will be all about hyper-personalization, and customers are going to expect that automatically. And so those who've invested into AI properly and their data systems are the ones, two to three years from now, they'll be able to offer that hyper personalized service and customization to their customers. And whilst today that might seem like a luxury, two to three years from now it's going to be a necessity. So I would say it's important to invest in that journey now.

Jim Marous:
And finally, you're looking at the broad sense of AI and machine learning, and even getting out of the financial services, eventually it's all going to start being used similarly. What is your overall prediction as to what's going to happen with AI and machine learning in the midterm?

Imtiaz Adam:
In the midterm, Jim, AI, as we partner out, is going to grow beyond social media, beyond digital media, et cetera, and eCommerce, and come into the real world sectors more and more, or what I call the real world, which is financial services, which is healthcare, transportation, et cetera, et cetera. And especially with the scaling of the Internet of Things, as we get more and more what's called standalone 5G, and standalone 5G has been slightly delayed in the US and the UK and certain other countries, because when we had the fight with Huawei, et cetera, we ripped up the Huawei equipment, and then we had to go for new 5G infrastructure. So that certainly delayed what's called standalone 5G because, often the 5G, over the last year or so, was piggybacking off 4G infrastructure.

Imtiaz Adam:
The real magic's going to happen as we get more and more of this standalone 5G in the US and elsewhere, in the UK, et cetera. And that's what's starting to happen now because that will enable a big transformation in device connectivity, because often 4G networks are close to maxed out in capacity and they struggle. But when you get to standalone 5G, you can get this huge gain in the number of devices per unit, per square mile, per square kilometer, whatever, that you can add onto the network. So you can add a number of machines that communicate with each other and you get very low latency as well, unlike 4G. And that will enable things like virtual reality and augmented reality or mixed reality that struggle with 4G, because of something called latency, to work as intended with standalone 5G. So you're going to get this huge transformation.

Imtiaz Adam:
Let me give you an idea. Statista, for example, they point out that by 2025, they forecast that there'll be 75 billion internet connected devices in this world. What does that actually mean? Well, they say now there's about eight billion people on this planet, that's over nine internet connected devices per person on the planet. That's huge. And then we talk about big data today, but in 2025, with all these devices, IDC Seagate, another consulting firm, forecast that we will have 175 zettabytes or 200, whichever number you believe.

Imtiaz Adam:
What does that actually mean? Well, it means that we'll be creating approximately three times more data than we did in the entirety of 2020 or approximately 2021. And one third of that is going to be data in real time. So if it's going to be realtime data like video analytics, like live streaming, like virtual reality or AR, that's going to need machine learning and AI to manage it and make sense of it. This is going to be a world that is very analytics, data-driven, and very much AI at the forefront. And it's coming within a few years, it's not far away. '25, '26 is only a few years away. It will go in a blink of an AI.

Jim Marous:
Yeah. Catching up is not going to be an easy process, where it's going so fast and it's getting harder and harder to play the game of catch up.

Jim Marous:
Imtiaz, thank you so much for being on the show. It's been too long since we've seen each other in person. We'll do that very shortly, I promise. And thank you again, I appreciate all your knowledge. And real quickly. How do people keep in touch with you or follow what you're doing?

Imtiaz Adam:
Jim, my handle on Twitter is @DeepLearn007, or you can find me on LinkedIn, Imtiaz Adam. If you search under AI or artificial intelligence or Morgan Stanley, you should find me

Jim Marous:
Again, thank you so much. And again, thank you for sharing all your knowledge with us on a regular basis, but also on the show today. Thanks a lot, Imtiaz.

Imtiaz Adam:
It's been a pleasure. I really always enjoy talking to you.

Jim Marous:
Thanks for listening to Banking Transformed, winner are three international awards for podcast excellence. If you enjoy what we're doing, please take some time to give us a five star rating on your favorite podcast app. In addition, be sure to catch my recent articles on The Financial Brand and the research we're doing for the Digital Banking Report.

Jim Marous:
This has been a production of Evergreen Podcasts. A special thank you to our producer Leah Haslage, audio engineer Sean Rule-Hoffman, and video producer Will Pritts. I'm your host, Jim Marous. Until next time remember, AI is neither good nor evil. It's a tool. It's a technology for all of us to use.

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FedNow: The Future of Instant Payments

Banking Transformed
Nick Stanescu, FedNow Business Executive, Federal Reserve Financial Services, provides an update on the future introduction of the FedNow Service ...
Listen to FedNow: The Future of Instant Payments