VO: Artificial intelligence is rapidly transforming the world around us. And the financial services industry is no exception. In episode three of this season of Zafin Banking Blueprints. Host, Dharmesh Mistry is joined by Tyler Pichach, Head of Banking Strategy at Microsoft, and Branavan Selvasingham VP AI and Automation at Zafin to explore the exciting possibilities of AI in banking. Discover how generative AI is revolutionizing productivity, accelerating product development, and reshaping the very operating system of financial institutions. We’ll delve into the rise of AI co pilots and learn how they can streamline processes, enhance customer interactions, and even combat financial crime. Hear real world examples of banks using AI within platforms like Microsoft Teams to achieve new levels of efficiency and customer engagement, Join us as we blueprint the AI powered future of banking. In this episode of Banking Blueprints.
Dharmesh Mistry: Welcome everybody to the second series of Zafin’s Innovation Beyond the Core podcast, This week’s special guests are Tyler Pichach, and Branaman, Selvasingham, Tyler you’re from Microsoft. I would love to find out more about your role within Microsoft, but also get a sense of the bigger picture of what Microsoft is doing in the space of AI.
Tyler Pichach: Thanks, Dharmesh, Happy to lead in. So my team is responsible for effectively our strategy and how we think about financial services around the globe And by financial services. We break that down into three core verticals of banking, insurance and capital markets. And of course, there’s lots of lines of businesses within each of those. So we spend our days thinking about how Microsoft and Microsoft’s partners can support each of those verticals and the lines of businesses within them. And we think about that really these days with a couple of key things that are first and foremost. So, the number one thing we think about at Microsoft is security And that underpins everything we do, We are one of if not the most trusted organization in the world and we need to maintain that And we think about that not just from how Microsoft creates technology, it works with our partners, works with our customers, but also how our partners build on top of our platform and how they think about security Because at the end of the day, it is an ecosystem and we’re all in this together including some of our competitors as well. So security is the number one thing. And of course, that applies in a big way to all customers, especially financial services, and regulated industries. The second area and it’ll be no surprise. Here. We’re all thinking about AI and how do we improve? You know, organizations, consumers, everyone’s lives moving forward. You know, how do we empower every individual and organization of the planet to achieve more? And this is a really fantastic opportunity for us as of course, everyone in the world is thinking about AI and what it can do. And we’re already seeing some really amazing gains As an example when we talk about generative AI specifically, we’re already seeing customers… on average with productivity gains of between 22 and 30 percent. We’re seeing that translate as well into revenue models. So already we’re seeing on average a six percent increase in revenue. You know, it might seem like a small number, but actually it’s quite substantial and it’s still early days. And one of the ones that I’m really excited about or we’re really excited about at Microsoft is also when we think about, you know, product development and how much faster products are now coming to market as a result of AI including generative AI. And we’re seeing anywhere between 20 and 40 percent accelerations because of the use of AI across these products. So it really is a substantial change in not just this cool technology that’s now come out, but actually how it’s being used. And what we really love is we’re just getting started. So, super exciting. Lots more to come And again, thanks for having me on today.
DM: Yeah. I mean, it seems like the pace of like progress in this in AI is just relentless. I mean literally from like one year to the next. It’s spellbounding the improvements that are being made. But I’m a little bit, I just want to probe a little bit longer with like your biggest strategy. I mean, what I’m seeing I guess is like co pilots for accountants and co pilots for, I guess is there a co pilot for banking? And, you know, what aspects of banking might that cover later on? What is the co pilot strategy here?
TP: So when we think about artificial intelligence, AI first, it means a lot of different things. And so one of those things is being able to have something that can support individuals and businesses in their flow of work. And that’s really where the co pilot strategy comes from Helping hands, something to really augment and make more efficient, more productive, Your daily work That’s our strategy. And so if we think about, you know, a co pilot for banking or a co pilot for insurance, we don’t necessarily think about it as a it’s just one one co pilot for all. It really is a co pilot that has specific tasks in mind. And those tasks may change dependent upon the user and how they want to use it. But at the heart of, it is really about augmenting human capabilities to be more efficient to become more productive. And in a number of different ways.
DM: And these kind of specialized AIs in effect, is that so that they can be the very best at a given task? Or, you know, at some point will there be the, you know, the father or co pilots that amalgamates? Everything?
TP: Yeah. You know, there’s a couple of different approaches. So when we think about the underpinnings of a co pilot that’s typically the language models, We often focus on our large language models like the ones that we have with OpenAI, But, you know, there’s also small language models that are being created that are very specific and trained on specific tasks or specific functions. And those can do very well also. And Microsoft’s approach is that we are very open to saying, hey, let’s look at all the different types of language models that are there and let’s make sure they are for specific tasks as well as broad ones as well. And we’re still learning… I think the whole world is learning on how to approach that In terms of, you know, an AI that can talk to an AI, a copilot that can talk to a copilot, it’s certainly within the art of possible. I think we’re still exploring that We can, you know, dovetail into that a little bit further in the conversation. But yes, all those things are coming. You know, we, We traditionally talk for example about Moore’s Law, you know, this idea of technology doubling every couple of years. And we’re now way beyond that right now Technology is effectively doubling every six months. So where we go from here is just an incredible journey that we’re all on together. We’re all figuring this out and learning.
DM: Wow. So Pranavan, I mean you’ve got a bit of a history with AI as well. Haven’t you And love to hear about your background in AI And, you know, tell us a bit about your role within Zafin as well, please?
Branavan Selvasingham: Yeah, absolutely. So Zafin has been about a year in now and it’s been just go And it’s been like a phenomenal time to be part of like all the initiatives that have been happening here. And I lead our AI initiatives both internal facing, as well as external facing because there’s lots of interesting vectors on both sides of the spectrum there. And prior to that, I was in consulting for about say like five years give or take And again focused on conversational AI enterprise search, but also underpin with some research and development on robotics. So it’s just kind of really scanning or spamming that gamut if you will of various aspects. But they’re somewhat connected as we kind of get into some of these discussions. And prior to that, I had my own startup where I was kind of throwing myself into, you know, early ML. It was back, in like maybe more relevant to this conversation. Prior to that. I was actually at Microsoft and I’m not sure if Tyler, I mentioned that to you before And so that was an interesting time where Cloud was just getting started. And so, it’s been this phenomenal trajectory where you kind of saw a lot of the infrastructure that is now coming into play, you know, build out and then getting into full maturity. And then now you see the models really taking advantage of it and really taking it to the true meaning of the hyperscale And so, yeah, it’s been a phenomenal thing to observe and be part of in my own little way. And yeah, so that’s been my I guess journey through AI over the last maybe six, seven years. And yes, now focused primarily on our implementations with generative AI and how we can bring that to banking and through a number of things that we can kind of get into in more detail. But I’ll stop there because there’s a lot to unpack?
DM: Yeah. I mean, before we get into like, let’s say how Zafin are using AI, what’s your take on the bigger picture? Like, how should we think about AI from your perspective?
BS: Yeah. So like we’ve been like I said, we’ve been on this journey for the past, you know, slightly over a year. Our vision for what you know, we set out to do was really formed like about eight nine months ago And we’ve been kind of executing against that vision And that vision and strategy rests on, I would say fundamental macro insights. And I think the first big macro insight that, you know, it rests on is what we’re seeing now with generative AI, conversational AI. And what I’ve seen in the past and in my prior experiences is the application of that technology in the conversational space. So chatbots if you will. And it’s a fantastic place to apply it. There’s lots of value to be had there, But that’s not actually what’s happening in terms of the long term of where this is really going. Where it’s really going. Is we’re seeing for the first time in a very long time the formation of a new OS. And I firmly believe this and this is not as bold of a statement anymore. It was when we were really kind of starting off that vision. But now it’s fairly obvious and I think accepted by the majority But that’s really where we’re going. And that’s also where a lot of these large AI labs are targeting their focus and investment towards. And you don’t have to take my word for it. This is things happening. This is even like there was a three minute section in Andrej Karpathy’s, talk like eight months ago where he goes into the details of actually what’s happening here. But it was only three minutes out of like 60 minutes discussion, But the nuggets were there. And so this is happening. So this is one macro insight that we’re… seeing for the first time The emergence of a new category of OS that is at the likes of Windows and Mac when it first came out and all the things, that unlocks for us as we think about like all the analogies of like how we went from, you know, we used to interact in the form of DOS type terminal command lines and so on. And we would get back responses and we thought that was great. And I almost feel like analogously we’re in that stage where we’re interacting with this new OS through this chat interface and it’s very similar to what we did back then with like DOS or terminal or like a Mac terminal, But where I think we’re about to get into and we’ve kind of already gotten into it is the GUI Like when we went from DOS to GUI back then. That was a massive expansion of accessibility of the actual platform and the… compute capabilities to pretty much everybody. And then we saw touch come in and so on. But really like where we like that shift is also happening now in the form of multimodal, where we’re going from this chat text interface to vision, you know, both recognition as well as, you know, image generation, voice recognition as well as voice synthesis. Just all of this multimodal aspect I submit that is akin to that shift to GUI back then in this new OS And that really will kind of, I think it’d be a phase change or like a hockey park, you know, or a hockey state type of a change. So that’s happening. That is one fundamental like macro shift as well as a macro insight. So what does that mean to us? It means a lot to a lot of people. But what it means to us is that we now have the opportunity to go after a bioengineering bi directional gateway. And what I mean by that is like for the first time, you don’t need to or at least up to now, we always go to the platform Like we go to the technology, we log into the app, we go to our devices and access the capability and, you know, and then interact and consume and so on. But I think for the first time we can truly go after it in an organic way where it’s a bi directional channel or a gateway to that platform where you can go to the platform just as you do now. But the platform can also reach out to you at the appropriate time contextually relevant like all of those things applied. But I think now we can actually do that with real relevance as opposed to before we just send a notification. It’ll be a bell icon and you can choose to ignore it’s. Just a static aspect of it. But now it’s a deeper conversation starter or interaction starter where you receive let’s say a signal or an alert that something has happened or something is worth your attention. You can say, well tell me more What should I do about it? And you can actually go off and action it all within your context without leaving that context to go to that other app. So that’s an important next opportunity and a macro insight that I think we’re going to start to see more of And we’re already seeing it That’s exactly what we just talked about which is co pilots, Like we see co pilots for everything, Right? And it’s starting to get quite, you know, some people say it’s quite confusing This. Copilot for this, you know, there’s a GitHub copilot which we love, Microsoft’s copilot. Now, there’s Zafin copilot. And there’s so many other copilots. But I would say if you instead of thinking of it as copilot, if you instead substitute the word app, then it just makes sense Like there’s an app for everything at least now in terms of mobile apps. But now going forward in this new OS, there’s going to be this copilot for pretty much anything you need to do or get done. Yeah, Right. So that kind of just deflates that pressure or the disambiguation aspect of it. And so that’s another aspect that’s coming. So that’s macro insight too. And then now, what does this really open up for us? And also what does this do for the consumer in terms of behavioral changes that we’re going to observe? And that is going to be that now we can actually meet the user where they are. And I know Microsoft has said this in the past and has been on this mission as well and so has a lot of other companies. But I think now we can truly go after it because if you kind of put the building blocks together, new OS, invisible asynchronous, instructable, you know, in the background bi directional gateway, meet the user where they are And why it’s more relevant now than before is because like when you think about how we actually get work done, like, I, you know, I start my productive day by going on Teams, Right? And I would say this even if Microsoft wasn’t in the room like, this is the truth. And of course, for others, it might be Slack… Or for, you know, sometimes after work, it’s Discord servers or something. So people are spending time. They’re not really spending time on like a www, something com Like they’re spending time in these networks and actually getting a lot of work done. It’s very productive. And there’s also a bit of a I would say a byproduct of what also happened with the public Internet where it’s like there’s been this stratification if you will Of the actual public Internet where you think about like, you know, where do you really spend your time? Like do you really spend your time on www, whatever com, or do you actually not trust or at least not really spend productive collaborative effort on somebody’s? Public aspect? Because a lot of, a lot of the stuff in the public Internet which there’s this. Notion of a dark forest that’s been proposed. I can’t remember, I think it was Maggie Appleton who kind of Maybe the article. Yeah, Go ahead.
DM: Interject a little bit because I just want to kind of delve on this Teams bit a little bit because it sounds like to the uninitiated and naive person like myself, it might seem like, well, hold on. How do we get from AI to a, you know, like an alternative to Zoom, right? But are you and don’t you know, Don’t let me paraphrase you If I’m wrong, just say so, But are you saying that Teams is more than just like a visual, you know, video conferencing tool? It’s also scheduling a specific aspect of your life and allowing you to interact. Let’s say, you know, I’m a bank manager, I’m a product manager. I’m a developer. It’s organizing the aspect of my life. Is that, is that what you’re saying?
BS: Yeah. I mean, I can be a bit more prerogative than that. And yeah, in the sense of like, so we’re all familiar with like, these concept of super apps, right? And we’ve you know, for those that have traveled to Asia and, or have friends that have talked about it, you know, the concept of WeChat, right? The super app where you can get everything done, That’s been something that, you know, a lot of the organizations in the West have thought of as like this Holy Grail or at least this, wow, what a concept We would love to have that Like X. Com, you know, is obviously explicitly wanting to go after something like that. Whatsapp same thing. So, but what I would submit is we already have the enterprise version of these things, right? And Teams is an enterprise version of the super app where you get your work done. It doesn’t like period Like you don’t like you don’t really think you can access your Word, you can access your PowerPoint, you can do whatever you want. Obviously, you get into all these conversations which, you know, a large part of work, is these conversations, meetings, conferences, discussions, brainstorming, whiteboarding, That’s a big part of how we actually do work now. And so like I think it’s this one place where you can also reach out to customers like your customers’, customers. Obviously, we can reach out to our customers. We have conversations conferences seamlessly through these platforms with our actual customers. But then their customers are also within reach because there’s telephony and all these things built in. So, like what I would submit is this is actually an important platform where there is intrinsic trust for the user on the other parties that are within this, you know, quote, unquote cozy web, you know, concept there where they trust the other agents or the other parties. And so they stay or at least spend as much time in this platform And for us to be there, That is a very important. It’s both a signal that we are also part of that innate trust earning party. But at the same time, we’re there where you are. So you don’t we don’t need to take you out of wherever you are to come. Meet us, where we are, We can meet you where you are in the truest sense of where that’s going. So that’s kind of that’s an important part.
DM: Is a co pilot AI as well as any other people that need to be involved
BS: We, as a platform, We as a platform.
TP: Why don’t I jump in guys and I’ll just elaborate. We can speak to a little bit of the strategy on that. So, so taking a step back, I totally agree, You know, the ability to think about teams as a super app is certainly something that is interesting to so many people. There’s over 350,000,000 monthly active users of teams. And primarily those are business users. So if you think about that interconnected business community, it’s pretty massive. I would challenge us all to think about, you know, what are those core use cases? Technology is cool and we love the idea of bringing things together but it’s certainly in the business world, it’s a bit different than the consumer side of things. So how do you think about B to B or B to B to B transactions, let alone B to B to C, right? And so think about use cases, think about trade finance, and many others that grow in that world, Think about CFOs, think about procurement, those types of things, Lots of opportunities in that interconnected community. What’s really exciting about with teams is we actually have financial services firms including banks that are actually starting to build their own applications directly within Microsoft Teams. And so Dharmesh, you asked earlier a question of, you know, are there financial services, things that are happening? And yes, it’s early days, but they’re starting to see this as a effectively as a channel, right? Very similar to how you just described it. So that path is there. And then we’ve got other partners Including Zafin that are building into teams as well. So all of those things start coming together, those ingredients, right? In a consumer world, it might be things like travel and shopping. But in a business world again, it might be more things like financial services, like banking, like procurement, like, you know, things tools that CFOs need, those types of things that come into it. What’s really exciting of course is the AI element. And so you have this thing built within teams, for example, called M3 sixty five Copilot. And that Copilot is extensible And it’s built on something that we call at Microsoft a Copilot Studio or Cloud AI Services. And so effectively any organization that wants to be part of a we’ll call it a business super app. Brandon. In the way that you were kind of thinking about it, I think is now able to not just build their application but build a Copilot on top of that, so that it can actually speak to the data that’s injected across teams from multiple different firms and not just speak to it but bring it together and do what you know, generative AI does summarize things, right? Kick off workflows, add notifications, think about the moments that matter. And then the other part which is really interesting about this. And it goes back to this whole augmenting strategy which is, how do we think about personalization, right? Not just in the consumer world but in a business world as well? And so then it gets in again to saying, hey, what are the moments that matter? How do we know about those moments that matter in those workflows? Not just your personal life, but your business life as well? And then how do we bring up not just as a notification but as a, in the moment that matters, you know, dialogue conversation that has now these networks of data across multiple different applications or data sources that can bring together all into a unified experience, i e, embedded finance or the super app journey that we could have in Microsoft Teams. So, 100 percent agree with everything you guys are saying. We are absolutely on that path to do that within Microsoft Teams. This is really an enormous place of growth. I think where the world is going to start seeing things because everything becomes connected in that way. And then Brandon, you know, that becomes effectively another layer of that OS that you were talking about.
DM: Tyler, I mean, you’ve alluded to the fact that other people are doing something along these lines. Can you give us some like real world examples of banks? If you can mention their names, that’d be great, If you can’t just, you know, what kind of bank it is, but some real world examples of, you know, AI and co pilot, and teams in action, I guess.
TP: Sure. So so first, I would say you know, most banks around the world or certainly many banks around the world are already starting to use co pilot of Microsoft Teams as part of their M thru 65 suite And typically they use it for, you know, internal use cases, especially things like knowledge mining and understanding data, trying to find information Where we’re starting to see and this is early days, but you’ll see some announcements over the next few months where we have tier one banks in the world that are actually building the applications that I spoke about. Now, what’s interesting there is they’re not necessarily starting with the generative AI co pilot piece, rather they’re starting with understanding the users, the personas if you will on the other side. So for example, targeting CFO teams within small business or corporates, and saying what are their needs while they’re working within Microsoft Teams that they would need to think about. So account receivables, accounts, payables, potentially product development, all of those pieces And as a bank, what are the tools they need to provide? So that’s the first step?
BS: That you’re going to start seeing within teams?
TP: But then the natural progression of that of course is now layering on top of that a generative AI capability to be able to talk and understand data and figure out what to do with it to streamline those processes and have that little assistant completely along the way that’s totally embedded. So that’s what we’re starting to see. I can’t name names right now, but again, in a few months, you’ll start seeing some things hit the market And I think the world is going to see how fast it takes off.
DM: What about Zafin’s actual use of AI? I mean, I guess you know, every company is investigating stuff, but what are you actually doing with AI today? I mean, or, you know.
BS: Yeah. So like I said, there’s two dimensions if you will. One is the internal, the other is the actual customer facing aspects of it. I’ll start with the customer facing side. So there are three large I would say themes, right? The first one and everything we just talked about up to now is like the foundation upon which these streams reside on. And so imagine now you have all these compiled within and we’ve reached the user where they can and so on. The three things that we then want to unlock in the immediate sense is explainability. So in various ways. So whether it be, we deal with a lot of transactions or contractual aspects of helping to model those things… Help us explain these fees. So that’s one example case, right? But also maybe a relationship fee or maybe the rationale behind how these are interconnected. So, but ultimately explainability and it fits within our theme because it’s conversational at this point, They can ask it whenever they want, They can get that support, They can deep dive and go further. The other side, the other theme is What we also call internally as a platform is signals. But essentially as it implies the conceptual aspect of signaling or giving them an alert or when there is a relevant movement in the market or some change in something that they’re watching, we will send that in as a signal through all the things we talked about through copilot contextually, relevant, when appropriate, all those things And they can then further… action on it. So it’s not just something you dismiss. They can then say, so tell me more about what’s changing here in this cohort Because we’re seeing movement of members being added in this cohort versus another one. Or we’re seeing changes in your competitors pricing structure, or the fees that we’re monitoring for you Here’s a notification about that. But then it’s not just notification dismissed. You can say, well tell me more What happened, Why, What are my options at this point? What can I do based on whatever that notification is specifically about? And then they can then go off and actually action on behalf of the user And you’re doing this all as an example through teams potentially even on your mobile phone, right? When you get this notification popping in. And then the third one which I think has its own roadmap. But the third theme is support this way, you know, again, so once we meet the user where they are when they need something, we’re right there with them. So whether it be something about, you know, how do we use this product document from a documentation perspective, explain how to actually go off and change. Some items in the catalog or explain to me how this feature works and how I need to configure it And we can go off and access it and actually give them step by step instructions on how to actually go and do that. The other being if there’s any kind of service request as opposed to an email, you can actually now do it through here. When there are changes or updates, we can actually get back to them. And this is that asynchronous aspect where we’re not really like, you don’t have to ping it. You don’t have to come to it to look for status. And it’s not just a mail that gets sent over to you. It’s a, you know, you see… kind of the tip of the iceberg, but then there’s a true, you know, like a context payload embedded within that notification that has a lot more that you can dig into and start to get into. But if you don’t need to, that’s fine too. So it’s very flexible and dynamic And, that has its own roadmap which I won’t fully get into. But support is something we’re very excited about And just being that concierge right in front of the user, right? Where they are to help with at least a lot of the basics that you can imagine in terms of like a phase one. But by really kind of taking it a step forward as we think about this more from an agentic approach and also this concept of this being the platform or at least the gateway to the platform, that is the Zafin platform. So that’s kind of the teasers as well as some specific use cases that we’re going to start to talk more about in the coming little bit.
DM: Brilliant. I just want to kind of summarize those three points if that’s okay. So firstly, it was about explainability, making sure that the user understands what the AI has done. And the reasoning behind that Second was about signaling so that it’s a proactive kind of reach out to the user, whoever that might be. It might be the customer themselves. And the third thing was to make sure it was supportive and assistive as in but not just, you know, Taking over a specific role necessarily, but actually supporting one as well. Is that a good summary or?
BS: Yeah. So I would just expand like in terms of explainability, it’s not just the explainability of the AI and its rationale but it’s more of explainability of a fee or explainability of a transaction or like, why am I seeing this? So? Those kinds of themes? And then in terms of support, I would say, yeah, like right up to, you know, even a service ticket and the management of all of that. And then the consistent like following up on those things and then being able to respond back when there’s an update, Any relevant action needed. So just being really on top of it, This is your agent, your co pilot to Zafin essentially.
DM: And so, I mean, look, one of the things I know from having worked in a bank is that, you know, like a bank’s product manager that has to, you know, come up with something new. What we’ve seen in the last few decades is very little innovation, right? Because actually, there’s what people don’t understand sometimes. And I’ve been on the opposite end of this where you kind of think crikey, what does it take to take out, you know, a new product with a slightly better rate and lower charges, right? But actually what goes on behind the scenes is a lot of analysis to look at the client base to see who we’re targeting, right? And to reach those people that didn’t buy before, right? And so that data analysis and then it’s followed through with kind of sentiment of the customer, et cetera. You know, can you see a role for AI in all of that to kind of assist the product manager? Or, you know, does AI, is it just assistive? Or will it, actually, could you say, well, Can you design me a product that would reach more of our client base than we have already? Can it come up with the design itself and leverage the data?
TP: So, let me jump in. You know, I think the last part of your question is the, is really important whether it can leverage data or not. And that’s when we think about, you know, large language model gen of AI, what they really intend to do, They take these mass data sets and make them meaningful and make them meaningful in a very, you know, natural language way, right? So, but it is again about augmentation. So the idea of, we have really good AI models. Now we can augment those with generative AI on top of it to create, you know, mass data personalization, if you will right Down to customer of one, which is something I think as an industry, we’ve really struggled in certain areas to do for a long time. So if you think about product management across the different phases we have like, you know, the research phase, we want to think about new markets, new geographies, How would a product test in those geographies? Maybe create synthetic data, leveraging generative AI to be able to figure out what it might look like without having to look at regular… PII data, things like that. That would have concerns. So now we can start actually doing testing of if you will synthetic products and how they might react in a certain market, right? And then we can go a little bit deeper and say, okay, now, let’s actually take a look at our broad customer base, which ones have been successful, which ones have not been successful. And again, doing that through a generative AI, co pilot type of experience. Can data Really speed up that overall process? Potentially even do things that, you know, traditionally a product manager would not be able to do on their own given the timelines, It can shrink that into days, but it might have taken months And then again start thinking about what could that mean in real world examples across, you know, different types of customers. And then, you know, again, Brendan, also just thinking about, I love what you said about support. So the idea of taking, you know, through the entire product lifecycle of understanding data, What’s happening with the documents that would be required? What kind of their past history would go towards that? What kind of pricing would we put in front of them as a result of all of those things, All of that can now be done by really just asking questions of the bot that points at all these multiple data sources to be able to provide an output and understand that? And then you have this idea of, okay, what happens if they call in? Well, if the co pilot has all that information and we’re starting to see this already, there’s two things that happen. Well. First is the change of self service, right? So in past, we used to say, hey, look, can I take that transaction and make it self service? Can I actually do that? Like Technically, can I do that? Can it speak to my systems and actually do things like take actions? Well, the cool thing with Genevieve AI is we don’t have to say, can I anymore? We actually now can say, should I, This is a paradigm shift. Can I, to, should I, In other words, let’s look at that customer as they’re coming in? We know all this stuff about the data about that customer. Now we’re saying, yeah, we know, we can totally serve them in self service, right? Maybe offer them a new product or different pricing on a certain product, but it’s a specific type of client we think would be best handled by a live person. So, should I, The answer is, yeah, let’s pull them out, Let’s let them speak to an agent and service them that way or sell to them in that way because that makes more sense for that moment that matters with that customer. And then of course, the agent themselves needs to have help. So this is where again huge pace for Genevieve AI, is this agent assist? We call it within the contact center or within other areas as well. Where the Genevieve AI can help with a few different things. Maybe this isn’t a real time call. Maybe it’s a proactive call. And the Genevieve AI is going to help with what we call meeting preparation. And so they’re going to understand all of these things that are happening with the client with their, if it’s a business client, for example, with their business, with their markets, feed that to the analyst. Maybe it’s a wealth advisor or so forth to be able to get ready for the meeting. Then during the meeting, just like a contact center agent, whisper sort of next best action or next best offers if you will during the conversation and take care of all the notes during the conversation. This is game changing.
DM: Yeah, I mean, I’d say that… Right? Yeah, yeah, I mean, I can see that And, you know, it’s not just, you know, the cross sell part of it which I guess, you know, would excite most of the banks. But actually, you know, I see it from a compliance perspective and say, oh, by the way, did you mention the T’s and C’s Did you mention, you know, what decisions that the client has to make, not the bank, et cetera. You know, I think that’s a big area.
TP: Not just about T’s and C’s and did they say the right thing, but more and more regulatory requirements are becoming more difficult and difficult to interpret against the bank’s risk posture and policies. So now we can do a summarization of regulatory policies, not even a summarization of you Generally. I can do a comparison against the bank’s policies and provide a delta That then can apply to T’s and C’s So it goes way beyond just listening to the conversation and saying, did they say the right thing? It’s now saying, did they say the right thing in regards to specific regional jurisdictional policies, regulatory requirements that have been created or changed recently? It’s a whole new paradigm in terms of the compliance side, And then we can get into things like financial crime and, you know, fraud AML, and so forth as well, Huge, huge impacts in those areas.
DM: Fantastic. I mean, look, we have run out of time guys, but I have learned so much about what not only co pilots can do but also, I guess the biggest insight for me from both of you was how it can actually really support human beings in their jobs and, And also bringing other people right? And whether that’s the customer or other specialist roles, but in a collaborative way using a platform like Teams. So lots of great insights there. Thank you so much for your time. And yeah, maybe we’ll have to do another show because this was way too interesting for me But thank you guys.
BS: It was a pleasure, Super fun. Thanks so much. Thanks Dharmesh. Thank you Tyler. Thank you, Dharmesh.
