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There is no technology that has generated more buzz this year than generative AI. We have all played around with ChatGPT and marveled at its capabilities. But what is really happening with this technology in financial services? Or more importantly, what is going to happen?

My next guest on the Fintech One-on-One podcast is Anu Sachdeva, Global Business and Service Line Leader at Genpact. She has deep knowledge of banking and fintech and has become an expert on generative AI and how it will impact financial services. This is our first deep dive into generative AI on the podcast, but it will certainly not be the last.

In this podcast you will learn:

• How Genpact works with banks and fintechs.
• How Anu first became interested in generative AI.
• What Genpact is doing with generative AI internally.
• How financial services can help unlock the potential of generative AI.
• The use case of using generative AI in anti-money laundering.
• The fundamental principles that Genpact employs when engaging with banking partners.
• How we should be thinking about generative AI.
• The impact of generative AI on the employee makeup of banks and fintechs.
• Will fintechs or banks take the lead with incorporating generative AI.
• How we should think about compliance risk when it comes to generative AI.
• The opportunity for better personalization.
• A look to the future of where generative AI is going.

Read a transcript of our conversation below.

Episode 455: Anu Sachdeva, SVP of Banking and Financial Services at Genpact

Peter Renton  00:01

Welcome to the Fintech One-on-One podcast. This is Peter Renton, Chairman and co-founder of Fintech Nexus. I’ve been doing this show since 2013, which makes this the longest running one on one interview show in all of fintech. Thank you for joining me on this journey. If you liked this podcast, you should check out our sister shows The Fintech Blueprint with Lex Sokolin and Fintech Coffee Break with Isabelle Castro, or listen to everything we produce by subscribing to the Fintech Nexus podcast channel.

Peter Renton  00:39

Before we get started, I want to remind you that Fintech Nexus is now a digital media company. We have sold our events business and are 100% focused on being the leading digital media company for fintech. What does this mean for you, you can now engage with one of the largest fintech communities, over 200,000 people through a variety of digital products, webinars, in-depth white papers, podcasts, email blasts, advertising, and much more, we can create a custom program designed just for you. If you want to reach a senior fintech audience, then please contact sales at fintech nexus.com today.

Peter Renton  01:21

Today on the show, I’m delighted to welcome Anu Sachdeva. She is the SVP of Banking and Financial Services at Genpact. And she’s also an expert in generative AI, which is what we’re gonna be focusing on today. So we obviously talk about how Genpact is working with banks and fintechs today, but the bulk of the discussion really revolves around generative AI, we talk about how Genpact is actually using generative AI internally in a way to train their employees. And she also talks about how banks and fintechs can really help unlock generative AI’s potential. Talk about the use cases that are happening today. Talk about possible future use cases. We also talk about the impact on the employee makeup of banks and fintechs. We talk about regulation and compliance. And what this is going to look like in the future. It was a fascinating discussion. Hope you enjoy the show.

Peter Renton  02:26

Welcome to the podcast, Anu.

Anu Sachdeva  02:27

Thank you, excited to be here.

Peter Renton  02:29

Excited to have you. So let’s get started by giving listeners a little bit of background about yourself. I’ve seen your LinkedIn profile. Two major enterprises you’ve been a part of in your career, why don’t you give us some of the highlights to date?

Anu Sachdeva  02:44

Sure, absolutely. So my role. I’m the global service line leader and solutions leader for Genpact. And what I do is I basically bring all our solutions, technology, data domain, together to bring the outcomes. Now Genpact has been, you know, a long standing partner for several banks and financial institutions, goes back a little bit into our history Peter, you know, we were born out of GE Capital. So the domain and really intense process expertise sits in our DNA. And we kind of brought in several different parts of data technology together. And now we are leading the path with experience, brilliant experience for our customers. GE was, again, a big powerhouse where I really learned a lot of transformation, and six sigma. So I was able to bring all that together in Genpact and to our clients.

Peter Renton  03:37

All right, all right, so then, maybe you could we could just dig into your role a little bit there. Like maybe you could explain the types of organizations you’re working with, and what you actually do day to day.

Anu Sachdeva  03:49

So in my role, I work with banks, I work with fintechs, and I work across all of capital market clients. And look, as Genpact being a professional services firm, a lot of our clients come to us for run/operate, because that has been a core part of our business. More and more Peter, what we are helping our clients do is design and architect. And what I mean by saying that is that we have been the powerhouse of their operating organizations. And now as the banks are transforming ,with cloud technology with data, and now with generative AI, it’s actually now helping them you know, change their operating models, or redesign some of the ways of working or help them identify what that, you know seamless customer journey is going to look like from the time of origination to termination. So, we really are embedded very deeply in helping really decide and define those outcomes so that we can take them on to that journey.

Peter Renton  04:54

Okay, so let’s dig into generative AI and maybe how and when did you become interested in this?

Anu Sachdeva  05:00

It’s very interesting look, generally AI, I think it caught steam really, I would say last year, I mean, we have always been dabbling in AI, you know, AI has been with most organizations, you know, as we were learning more on data. So we were learning more on creating models with AI. But I think really the big, the big pathbreaking event which happened was, like for anyone else with ChatGPT, when really generative AI was in our hands, you know, we were all trying, playing and experimenting with that. So no one could have imagined, Peter, I don’t know what you thought, but no one could have imagined, like, you know, what, 120 million users in two months that is really pathbreaking. And I think that has made it even more intriguing, not just for us as partners, but our clients and their clients as to how do you now take this technology, and really make lives better for customers in every industry? I mean, I am more working with the banking and financial services, and really there, it’s all about client experience, how do you completely change the experience? So that’s what I’m seeing. Very very fascinating experience, you know, just to see how this technology is now starting to take shape. And with so much of, you know, piloting, experimenting, which has already started off,

Peter Renton  06:22

Right, right? Well, we’re gonna get into financial services in just a bit. Before I do though, I was researching this topic for this podcast, I saw that you guys at Genpact are actually using generative AI today, you’re training your employees with generative AI. Maybe explain what you’re actually doing there, and how that impacts the attraction and retainment of talent.

Anu Sachdeva  06:47

So Peter, for us look, employees are our jewel, They are our core assets, you know, for the organization. And for years we have, we have been on the journey on really bringing our employees with us, you know, as we transform as an organization, and we see a very big correlation between learning and employee engagement. So more you involve employees in their own path, in their own journey of learning, the better engaged they will be in making sure that they are a part of transforming not just yourself as an organization, but your client organizations too. So we actually embarked on the journey of expertise for several years, you know, it kind of came in, you know, through the process of our heritage with GE. But more importantly, you know, after being process experts, that’s what/how we have always been, we invested a lot of our time and effort on being domain experts. So choosing specific areas of expertise, and really hone in on that expertise. That gave us a little bit of a path entry into what we need today, which is all about data. And I think to get ready for generative AI and to get ready to become generative AI experts for our clients, we started on really preparing our employees from becoming process experts to data experts, or data discoverers.

Anu Sachdeva  08:12

So that while they understand the process of how a banking, origination work, how do you unpeel that process and see and look very finely into what are some of the data elements behind that, you know, how are those insights from the data connect together, so that we could actually create and bring models and, and futuristic ideas to our clients, you know, cultivate innovation. So we actually launched what is called a DataBridge program. And that DataBridge program helped almost 80,000 or 100,000 plus employees to really get trained on becoming data experts. And that set path for us to become generative AI, you know, experts, which is, you know, of the 80,000, we are, we launched a program to actually make them experts in AI models, are experimenting with some of the AI tools and technology so that we can create some of those innovative assets for our clients. That has really been our path, you know, process to data and domain. And from there on getting onto our AI journey. And that helped launch so many initiatives for our clients as well. And we call it a collective intelligence platform genome. It’s a collective learning platform. So it has a very well defined learning journey for our employees. If you want to cross scale, cross train, upskill from one function to the other. There is a very clear curated journey so that our employees can choose the path that they can go they can go after, and we are sharing that with our clients as well through some of the solutions that we have brought using generative AI. One very interesting one I want to share with you is what we call as guru GPT. It’s actually documenting a lot of the operating procedures, and being able to retrieve that information to train, you know, on some of the transition, so our customers love it as well.

Peter Renton  10:16

Interesting. Okay, so let’s let’s talk about financial services. Those of us in financial services here, how can we help unlock the potential of generative AI?

Anu Sachdeva  10:27

So in financial services, I would say it’s very early days, and rightly so. Because look, banks are still very, very cautious about where and how they will apply generative AI through the value chain, a lot of experimentation is going on, I would say that a big amount of use cases that I’ve seen are mostly in areas where they can actually help their own employees get trained faster and better. Case in point examples are in contact center customer service, how do you bring information for your customer service agent, so that they are well prepared to answer a customer’s question or a query. So there is a full summary of, you know, information available, scraping of data, so that, you know, the chat, windows can be, can be actually pulled together and summarized. And next best action can be recommended. So some of that is already, you know, already in the path of going on, I would also see a lot of personalization of experience. That’s really the next stage of leveraging generative AI, beyond just internal use cases, more external, is around personalization of experience on launching newer products. So you try to look at, you know, various interaction points with a customer, use that data to be able to predict what is the next need of my customers, whether it’s new products, new commercial solutions. And that’s really another big area that we are seeing that financial services institutes are leveraging, or at least trying to experiment in generative AI. We at Genpact have actually been able to very successfully launch generative AI in a very important part of the entire process to stop money laundering. So that has been a big effort which Genpact has launched in using, leveraging generative AI in anti money laundering. And it’s very exciting journey that we are going in right now.

Peter Renton  12:44

Can we just dig into that a little bit? I’m curious like, because, obviously, you know, generative AI needs a massive data set,  large language models. And maybe could you just explain with anti money laundering obviously, there’s massive amounts of data on the bad guys and different transactions. And how’s it working? How are these large language models working in AML?

Anu Sachdeva  13:12

So let me actually back it up a little bit, kind of give you, and for our audience, a little bit of a background. So look, there are roughly about 1.5 trillion odd of illicit funds which transact across the globe, okay. And guess what, only 1.5% of that is ever caught. Or actually, you know, remediated. So this is a massive, massive issue for us in the industry. And banks are trying hard, you know, they are trying to, you know, get rid of the problem, but it is big, it is massive, and then they end up paying fines. I think in this last year alone, alone, $8 billion worth of fines were paid to the regulators because of lack of some of the remediation efforts. Now, what happens here is, transactions which go through the system need to be caught early on, so that the bad actors, the bad actors do not, you know, go into the system for long and are actually caught and, and you know, remediated right away. Now, this whole process typically takes roughly about 15 to 30 days. So it’s a very lengthy exercise because typically what happens is you have to pull or you know, get an alert from a transaction. That alert is looked at, it’s called suspicious activity monitoring or SAR. And in that you have to match the keywords in, and see if there is anything suspicious going on, and then see whether it’s a false positive or a false negative, and then, you know, remediate that by writing a narrative. So that’s where it takes time in really comparing the end to end process. Now guess what, in the meantime, that same fraudster probably had gone through five different institutes and probably you know, done money laundering there as well. So the need of the hour, or the biggest issue of pain point is, how do you get to these bad actors fast? How do you contain these, you know, false positives? And three, how do you make sure that these narratives and the search is done faster? So that’s what we have been able to do using our proprietary platform called riskCanvas. And we are probably the first in the world to actually work on AWS Bedrock in production, where using their large language models, we’ve been able to accelerate and in just a few seconds, and a few minutes, this whole search process of keywords, and writing those narratives, so that there is full traceability as we go to the regulators. So it’s been a phenomenal experience for us as we went through this. And we are very, very excited, because this really actually helps, in some ways, you know, solve a bigger issue in the industry, and very close to the purpose of our organization as well, which is making the world a better place for people.

Peter Renton  16:08

Right, right. Now, that’s really interesting that I could see how it just takes a lot of the work out and the time, more importantly, probably. One of the things that I’ve seen you talk about is some of the fundamental principles that Genpact employs, you know, when engaging with your banking partners, maybe you could share some of those with the audience.

Anu Sachdeva  16:29

Sure. And I’m actually going to narrate little bit of a story before I go there. And this is very, very important, because, you know, I hear a lot about, hey, generative AI can actually help us release productivity. And I just want to, I’m gonna poke a little bit on that one, because, you know, I think we need to look beyond productivity. That’s our experience. And I’m going to go back and think about, you know, when electricity was introduced in our lives, and electricity was was not meant to be, and is not a cheaper steam engine. You know, that’s not what it is, right? What it did was it actually it, it actually unlocked, you know, it actually, for us unlocked the whole source and use of energy. That’s really what it did. And with that, what came in was a completely redesigned factory model, completely new ways of using electricity for newer products. And I think that’s really how we need to think about generative AI. Don’t think of it as productivity only, think of how it can actually create and really make our products and solutions more innovative, generate larger outcomes, which we may not have even thought about.

Anu Sachdeva  17:44

And our lessons and learnings have been that look, generative AI cannot be and should not just be a point solution, it should actually be an end to end, should be orchestrated end to end within the value chain. That’s number one. Number two, I think it’s important to think about generative AI in combination with so many other initiatives, which the banks and financial services institutes have. Whether it’s cloud migration, or data orchestration, or, you know, robotic process automation, because it’s all really a very connected fiber. And I think if we connect the dots between everything, that’s really what will enhance, you know, innovation and create a much bigger, larger value. The third big learning for us has been in data, you know, and I smile and laugh, you know, because I almost always think of data, because when people say, Oh, I’m done with, you know, cleaning up data, but guess what, your data is almost like your teenager’s room, which can never stay clean forever, you have to go back and clean it every time. So believe it or not, you know, data can be a limiter, as well as an accelerator. So it all depends on how you treat it, how you make sure it’s available, how it’s governed. So data is going to be a very, very big enabler in the journey in generative AI. And so is process, you know, how well defined are the processes and the KPIs. So that what we are aiming for, is very clear, and that’s how we can link lot of the outcomes that I was, you know, sharing with you earlier. And I think lastly, I would probably say is chain management that cannot, I can’t, you know, stress enough on on how important it is because that’s really all about embracing the change, and really partnering in making that change sustain in those organizations as we all embark on this journey together.

Peter Renton  19:46

Interesting. Well, speaking of change inside organizations, what do you think about the impact that generative AI is going to have on the employee makeup of banks and fintechs?

Anu Sachdeva  19:59

There’s always that big scare, Peter and the reality of the hard data, and every conversation I go into, oh my god generative AI, it’s going to be, going to lose jobs, you know, people, we’re going to have 50%, you know, people being pushed out, whether it’s different functional areas. I think it’s to me, it almost feels like human nature. You know, we always try to overhype and overestimate sometimes, you know, back from the times of Socrates, when, when I guess, you know, the reading came in, it was thought that, hey, you know what, it will actually atrophy memory, or when the newspapers came in, oh, my god, we will stop meeting each other, and they will not be enough exchange of information or when TV came in, oh, we will stop doing intellectual activities. Well, none of that is going to happen. I mean, it’s more about yes, our roles are going to change, probably tasks will get eliminated. But I think we will have much newer and different ways of creative ways of solving problems. And that’s really where I think as an organization, we are firm believers of what we’ve got as human in the loop. And especially for banking and financial services, this is going to be even more critical, because humans are the one who will bring in that ethical judgment or emotional quotient or you know, that, you know, that that EQ and intelligence into the entire process and make this technology, more creative, more innovative, and more nuanced, you know, so that we can actually make, make the experience better for the end client that we all are serving for. So I am actually very, very excited about the journey that we are on and, and I think we will see much better lives for all of us, and much better product and solutions for all of us.

Peter Renton  21:55

Right, right. super interesting. So yeah, when it comes to new technology, oftentimes banks have a wait and see approach, they will, you know, be careful for good reason. And then fintechs have come in, particularly when we first sort of made the move to digital banking, fintechs took the lead there. So do you think that’s going to happen again, with generative AI? Are banks gonna sit back and watch? What are your thoughts?

Anu Sachdeva  22:23

Look, I think fintechs have always been digital natives. And I think they will probably be much faster in the journey. But I actually strongly believe that banks will not be left behind, we’re already seeing actually banks partnering with fintechs a lot more. And I think, the whole cloud journey, digitization was a big lesson. When fintechs were almost kind of threatening a little bit of existence, but that didn’t really pan out that way. And I think banks learned that look, better to be closer and faster in giving that experience to the customers. So I’m talking to several banks, large banks, midsize regional banks, they are all very, very eager to experiment, create some solutions, some very quick wins. I also know on the flip side, that they will always be in the in the guardrails of what regulators, you know, will allow, and rightly so, because I think this is still going to be a very nuanced industry, because it will always be here. How will it impact some of the models that we are building? Are we, are some biases creeping in? Are we making sure that it’s ethical, there is data protection? So I think banks will think lot more about that. And that’s, that’s probably what will take little time. And I think they will be far many more partnerships, wherever it’s linked to customer experience, I think we will see many more of those coming through.

Peter Renton  23:53

Right, right. So I just want to go dig into the regulatory piece there. Because that’s obviously something that banks and fintechs for that matter are very top of mind. And this is such a new area that’s really being, you know, being defined kind of in real time, and there’s no regulatory framework that exists yet for generative AI. So even banks are obviously going to be hesitant. But how should we think about regulatory and compliance risk when it comes to generative AI?

Anu Sachdeva  24:29

Yeah, I think Peter actually, I was talking to a bunch of Chief Data Officers and we were just discussing exactly that. And you know, one of them were just joking about it saying, you know, what, it took us almost nine months to explain to the regulators about what the impact of AI is gonna be. So good luck with generative AI because, you know, rightly so because this is such a new area and you feel, you know, so I understand that responsible AI is going to be big, and I think it’s going to be important for all of us as, as we collectively as partners work with the banks and fintechs, you know, as to really the whole transparency in those models, you know, what the models can do? And what they cannot do? Or, you know, what is the IP? Where is the IP sitting? Who controls the data? What are the security requirements, how are they being met? So there is a full, robust framework, what we call as responsible AI framework, which has to be put in place. And as in fact, we are very actively working with a lot of our clients in really, you know, implementing that. But I think that is really critical to ensure that, you know, there is a full robust governance, which is there, and full explainability and traceability for the regulatory environment as well and to the customers too.

Peter Renton  25:51

So I want to get back to sort of the the personalization thing that you talked about earlier, because this, this strikes me as, like a huge opportunity, shall we say, because we know, I’ve been banking with traditional bank for 30 plus years, and they know a lot about me, they have a huge amount of data. And they see, you know, they see kind of everything that goes in and out of my account, and, and yet, the personalization that is providing today is still, you know, it still wants to know, if I want to transfer a balance to a new credit card, I don’t carry a balance. And you know those are sort of things that I’m just wondering, are we going to see a leap frog in advancement there when it comes to personalization? Or how can you maybe color it a little bit better for us?

Anu Sachdeva  26:36

Is there an opportunity? Hell, yeah, absolutely. There’s a massive opportunity in personalization. Are we there yet? Probably not. And I think that’s really where the journey is right now. And I’m gonna backtrack, actually, all the way into data. You were sharing your example. You know, likely some of the banks have data sitting on five different systems, five different platforms. How do you create that data, lay that information, you know, repository, about a customer, and create those insights so that if a call comes in, it’s almost you know, it’s almost to a point of predicting what the call is about, right? So generative AI will enable all that for us. I think that’s really the big, fantastic news. When is it going to happen? That’s really when as banks go through the journey of really cleaning up their data, standardizing it, making it available so that some of those insight factors can be created, and personalization on to what kind of products can be offered to you, or to anyone who calls in. This is the predictive needs of those customers. So I think, I think the journey is very exciting period. But we’re not there yet. I think some of them are, I think there is different. Everyone is in different phase of their of their life cycle. But, but for sure, it’s really an exciting journey.

Peter Renton  27:58

So in closing, I want to I want to dig into a really big topic. And that is AI becoming so advanced, that, you know, it’s indistinguishable from a human, I was actually watching a video this morning was sent to me, of people that I know, one of them was speaking for real. It was him, it was a human, and the other person, it was an AI generated avatar of that person. I couldn’t tell. I couldn’t tell who, which they said which one is the AI generated one? I could not tell, and that was that was amazing to me. And so I guess the question is, do you see a time when AI is going to get so advanced that it simply replaces the need for humans in much of much of sort of the financial services arena? Or are the issues so complex and people’s needs so different that they’re always going to want to have a human element in this?

Anu Sachdeva  28:54

Great question, Peter. And actually, there are several theories out there. We will talk about the WINS framework, W-I-N-S, I don’t know if you’ve heard that, wherever there are words, images, numbers, and sounds, all that can be replaced by generative AI. Now, can that happen, you know, in certain industries? Maybe. Can it happen in banking and financial services? Maybe a part of that. But I think as I was talking earlier, look, it’s going to be far more nuanced than a simple, you know, hey, can generative AI replace humans? My personal belief is that it’s unlikely. Yes, there will be, we have to be very, very watchful, careful of the deep fakes and what we see as a wrong use of generative AI, which is also possible, and I think that’s really where the whole framework of responsible AI has to play in. Banks will always, you know, stay in the zone of caution, wherein, you know, the regulatory bodies, the oversight bodies will ensure that there is enough explainability on where generative AI is being used. And we firmly believe, as I said earlier, I think with human in the loop, some of these, not all, but some of these issues can definitely be taken care of, because that’s really where the ethical judgment and making sure that we are really leveraging the technology in the right way will come in. So that’s, that’s my personal belief.

Peter Renton  30:23

Interesting. Well, it’s it’s one of the most fascinating areas in all of an all of technology right now. And we’re not even at the top of the first inning. It’s really just started, and I’m excited to see how it unfolds, as I’m sure you are. So Anu, thank you so much for coming on the show today.

Anu Sachdeva  30:42

Thank you very much. Very excited. And thank you very much for having me here.

Peter Renton  30:47

Well, I hope you enjoyed the show. Thank you so much for listening. Please go ahead and give the show a review on the podcast platform of your choice and go tell your friends and colleagues about it. Anyway, on that note, I will sign off I very much appreciate you listening, and I’ll catch you next time. Bye.

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