Name: Future of Finance 2024: How AI is Shaping the World of Finance
Uploaded: 2024-05-16
Duration: 17 min 50 s
Description: Tracy Kerrins, Senior Executive Vice President, Head of Technology, Wells Fargo & Company, KC McClure, Chief Financial Officer, Accenture Moderator: Sheryl Estrada, Senior Writer, CFO Daily, Fortune; Co-chair, Fortune Future of Finance

Tracy Kerrins, Senior Executive Vice President, Head of Technology, Wells Fargo & Company, KC McClure, Chief Financial Officer, Accenture Moderator: Sheryl Estrada, Senior Writer, CFO Daily, Fortune; Co-chair, Fortune Future of Finance

Future of Finance 2024: How AI is Shaping the World of Finance

Conferences

Tracy Kerrins, Senior Executive Vice President, Head of Technology, Wells Fargo & Company, KC McClure, Chief Financial Officer, Accenture Moderator: Sheryl Estrada, Senior Writer, CFO Daily, Fortune

Good afternoon everyone. Thank you for joining us. Thank you so much. We have Finance and Technology United on stage, which is awesome. So I'm gonna kick things off with you, Casey. Um you lead accenture's Global Finance organization, which includes strategic planning, financial reporting. A ton of things, right? What are the best use cases that you have for A I in the finance function? That's a great question. And we have a number of different use cases for A I. And I think the key first thing that we do is we look at what is the right technology for the process that we're talking about. So in some cases, you know, I have a predictive uh analytics tool that does revenue forecasting. I've used that for years. We're going to enhance that with gen A I, but we, we use that today and that works. I have A I machine learning again, a good old fashioned A I like not necessarily gen A I that we use for Treasury that helps us with uh we call it the Treasury A I application or, or platform and that has helped us identify $600 million of idle cash so I can put the better, better, you know, better use. And then now we're looking at gen A I for different functions. I have a function where we've looked at this for controllership. Again, we're working with clients um and internal use cases because I want to be our own best credential. So I'm privileged that we offer this work to clients. So we do it for myself. So I have a unique position. And one of the areas we're working on, for example, is controllership. Everybody closes their books every month and every quarter doesn't sound so exciting. But we are able to free up tens of thousands of hours annually based on using jet A I. Now, that's great. And that's amazing. But the key part is that we also have to then to really get the most benefit of that cost savings and efficiency is we're also looking at the processes that we need to change and the organizational structures that we need to change. So, you know, again, it's a I it's technology around the continuum of what you need for each tool. That's how we're evaluating it. And it's not just technology, it's, you know, you have to bring the people along along with the process and potentially organizational changes. Now A I uses large amounts of data. How do you determine, you know, the data process in the finance? Yeah. So I think, you know, the first thing is, you know, all companies really need to be focused on their digital core. And so if I talk about us and then what we're seeing with our clients, right? So the first thing is really, what's your data readiness? Right. What is your data readiness? Um We actually have decent. No one's ever done. That's a continued, that's a continued um movement as we know. Um And then with our clients, they're at different places with their data, right? So some of them have a strong digital core and they're getting ready to move faster. Others now are just really reckoning with the fact that they need to really focus more on their data uh in the, in their readiness. So we work on that in terms of data readiness. Um And then we also look at um you know, what is the capital allocation that we have and where, where do we want to put capital? And I think that's really where we collaborate with our my technology partners, but for our clients and as well as for us, in terms of how it is that we're going to approach um you know, the different use cases that we have now, Tracy in terms of data that's in your wheelhouse. Uh What is the importance of having like the right data structures? I mean, you, you're going to invest in A I technology. But you know, if you have an an ancient, you know, system that is incompatible, how do you manage that within your team. Well, I mean, when we're talking about um artificial intelligence, like whether it's machine learning, kind of more um kind of A I small A I and then you move into gen A I data is the most important thing, but good data in equals good data out, bad data in equals bad data out, I think as you know, so it's really important that you're looking at your data. And so one of the things we've been doing, we've been doing A I for, you know, the last 10 plus years, we're a heavy A I shop generative A I is a newer piece, but we have really robust data and model risk governance. Um And it's taking the time to do all of that analysis upfront, make sure you're complying, you know, to compliance law rule, reg you have ethical, you know, checks um for us, it's like fair lending all of that. Um And making sure that the data, we understand where it's coming from, the lineage of it, the quality and that has to be proven before we'll even approve a use case to be deployed or used in production. And we like to go slow to go fast, right? So we do a lot of piloting, we do a lot of checks. So based on the risk profile of the model that we're using or the use case that we're doing, we have frequent, we go back and we look at the output. Because the other thing you have to be careful on with A I is model, it's drift, right? As you add additional information into the model, are the decisions coming out of that model changing in a way that you don't agree with or that weren't originally approved? And you have to course correct when it comes to generative A I specifically and financial, uh, services. Um, it's a highly regulated, yep field. So, uh are there extra measures that you take in regards to safety of information or is it something that you've already been doing? Well, we, we've layered it on top of what we're already doing with A I but we have set up um a generative A I Council that includes, you know, legal risk compliance. Um myself as the head of tech. Uh we're all in there and we review use cases and we review it for a lot of different ways. Um And because generative A I is still so new and we wanna make sure that we're making the right risk based decisions. Um We do a lot of piloting first. Um And then again, we go and we compare results that we thought we would get to the results that we're seeing. Um And we come back and we look at that a lot. Um So yeah, we have a lot of extra scrutiny that we've put on it. We also evaluate the use cases themselves. So not just the data. But what is the use case? What are you trying to drive? What models are you using? Right. So when you look at generative A I, it's all about data, large language models and then the computing power needed to run the large language models. Your riskier use case scenarios are when you need to start using some of the external large language models that you're not controlling all the data input into it. And so that's a big point of uh discussion and review in that generative A I group that we have that reviews everything. Thank you. Uh Casey in terms of risk, I know uh in the finance function with generative A I, there's been some kind of hesitancy in some, in some cases. So I was wondering, are people still kind of like feeling out their way? Finance leaders in, in, in terms of genitive A I or? Yeah, I think that, you know, that it's, you know, I talked to a lot of CFO S because I've talked to a lot of our client CFO S and it's, there's no question that I, you know, A I gen A I is viewed, you know, as being very transformative, you know, in technology. So I think everyone is embraced that and, you know, as I said earlier, really, then it's figuring where do, how do I get started and where do I go? And with that, you know, we have designed what we call an A I um, Navigator for enterprises. And what does that do? That helps you take a look at your different industries and now we're doing it by function. So like for the finance function, we have a, an A I Navigator. Therefore, the finance function that goes through all the different core processes that every finance organization would do and you customize it a little bit for industry. Right, too. But there's some core things that all CFO S deal with. And we really look at three things like at least to start what's the value case, what then based on that value case, what is the A I architecture? Right? And then what is the A I solution? And then as we were just talking about how things will get more complicated with large language models and having to switch back and go and decide which, which avenue do you take for each of these pilots or points of view, we then have what we call an A I switchboard that helps us determine that how would you switch over and make sure that you use the right solution for the problem that you have. And so I think that is a big part of how we're helping ourselves. I spend a lot of time on that. And um our view is that for me, I'm taking, I would not take risk in terms of how I run accenture, but we want to be the leading, you know, company finance be our own best credentials since we do this uh as a company. So I am pushing kind of boundaries with the teams that deliver the work to the clients to say, let's see what we can do together because we can all, you know, they have access to all of our data and you know, and so it's all we as we as me and uh so that's really very, I feel very privileged to be able to kind of move and go fast on, on gen A I use cases which we have dozens that we're working on right now in the finance function. Are there best practices in kind of measuring the ro I of A I um within? Yeah, I think, and again, I think it does. There are and I think there's, it really depends on the organization. I, I do think that the key thing that we look at is, you know, what is the no regrets move, right? What am I looking at right now? What are the things that we should do? Right. What are the things that we should do that will pay for themselves very quickly? But then I call it the second box that we look at, which is what are the more transformative things at scale, right? So they may not be identified right now as a quick win. But really what I'm looking for is at scale efficiencies, risk mitigation, right? Cost savings. And I think that's really where it becomes even more um interesting as we're all working through our different use cases. Thank you, Casey. Um In regard to strategy, it can I quickly it can change. Um So that means maybe data structures need to change. How do you scale for growth? Tracy um in this type of environment. Yeah, I I would say it's not, it can change, it will change, it changes almost weekly. I mean, we're seeing um it's just an ever evolving industry. So from a data perspective, what we're doing is we want to design everything fit for purpose, right? And that's based off the use cases that Casey and I have both, both talked about. So when you come a use case comes in, you evaluate it and you say here's what we think the need is and that could be uh the large language model that you're using. Does it require a human in the loop kind of review? Do we want to run processes in parallel before we go and deploy things? You know, we're making the right, the right risk based decision. Um We're also making the right cost based decision. So one of the things I mentioned before was compute. So when you talk about these large language models, we have to make a decision do is that something we wanna run internally or is that something that we wanna run on, you know, a public cloud leveraging one of our partners in our case. Um So that's really what we're watching every time you move data right to one of these models. And if you're doing it not in house and you're moving it to, you know, a public cloud provider, there is a cost associated with that. Um Sometimes that's needed. Uh But it definitely always has to be a point of consideration is how I would look at it. Yeah, and, and maybe add on one other thing I think if we talk about value is, you know, you really need to look at responsible and ethical A I because that really is a value element too of it. And so as everyone is embracing A I, what they're also realizing is the additional responsibility that comes from the potential ethical implications of the, the way they choose to do their A I and really to unleash your value in A I, you really need in, in full innovation, you have to have guard rails around that, right? That also, you know, that, that serve to make sure that you have the trust of your consumers, your employees and your other stakeholders, right? And so it, you know, at accenture, we really start with responsible and ethical A I, we just named a chief um responsible A I Officer this week, this position and we, we are able to help clients because we've done it to ourselves. We're global, we have 740,000 plus people and it is an immense ongoing effort and task to stay along with that. And so, you know, we anchor to our accenture core values and our code of business ethics, right? And then have a compliance framework and it's, you know, and and also training that goes along with that. So each company has to do that though themselves to define what are, what is their ethical framework, what are their core values? Right? Because it could vary by industry, it's, you know, different by company. And they, I think everyone to really unlock the value and the innovation and to do it in a way that is taking care of, of people as well. You need to make sure that that's really at the heart of everything that you do. We talked, I, I just wanna, I wanna mention um because we've talked about a lot of use cases and you were talking about use cases even for finance, but I would be remiss if we didn't talk about A I and even generative A I and how it's transforming the technology industry itself and the technologists that develop the solutions, which is a big part of that. That's something I worry about and focus on every day. There's a lot of tools out there that help developers generate code better faster. Hopefully it reduces risk by proving the quality of the code. Uh making sure there's no gaps in, you know, uh test scripts, test cases, et cetera. Um we're also seeing, you know, the potential for value when you're developing um user stories or business requirements and the quality of those and how we can help improve the quality of those that we get from business partners. Um So there's, for me, it's an imperative is how I talk about it with the team. I say the best engineers in the world won't want to work here if we don't give them these tools because they're available in the industry and it makes them better and it allows them to do what they like to do well, which is develop high quality code and solutions. So we have to give them those solutions. So it's not just about technology delivering solutions for our customers and clients, which is of course, top of mind, but the way we do that better is looking at ourselves as a technology organization and how we use A I and gen A I to transform the way we do our own work. Speaking, that kind of leads to talent in my mind. Uh So what type of talent are you looking for your organization now that we have all of these advancements uh to keep up? Yeah. Well, we hire a lot of quants on the model side. Of course, uh that's not as new but I mean A I and gen A I, technology hasn't been out there that long. So you can go and hire, you know, talent, but they, what they have is expertise maybe in a certain technology or a tool which is great. But then when we put them into our environment, they still need to understand and how to, you know, deploy a solution or you know, deploy a solution for a use case in our environment that's aligned to the business and honestly at the scale and complexity that we operate in. So we found a lot of success just hiring great technology talent. We of course have a robust training program and we're bringing up our own talent, people who are interested in it, just good technologists, it doesn't matter what, you know, technology, you know, we can train you on this and you're kind of learning with us, right? And that's part of fun. It's actually it's a pretty big, we're a pretty big. I like to think we're a pretty big talent draw because we have so many different opportunities. We use a lot of different technology in the space and our scale and complexity is sexy to kind of top talent who like working in A I and gen A I and I think on the talent, if you look at just the finance function itself, we did a survey that said over 70% of CFOS who've worked on transformational efforts of which all of what we're talking about is clearly transformation. They have to collaborate across, you know, three plus parts of their business, right? So obviously, with technology is is obviously a no brainer. That's one of them. So you really need collaborative skills in finance and people who like to collaborate. And we've also shown that the CFO S and those and their entities who are able to collaborate actually perform, you know, I'll perform their peers in their industry. So like the way that, you know, I organize our team and I talk a lot about my CFO peers is I have a strategy and enablement function, which means I have a group of people who wake up every day and think about what we're talking about. And the lead of that has I call it a foot in finance and a foot in technology, right? And really just bridges that there's not a divide because maybe that was a divide a decade ago, but just is part of both worlds because this is so big that, you know, I you need someone who does wake up, you need people who in strong leadership with the ability to make decisions, collaborating across your enterprise to really drive this change. Because the pace of change as we say will never be as slow as it is today, right? Everything is just getting faster. And so to do that, you have to empower a team. And I I find it highly valuable to have a strategy and enablement function within my finance team that serves as key linchpin across the organization collaborating and it helps the team to say No to, you have to focus your efforts. Things are moving so fast and the technology is moving so fast. You can't have kind of that squirrel mentality where everybody runs and tries to work on all the cool things you really need to be focused. Pick what aligns to your strategic priorities and your strategy and do them well or try to. Right. Well, that's a great note to end on pick what aligns to your strategic priorities. Thank you both so much for joining us here today. Thank you. Thanks for having us.