Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[00:21]
Hi, everyone. Welcome to this edition of HFS Unfiltered. I am Elena Christopher, the Chief Research Officer and Head of Financial Services Research at HFS, and we have a fantastic topic for today. We’re going to be talking about hype-busting GenAI — generative AI, AKA ChatGPT — in financial services. I think we can all agree that GenAI is on a gigantic rise; it’s captivated consumers and businesses alike with its seeming quick-win benefits and maybe its still untapped potential. So we’re going to talk today about how GenAI can be safely and effectively leveraged in the financial services context, for value and maybe even differentiation. I am joined today by a couple of fantastic folks. First, I’ve got Gil Perez, who is the Chief Innovation Officer at Deutsche Bank. Hello, Gil.
Gil Perez — Chief Innovation Officer, Deutsche Bank[01:11]
Hi there.
Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[01:23]
We also have Sean O’Donnell, who is the CTO for Financial Services International at Publicis Sapient. Sean also has responsibilities for a range of emerging technologies such as cloud and AI. Welcome, Sean.
Sean O’Donnell — CTO, Financial Services International, Publicis Sapient[01:23]
Great to be here. Thanks, Elena.
Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[01:23]
Before we go deep on GenAI, I’d like to level set a little bit with Deutsche Bank’s innovation history — partially because you guys have been, and I love when firms do this, very public about some of the things that you’re trying to do in the way of modernization. For example, you’ve been open about some of your investments in innovation and cloud, in an effort to impact your cost-to-income ratio. Could you maybe — and I think this will set us up for that NBT you’ve been working on with generative AI — help us understand a little bit about where Deutsche Bank has been with some of your innovation, to set the stage for where you are and where you’re going at this point?
Gil Perez — Chief Innovation Officer, Deutsche Bank[02:11]
When I joined — I joined Deutsche Bank in 2019 — at the time we did not use the public cloud. We had what we called private cloud, which in essence was running our own data centers but outsourcing them. And what has happened is, over the years before that, up to that point, a lot of the technology and the innovation of firms around the world, of the industry, had gone into the cloud, while not as much had been done on what we call the on-premise world. And also talent: if you look at schools and all of the new technologies and capabilities, everything is now shifting more to the cloud. So in essence, the first thing that we had to do was to create a platform, a basis for innovation — and really the cloud is that. And if you think of even generative AI, if we didn’t have the compute power of the cloud, if we weren’t able to tap in and out consumption into compute, it would be almost impossible to leverage and to use AI and generative AI in the way that we’re doing right now. So really, we had a couple of years of actually deciding that we’re going on this cloud journey, creating the right frameworks and controls to use it responsibly and prudently, within the constraints and the guidelines of regulatory — and you can imagine, as I mentioned before, we’re working with 46 different countries, 46 different regulators. There’s a lot of stuff that needs to be done, explanation and approvals that need to be done, before we could have had our first production workload on the cloud, and that basically took about a year. So throughout 2020 — sorry, 2021 — we really laid those foundations and then we started moving the workloads onto it. And in parallel, we started working on generative AI as the next thing.
Sean O’Donnell — CTO, Financial Services International, Publicis Sapient[04:35]
I think, Elena, the other thing — you touched on it — but what Deutsche Bank has been very good at over the last three-plus years, and Gil, hats off, you’ve kind of driven this, is you’ve been quite open about what you’ve been doing, right? In a traditional industry — I won’t show my years, but I’ve been a long time in financial services — there has been a hesitancy to publicize the work that’s going on, even down to partnerships and that kind of stuff. And I don’t know, Gil, but I definitely think we’ve seen that, both from the outside in but also in terms of just working alongside you guys, which you don’t normally see with other firms.
Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[05:34]
I would thank you for mentioning that, Sean. And that’s why, I mean, my intention in wanting to bring up some of the innovation backstory is because it was very certain that it starts to point to the underpinnings of why you’re able to do anything with AI, let alone generative AI. So thank you for pulling through that thread. But I feel like financial services is an industry I’ve worked in for a while — I too don’t need to enumerate my years — but it’s perhaps one that doesn’t work and play well with peers as maybe it could. Your openness lends itself to all of the elements you mentioned, Sean, but it also really starts to help develop ecosystem and allows you to better collaborate with unrelated entities to drive new forms of value, or at least that’s the potential. But thank you for that detail. Sean, I’d love to hear a little bit more from you as we start to back into the topic of generative AI. In your work certainly with Deutsche Bank — Publicis Sapient and Deutsche Bank have a relationship today — but you also have the benefit of looking across the market. I’d love for you to maybe give us some comments on what you’re seeing demand-wise in terms of generative AI, and really, what is your firm doing to ensure that you can bring skills to the table but also help your clients really unlock the opportunity?
Sean O’Donnell — CTO, Financial Services International, Publicis Sapient[07:00]
It’s a very rapidly evolving space. I think what we’ve seen — and we’ve obviously seen this, Gil, together, both in terms of Deutsche Bank but also looking outside — is that it’s gone through kind of mini waves, even in terms of GenAI. The initial wave was a little bit of discovery in terms of, OK, really, what can this technology do and how can it be applied? And I think that’s kind of a general pattern that we’ve seen with other clients — through to both getting the business on board but also unpacking use cases, right? Because people have seen the power of what they can do, and that’s where great things like the access to technology like ChatGPT has really opened that up to an audience that we couldn’t have dreamed of beforehand. But now I think we’ve gone past that; now it’s a case of productionizing it. So for us, what we’re seeing broadly across the board is, yeah, we get all the use cases — we have many organizations helping us and we kind of know that ourselves. What can you do in terms of actually putting the right guardrails in place so that, from a CRO perspective or a legal perspective or an audit or compliance, and frankly even from a regulator, we can evidence that we’re pushing the envelope in terms of technology but we’re also playing safe? Because at the end of the day, lots of use cases can be used in the back office, and they can be used as replacements for RPA, streamlining the flow of work, but ultimately the real power comes if you can put it in a colleague’s or customer’s hands. And that’s really then where you rub up against the ethics questions — is this safe, are people doing what they should be doing with my data?
Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[08:52]
What I’d love to do briefly, Gil, is — because we’ve been talking broadly about GenAI — I’d love to, if you’re game for it, talk a bit more specifically about how Deutsche Bank is leveraging GenAI. Because one thing that was mentioned both in Sean’s comments and in yours, Gil: there’s a lot of use cases out there. And with any new exciting tool, you run the risk of that hammer-looking-for-a-nail type of scenario. Oh, I have this cool new thing — what the heck can I do with it? Now, mind you, you have to go through that cycle to figure out what it is, what it’s good for, what the risks are, what guardrails you need. But you’ve been at this for coming up on a couple of years now. What’s really passing muster? When you go from a use case and a good idea to what’s really yielding value, do you have anything you can share with us?
Gil Perez — Chief Innovation Officer, Deutsche Bank[09:34]
Yeah, sure. I think I mentioned it, but I’ll give three examples. The first one is just software development. Software development is going to be transformed, and it’s not so much the generative aspect of creating new code. It’s actually documentation, and it’s also understanding old code. We still have — and we’re not the only ones that have — a lot of legacy code; for example, COBOL, a language that was used in the past. You have new people coming in, having no idea how to fix or deal with that code, or maybe replicate it. A generative AI tool can easily go through the code and actually explain what it’s doing, and even suggest a way to migrate it or rewrite it in a different language. So there is generative AI capability, but there’s also a very close human in the loop in that entire process. Documentation is not something people like to do; it’s required, and with a single click you can get documentation too. It’s important for regulatory reasons, and it definitely will improve the efficiency of our developers. So all of that space is really, really exciting. The second thing I would call the chatbots, the various chatbots. That could be from interactions like the one we’re seeing right now — this entire conversation could be transcribed, summarized with action items. So there’s going to be a lot of interesting capabilities around chatbots, around transcription, translation into multiple languages — but not only multiple languages, also including the different nuances of every business and their specific additional terms and elements that they use in their conversations. So being able to take a body of documents and inquire of it, to summarize it quickly — again, with a human in the loop — is going to be extremely important, and we’re seeing a huge amount of that, which falls into the banking area and what we call the research area. But the research area is not just researching a market or a specific stock; it’s even a salesperson, or anybody researching the company, using the same kind of tools. So I think large language models and the body of work and the chatbot will initially be used as kind of an advisor, somebody you could use to refine and accelerate your output. And over time we’ll get more and more comfortable with that. And last but not least, we’re also seeing the use of large language models with anti-money laundering, trying to figure out how we use all of the data that we have in order to improve the resiliency and the compliance of the bank. I just think that requires the regulatory on board, and that will take a bit more time. But those would be my three topics again: software development, the chatbot interactions, and obviously regulatory.
Sean O’Donnell — CTO, Financial Services International, Publicis Sapient[14:04]
And then the other piece of it — and this again is more on the revenue-generating areas or streams, rather than a pure cost play — is what can be done in terms of intelligence around pricing and product and service offering, and frankly how you can use that technology to be even more insightful and more personalized around dynamic generation. We’re used to the physical world where the lock-in in terms of the price at the point in time is what it is, or the product at the point in time is what it is, and frankly financial services has been as guilty as any industry of not being as responsive as it should be. I think we’re going to see a massive change in that. Probably in the next 12 to 24 months you’re going to see smart products, smart pricing, intelligent products, more reactive products that take into account where you are in a journey — frankly, where you are from a life-moment perspective — to be more responsive in providing the right product, right price, at the right time. Which again, Gil, we’ve talked about this in the past, but it’s really exciting, because that’s really where you get to the point of saying, well, why can’t I do this, why can’t I offer that product, why can’t you price it? In the past you couldn’t do it because either the cost of change was too high or the understanding of what to do to make that happen just wasn’t there. It goes back to my point on legacy — it was just too interconnected, too much spaghetti. I think GenAI is really simplifying that problem; it’s got the data, it’s got the understanding. So from our perspective, yeah, we see that as a huge area. It starts to force the hand of many enterprises.
Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[15:54]
So a lot of good themes there. All right, gentlemen, I love this conversation, but we’ve got to take it home. I want to end on an actionable recommendation note. I’d love for each of you to share your top recommendation for other financial services leaders — or, as we’ve talked about, even outside of financial services across industries — your best recommendation for leaders embarking on a generative AI journey. What do you think they can do to best harness the value and not the hype associated with GenAI? Sean, do you want to kick us off?
Sean O’Donnell — CTO, Financial Services International, Publicis Sapient[16:19]
Yeah, I mean — look, it’s a bit of an overused adage, but it is a journey, right? And I think there are foundational building blocks you need to get right, and they cut across things that are outside of technology. Absolutely that’s important — finding the right partners, finding the right kind of infrastructure, things like what Deutsche Bank had done with Gil driving in terms of cloud, etc., a bunch of those. But also making sure you’re connected to the business, that somewhere you’re connected in terms of where this is going to impact the customer at the end of the day. That has to be front and center in the approach you’re taking, because otherwise it becomes tech for tech’s sake. So I definitely encourage taking the longer-term view, and try to get the basics in place.
Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[17:27]
Got it. Thank you, Sean. Gil, what would you like to add? What’s your recommendation?
Gil Perez — Chief Innovation Officer, Deutsche Bank[17:27]
I would add just one thing, again very focused on generative AI. I think that people undervalue and underestimate the interaction and the knowledge of their own company and employees. So if you look at what Google has done in the past — its engine around the Internet — they understood that the interactions, the questions, the keywords, the search questions, were key to developing the underlying capabilities. And the same thing is happening around large language models. You have a great engine there, but actually the questions — what are usually called the prompts — are key, and they’re very unique to every company. And what I would recommend is for people to understand that that’s their intellectual property. That’s the unique sauce of their company, and they should, from day one, consider that and think about how they capture it, analyze it, harvest it, and continuously improve it. And so it’s a big mindset shift for corporations, because they need to think differently. It’s actually all of those interactions and that body of work which in essence will be their key to their success in the future.
Elena Christopher — Chief Research Officer and Head of Financial Services Research, HFS Research[19:15]
Got it. Thank you for that, Gil. So we’ve got foundations from Sean, and we’ve got value — the insights and the unique perspective, what’s unique about your enterprise — as two key recommendations for long-term success with generative AI. I’d love to thank my fantastic panelists, Gil and Sean. Thank you so much for sharing your insights and experience with us today. And that concludes this edition of HFS Unfiltered.
