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Navigating the Data Race in Financial Services · Leaders in Lending

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You are listening to Leaders in Lending from Upstart, a podcast dedicated to helping
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consumer lenders grow their programs and improve their product offerings. Each week, here
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decision makers in the finance industry offer insights into the future of the lending industry,
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best practices around digital transformation, and more. Let's get into the show.
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Welcome to another episode of Leaders and Lending. I'm at snow here with
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Greg and Amyor from Salesforce live at CBA. Greg Amyer, Welcome. Why
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don't you take a minute to introduce yourselves talk about what you do as Salesforce.
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Thanks Matt, happy to be here. My name is Greg Blasei.
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I'm a senior director in our financial services cloud product organization. In that capacity,
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I work directly with customers and with industry leaders to help make sure that
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our industry verticalized products and point of view and solutions are headed in the right
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direction and generally trying to stay to where the puck is going in the industry.
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Awesome a mere magell Essie, industry advisor at Salesforce. Been here for
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about three years, but it's spent almost twenty years in financial services and have
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seen the movie before on how you know, clients are contending with big business
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problems. In my role, I cover the US in Canada and we really
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try to unearth what the biggest and brightest problems are for our clients, bring
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practical solutions to them, and figure out a way that we can leverage on
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the platform in an extensible way. So looking forward to that conversation great,
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and it's a pleasure being joined with two reformed bankers. You know, we
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talked with some of the guests about like accidental careers in banking, but I'd
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love to hear from each to view like how you got in and out of
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banking to now more on the service side. Yeah. So I spent the
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first twenty years of my career working directly in the industry and may have crossed
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paths with a couple of folks sitting at this table at various points in that
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career. And you know, I was very fortunate to have a few different
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roles in banking. I started out my career in retail banking as a teller,
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like a lot of us have, actually just kind of worked my way
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around the bank. I worked on corporate banking team and in the commercial space,
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specifically spending a lot of time in treasury management and had the opportunity to
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do a lot of roles. In my last five years I was at the
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bank, I actually was the business owner of our salesforce deployment across the enterprise,
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which is where I got my experience with the technology and really really liked
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it and thought I'm gonna make a leap over there. So about seven years
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ago I did just that. Nice. Yeah, not surprised. So Greg
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and I did work together at Huntington Bank and Columbus and on the retail side,
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and I just remember your passion around like the use of technology to enable
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the consumer and banker relationships, So I'm not surprised to see you on this
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side, like helping many other financial institutions on that journey. Yeah for sure,
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Yeah, no doubt. So I started in banking in the retail franchise,
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managing branches, regions, larger territories, zig zagging around the organization quite
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a bit, went to strategy, went to business banking, managed relationship managers,
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and then having the opportunity to actually go on and run the consumer strategy
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in another large regional and had the you know, staffing model, incentive plans,
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data analytics the operating budget for the firm, and had the pleasure of
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implementing the platform as well at both institutions, in addition to other platforms as
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well. So it's now I saw the inflection point of you know, technology
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and the need for technology well before the pandemic arrived. The pandemic obviously accelerated
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some of the need and the passion for that, and so I thought which
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organization would take a clunky banker like me and leverage them in their ecosystem.
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But Salesforce was such a great fit culturally and for us to find ways to
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really walk in the shoes of our customers, and so that transition was really
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related to how do we actually more deeply understand our customer needs and how do
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we apply technology to solve those problems. So it was a great match.
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Awesome, great, Well, I know technology and AI and we'll get to
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those things. Get a lot of the press. But one of the things
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that is spoken me at this conference that you guys have hit on, it's
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just the data and the foundational layer of data and getting that right, getting
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an organized or how do you make best use of it? Maybe it Leo's
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start with that, like what are you seeing in terms of the customers you're
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helping, like what's their state of their data? What are the biggest needs
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or what advice would you have for banks like struggling with what do I do?
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Like how do I get my data AI? Ready? Yeah, And
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maybe I'll start where I'm going to end my response, and that is that,
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you know, AI is the hot topic of the moment, and it
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should come as no surprise that good data is good AI and bad data is
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quite the opposite. And so it has, you know, certainly accelerated some
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conversations and strategies at some financial institutions. Although I would say that folks have
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been on a data journey for a while. I would sometimes say that almost
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the strive for perfection was almost to a fault in some cases where it was
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like, oh, I think we have to have this perfect and good wasn't
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good enough. And that's unfortunate because I think that those same folks are the
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ones that are finding themselves trying to play catch up to those that you know,
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we're more forward forward looking. So I'd say just in general, the
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industry has been on this data cleanse, refresh reorganization and truly understanding the value
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of the data that they hold. Now in this world that we're in,
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it's not only the data, but what data? What data has a shelf
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life of usefulness? And how do I consume it? And how do I
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consume it in massive amounts? How do I harmonize and unify that data so
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that I've got one understanding of who Matt is as my customer. And so
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that's really kind of I think where folks are landing right now or what they're
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trying to figure out because they know that in order to deliver the promise of
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AI, or deliver on that promise that we are all hoping it brings to
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the industry that is really critical and foundational. Yeah. I guess that's a
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good nuance. So you're saying that you don't have to have your data like
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cleanse and strategy strategy set first before you leap into AI. You can do
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some of these in parallel. Yeah, I mean, I think, and
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that's exactly what we're seeing as some folks are kind of flying the plane in
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the air right as they're built. Or maybe I got that backwards, but
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you know what I'm trying to say, right, build the plane while they're
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flying and that's it. But but it is, I mean, it is
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something that goes and it's also you know, you get your data to a
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certain state, and I guarantee you that's not going to be the final state.
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So it's something that you're always kind of working with and massaging. We
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talked in the panel discussion yesterday, you know about even just idea of test
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data and making sure that it is whole and representative of the you know,
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base that you want to serve and that you've got the right amount of data.
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And then it's fresh data, right because these models are learning off of
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that data, and so it's just really important to even give consideration and out
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that kind of element. I'd love to build on what Greg is talking about
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though, mat because I think I feel like it's a race without a finish
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line because the amount of data that's accumulating on a daily basis and listen and
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financial services, it's a knowledge based industry, and so it's like, how
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do I now interpret that information as a business line lead and a meatail owner.
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Formerly, you know, I got a lot of information a lot of
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times, but I'm not sure I got a lot of insight around that data,
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So it's not only taking what Greg said, but putting it into an
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actionable format. So you've got data, you've got a UI that you've got
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to worry about so you can deliver it to people that are interacting with customers.
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But you have models behind that that. Now, whether those are lms
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that are externally purchased and integrated with or you're building it on your own.
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So there's an opportunity for us to now build those things together in a cohesive
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way so that you can actually serve customers in an intelligent and intelligent man Yeah,
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so what do you think is the fastest path to those insights our customers
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coming to you with, like the insights they're striving for, looking to connect
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that to the data or vice versa. I think it's challenging because it's broad
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and it's vast kind of set of options architecturally or the UI that I was
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referring to, or even the models that you're going to engage in, and
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then how do I account for the regulatory landscape that Quite frankly, I'm not
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sure that the cement is quite dry on how you know, our regulating bodies
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feel about the usage of certain data. So I do think it's a matter
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of taking that a mass amount of information and thinking about the use cases that
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actually impact people's business imperatives, and then cobbling together the data set, the
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UI and the model that makes the most sense for the use case, because
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otherwise it's such a large endeavor for organizations, they can be crushed by the
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magnitude of the effort and that can be paralyzing for a lot of organizations.
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So we really try to figure out what's the broad vision, but then let's
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get down to the use case levels, so that solving real business problem.
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Yeah, that's a good point. We found that the status quo is often
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the biggest competitor to doing some of these innovative changes. Yeah. Do you
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find then challenges around compliance privacy as the data it self is ever changing,
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like customers expectations, the regulators view like, how do you think about those?
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Well, I think it's again down to the use case level. So
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if there's something that you know, a regulatory clot starts, well, then
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the amount of information that you need and the speed in which and the access
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and who has that I think becomes paramount. Right. So yeah, I
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think we're seeing that in some of the early use cases that are emerging,
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it's typically going to be a little bit lower risk use case one where we
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definitely would still want to keep a human in the loops so that there is
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that at least guidance of you know, critical thinking human being engaged in the
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process. Gotcha, what other aspects of AI are you saying introduced into you
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know, the questions your customers are coming to you with, are maybe some
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of the innovations even within salesforce. Yeah, so a term if you're not
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familiar with this term, and watch they'll play this podcast six months or now
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and everybody will know it. But it's retrieval augmented generation and you will hear
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people talk about it in its acronym form RAG. And so heavily regulated industries
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like financial services are running to RAG. And what that really means is rather
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than going out and searching these vast ll ms that you know, may have
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training data that might be you know, months old, to rely on,
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what this is really doing is saying, number one, it's kind of reducing
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compute power that would be needed to generate some responses, because what it's doing
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is saying, Okay, here's my scenario here's my situation, here's my customer
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and what I know about them. Now only go search this universe. And
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it is really a way that I think regulated industries, you know, like
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financial services, are really going to be able to adopt generative AI more quickly.
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And you know, it's also in addition to that, there's like this
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whole auditibility component on it. So an example might be, you know,
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you call in, You've got a question about say a wire transfer or something,
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right, and and okay, great, I'm an agent. I just
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need to really quickly, I'll do a query on that and return it and
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say I'm giving math this response. And I'm giving math this response because my
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bank says that this is our policy as it relates to that question. And
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this is our policy because of this regulation. And so there's this auditibility factor
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that is really critical. And then that human in the loot component, right,
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so it's even bigger than that. It's like, okay, great,
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well I've got this response back. Well, what did the human do?
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Did they execute as I said or as the tool responded, or did we
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have to kind of nudget in a different direction because it wasn't quite right.
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Yeah, I'll admit that was a new term for me, So I did
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search that immediately after you said that in the session. Do you view that
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as like a completely separate class of like GPT's or is it a different flavor.
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I'd say it's kind of a subset of it, right, So,
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like, as we've been kind of thinking about generatively I just in general,
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not we as in sales sorce, but is generally as a as a population,
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you know, everybody is very focused on these lens and it's still it's
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still a part of it that you're saying, like, only search this part
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of it, right, because I know my answer is in there somewhere.
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Yeah, right, Yeah, Okay, that's helpful. What else in terms
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of you know, AI driven it seems like personalization was also a key topic
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for sure. Yeah, you know, And here's the thing, Matt,
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I've been thinking about And you know, I've been gone from the bank for
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seven years, and I remember seven years ago still talking about personalization, and
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so personalization is certainly interesting, and a part of personalization is gonna be a
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segmentation strategy and kind of getting down to this small group. But what I'm
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really excited about with generative AI is you know, A more current term around
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personalization is kind of category of one, and so I've been using that terminology
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because I think that really resonates with marketers because at the end of the day,
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we are all different, and we all have very different needs and finance,
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and although we may have a common set of tools in the financial services
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space, you know, to solve some of these challenges, the way I
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might consume something versus a mere versus you could be totally different based on on
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our scenario. And that's why I think this category one idea is really really
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important, and it kind of in my mind is like next level personalization stuff.
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I want to build on that just for a second, because I think
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historically I see personalization and conversations going on to generate demand and convert the top
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of the funnel. If you will have activity an opportunity. I also think
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there's a tremendous opportunity to think about the service excellence that you're trying to deliver
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for your clients. And so when I think of lending, if I'm a
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business banker or a commercial officer, I've got renewals, what's the level of
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intelligence that I have at my disposal and does the client have the same amount
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of information. If I'm sitting there in a mortgage situation, how am I
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actually able to personalize the experience in such a way that it's reflective of their
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financial position, the experience that they are looking for and expect, and of
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course what the capabilities we can deliver in that time. So I always feel
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like there's an opportunity to go much broader than just the top of the funnel.
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Yeah, totally agree, And to borrow your phrase, I think personalization
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is another race without a finished line or an end, right. It's a
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negotiation or a dance continually with the consumer, with the company in terms of
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what's okay for the company to know and use and leverage in terms of the
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data or information they may have, consumer expectation and what they're coming with all
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to help facilitate or think about a recent regulatory change that's happening DoD Frank Section
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ten seventy one. You're going to collect a lot more information, but you
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can't share that information with every single person in the ecosystem if you're going to
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do a lending opportunity right now, because underwriters cannot see the incremental information that's
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being collected on that data expectation. So I do think that personalizing it for
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dot Com slash AI Certification. Thanks, and now back to the show.
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Interesting. Are there other pieces of automation you see happening either on like the
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direct to consumer or with bank staff? Anything that helps well, you know,
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I think personally, you know, my personal experience has been the middle
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and back office that has this heavy intensive human infrastructure need is where there's ripe
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opportunity. I think there's plenty of studies that demonstrate there's trillions of dollars an
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annual economic value associated to actually improving the efficiency. Because it takes a lot
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of human intervention a lot of times to help deliver a product or service or
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a request from a client, why don't we augment with digital client excuse me,
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digital colleagues, if you will. That was sure to help either delegate
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to AI or maybe in the new wave as we kind of talked about an
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R deep dive, how about the client being able to delegate to AI and
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then actually having the human in the loop ensure that is appropriate for that customer.
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So there's a there's this kind of interesting way that's going on right now.
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Yeah, interesting, and I still have islands in the stream, like
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in the back of my head. So thank you for that. You're earlier.
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But if for those of you at the session, we'll get that joke.
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But the other thing I think about talking about data again is how do
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you know you know what data is relevant for what decisions? And you had
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some really interesting ideas around either like reaching data where it was or thinking about
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and using data, not maybe at a stored or artificial way, but you
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know going to those sources when you need it. Like, can you describe
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more of that? Yeah, I mean in my mind, it's kind of
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like there's you know, data that's trapped in you know, in enterprise application,
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like a core banking application, right, and we might want to access
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say transaction information or even transition details in a service context or something like that.
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Right. So that's kind of one element of data. Then there's you
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know, data that might be in or provided and integrated via a third party
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data aggregate. Right, who's you know we're able to consume data in that
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regard, And then there's kind of like all these other places that data might
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live. And one of the stats on that very slide that we had the
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Islands in the same joke about was you know that seventy one percent of companies
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say that I'm sorry, of the applications at an organization, on average,
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seventy one percent of them are disconnected. That was a proprietary research that our
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team at Mealsoft did with our customer base, which I think is just really
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astounding and speaks to kind of like the missed opportunities that we're talking about.
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We don't have a good data strategy. Now. One thing on that slide
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those we were talking about and if you can visualize our river with data islands
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sitting in it, there was a there were some examples of big islands in
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there and they had you know, logos of part nor is it ours on
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there, right, Amazon, Google Snowflakes as examples, And certainly that is
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part of a data strategy, right, Like I got to get my data
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in order, and they play a very important part in that. But the
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challenge is then you may have your data in order, and you may have
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at some place that's useful for data scientists and marketers potentially to go in in
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mind, but if you're really just thinking about serving it up in an actual
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way in the context of work or a banker's day, that's a miss,
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right because it's not like your bankers are going and logging into snowfake directly.
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Then if they did, they wouldn't know what to do when they were in
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there, right, They're bankers, not data scientists. And so I'm just
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this idea of freeing that data up and then in terms of like the right
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data at the right time. You know, that's I think a matter of
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well, what's the situation, right, is it a service situation, it
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is a is a sales opportunity? You know? Is it more just kind
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of routine financial wellness type of activity that we're doing and what might that do?
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And just kind of having the ability to put the controls in place or
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levers in place and die. Yeah, to be able to surface that right
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information to the banker or the client at the right time is incredible. I'll
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just add too that, you know, it feels like it's all types of
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forms of data too, right, So there's streaming data, whether that's mobile
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or onine engagement, or there's batch data because maybe sometimes the integration effort is
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a herculean if you will, in nature, and it doesn't feel like that's
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that's achievable by some clues. So again, finding a way to say,
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what do I need for what reason for which persona, and where is this
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going to come from and where can I use it intelligently? So it's not
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just the kind of traditional sources of data, but what about digital signals,
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the hand raises that are happening with your client all throughout their experience and ecosystem.
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Are you able to pick that up, interpret that and actually use it
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in an experience or interaction that's I think when magic and really gotcha. Do
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you envision a future where either the platforms are more natively integrated, or there's
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middleware layers that facilitate that discovery fact store or will it that always be a
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challenge of well, listen, I think I always describe it as simple but
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not easy, right, So we talk about it in such simple ways,
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but when you have been part of an organization that has thousand applications and potential
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various platform aggregating and wrangling that data can be extraordinarily challenging. But I'll be
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honest and forgive me for the shameless plug. But I think that's what Salesforce
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is focused on is our data cloud product that allows for the variety of different
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types of data sources, different data types, and even in file or assuming
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fileing, batch or streaming types of data to be able to aggregate that.
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The key though, is being able to unify so that we know when we
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talk to Matt, it is the same math that's using the mobile app,
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It's the same math that has an unsecured loan potentially and the same math that
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has a small business and as an entrepreneur, and how do we really understand
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the full relationship is that helps us with relationship pricing, disc management, and
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quite frankly, people are just used to that in their personal lives and every
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way it's connected, it's social and quite frankly, when they have their experience
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with many financial institutions, it's the end tothis of that. Yeah. True.
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And one of the things you mentioned, Greg, I think was around
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like metadata like that is important and making sure all that connects well and that
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that's like that's kind of the secret sauce right at the end of the day
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to where we're we're spending our time and really working. As you know,
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we're fortunate in that we're CRM right at our our at our roots, and
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that is where customer data is sitting. In this understanding and this it creates
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this opportunity to engage with customers. So not only do we have that customer
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data, but we have the metadata about it in our product set. It's
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those for all of those apps that we have build on that common metadata layer,
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so it's you know, kind of the data about the data, right
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and understanding what the data elements actually each represent, and then the ability to
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We've got a couple of different avenues to bring data in or to engage the
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data where it sits, so in a totally zero copy kind of way,
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because you know, some data security folks aren't going to be too excited about
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moving data from one place to another. So great, how can I look
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at it where it is, but visualize it in the context of my customer
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engagement and with that metadata layer on top of it. Right, So I've
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got this understanding, then make use of it in generative AI, and that's
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really where the magic comes to life, and that can unlock the scale.
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Probably like you're talking about getting down to an audience one, Like you could
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do that at an institution, but the cost continues to escalate as you break
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down the audience. So yeah, like, are there metrics because you did
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talk a lot about driving down costs of acquisition or servicing? Are there other
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metrics you help your customers like understand their programs or how they're making improvements.
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How do they know they're Yeah, it's still on this journey. Yeah,
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it's gonna it's sort of you know, depends on where they're starting. Right,
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If I'm a marketer, I'm really going to be concerned about the effectiveness
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of my campaigns, right, my offers that I'm putting out there, and
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my personalization efforts, Like am I really getting the return on that that I
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wanted to do? The pull through? You know, managing something like this
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on platform that gives you visibility not only to your marketing efforts, but your
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sales efforts and then even farther down the road loyalty, right, Like,
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you can have a much better and straight line view on your effectiveness actually of
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your of your your marketing. So like that's one example, you know.
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The other would be, you know, something as simple as well as simple,
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but something but your your c SAT scores, right, I mean,
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because I really do think this stuff and what I'm terribly excited about, Matt,
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and I think it'll be a minute before we get there. But a
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mirror was kind of alluding to it is when we when we are able to
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combine AI and automation all in one where things can kind of just happen.
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I mean, don't you sometimes have the expectations like, well, they know
23:56
that about me, why didn't they just do it? Right? Like Why
23:59
did we have to make a you know, conversation back and forth and fourteen
24:02
emails just to do something really really simple? Why didn't they just do it?
24:06
And I think we're getting We're really close. The technology exists today.
24:10
You know. The fact of the matter is we could go out there and
24:11
put something like that in place today. It's just there's a little trepidation.
24:14
Yet We've got to get past that as an industry and begin to really trust
24:18
the tool in order to I think, allow those experiences to get there.
24:22
But that's a great example of c set where it's just like, yeah,
24:23
my bang me, they really do take care of me. I do want
24:27
to build on that because I think one of the other things that I quite
24:30
frankly talk a lot about is because a lot of people coming in and go,
24:33
how do we drive privacy, It's like, well, that's a lagging
24:37
indicator of sorts. So like, how do you know that you're implementing technology
24:41
and facilitating conversation digitally orienting human or hybrid scenario, and what are the leading
24:47
indicators that your actual technology investments are transiting into real economic value. So it
24:52
might be a level of adoption readership, there might be the time to actually
24:56
serve up a solution. I think it marked. I would have very expensive
25:02
data scientists work on our behalf to interpret marketing campaign ROI and the responsiveness of
25:08
even certain control groups or otherwise. Deposit growth is a little bit of a
25:12
hot topic these days. Sure, what about the opportunity to expedite what is
25:17
normally a seven eight nine week turnaround on insights of a previous campaign. By
25:22
the way, I've already set the budget and the marketing campaign for the following
25:26
quarter, but I yet I have not had the insights already. So think
25:29
about the speed and agility and velocity that you can move around sub segmentation.
25:33
When you think about sales productivity, when you're in retail banking, it's about
25:38
leeds, It's about referrals. How can we reduce the time that it takes
25:41
to getting from you to a partner? How do we improve the KPIs around
25:45
the conversion of those referrals. These are still core competencies of a retail bank
25:51
today, and quite frankly, people still struggle with actioning because every day an
25:55
hour that goes by that's not actioned on that referral, a less likelihood of
26:00
them buying whatever cent product or service that's being teed up. So like that's
26:03
where I see a lot of the interaction of and that's why I think I
26:07
emphasize use case by use case. Yeah, because if we think about like
26:10
the organizational objectives, if we're being honest, every organization has very similar financial
26:17
ratios that they need to improve when that's efficiency or net income or cost of
26:21
deposits or funding. But like one of the real levers and drivers that we
26:25
can pull on to really go forward in an outperformance way, if you will.
26:29
Yeah, So you definitely hit a hot topic on deposits. Are you
26:32
guys seeing anything outside of price wars, customers who are succeeding in winning deposits,
26:40
bringing in new customers, deepening relationships. Where are the success is happening
26:42
there? Yeah? I think while smart intelligent elasticity pricing and MODELNK can help
26:49
you understand the right segment that's going to buy at the right ray and give
26:55
you the balance performance that you're looking for, because that's kind of it's not
26:57
deposit growth at all costs, right, right, I think it's like deposit
27:00
growth at a prudent ray and what the funding needs of the organization might look
27:07
like. And so I think it's yes, being smart about the intelligence and
27:10
the analytics around how we can interact with those customers. That's a big winning
27:14
recipe. But it's also simple things like what's the on boarding experience when somebody
27:18
actually raises their hand and says yes, Because there's a massive amount of an
27:22
abandonment rate when you're digitally engaging or even in a branch environment, if you
27:26
can't fully bring the account to fruition fund the account and getting used quickly,
27:33
that leads to privacy, by the way, at some point, right,
27:34
So I think there's some pretty big opportunities and we're seeing some clients succeed in
27:38
that way. Okay, yeah, probably one of the benefits of a company
27:42
like salesforces you have many other verticals to learn from. So are you seeing
27:45
with your peers and maybe less regulated spaces, things that you're learning from or
27:52
ideas that could potentially catch on in banking sometime or I this was a really
27:59
it's almost like you knew this because onboarding is a really it is a passion
28:03
of mine because I just think that experience has for far too long been rocky,
28:07
and you know, I don't know about you, Matt Billy, Like,
28:11
if we went out, you know, to a lot of banks and
28:14
we said, we want to open a checking account and we want to take
28:15
out a loan, and we want to do so simultaneously. I don't know
28:19
what the stat is, but I can guarantee it's more than not. We're
28:23
probably going to have to go down one path first with a series of questions
28:26
that's very product driven, and another path going down the loan that's very product
28:33
driven, and probably sixty percent of the questions are going to be the same.
28:37
Right. And so I say that because I look to retail, you
28:41
know, and think about just how easy some online retail experiences have gotten in
28:45
And so we're learning from our friends over in that particular industry and what those
28:52
experiences might look like. And I said, this notion of like shopping cart
28:56
is really starting to take hold in some conversations at banks. So how can
28:59
I make my experience more Amazon like? Frankly, right, and extreamly,
29:03
you already know a lot of the stuff about me in theory. If I'm
29:06
an existing customer, why is it in just as simple as adding these to
29:10
my cart and getting checked. Yeah, it makes sense. I would offer
29:14
a couple of very public logos that we've talked about one Disney, Formula one,
29:19
Gucci. They are all there's a commonality between all these stories around the
29:26
ingestion of behavioral activity and digital signals that their comments are exhibiting be able to
29:32
harness that and serve it up in such a personalized way that it blows people's
29:37
socks off that they know exactly what ride that I want to ride, or
29:41
what movies that I've seen. If I'm a Disney fan, if I met
29:45
Formula one, the ecosystem that's built around Formula one, and the amount of
29:49
fanfare that's being built right to it, I think what they have, what
29:52
they don't have are some of the handcuffs. Quite frankly, because they're not
29:57
highly regulated industries. Interesting they get to use that data and probably a more
30:02
expansive and a little bit more free way with tests and learn. Yeah,
30:04
Whereas I think, you know, it's a bit of a cautionary approach and
30:07
financial services, and rightfully so. I mean, we're systemically important to people's
30:12
lives and well being in financial services versus if I don't make it to some
30:17
of those other brands, no one's going to get hurt, right, yeah,
30:21
yeah, great, Well I appreciate both of you taking some time out
30:25
of your busy day to do this. Any other closing thoughts you want to
30:27
leave with, don't be scared and get in there now, because guess what,
30:33
the world is not waiting for you, and everybody else is talking about
30:37
it and doing this. So when it comes to AI, let's get in
30:38
their test, let's learn, let's you know, talk and have great conversations
30:44
because not only is everybody else doing it that competes against you, but your
30:48
customers are going to become accustomed to it in the space, and so there's
30:52
no better time than right now. Right, I would just offer that,
30:55
you know, think about an outcomes based approach. So again I kind of
30:59
parked labeling this point, probably too much at this point, but the use
31:03
case by use case opportunities, what are the expected outcomes and let's build a
31:07
business case that's really relevant to what you're trying to achieve. And we can
31:11
pressure us that with your internal data and outside of view, but let's build
31:15
a business case for change that is palatable and on whatever time horizon the institutions
31:19
looking for find of thing. It's like jumped into your point brain. Let's
31:23
understand what it is that it takes to be successful. Let's build a financial
31:27
model, because you know in financial services, I opened that the business spase
31:32
dost strategies and innovation, so looking forward to that. Awesome, Well,
31:36
Eric, great, thanks so much for that. Yeah, thank you.
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