Leveraging Data for Strategic Decisions and CX in Banking

Jim Marous (00:08):

Hello, and welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous, owner and CEO of the Digital Banking Report, and co-publisher of The Financial Brand. In today's episode of Banking Transformed, we're exploring the transformational power of data in the financial services industry.

Jim Marous (00:27):

Joining us is Jeremiah Lotz, the Senior Vice President of Product & Data Experience at the Velera, formerly known as PSCU/Co-op Solutions. We're going to be discussing how banks and credit unions can harness data to drive strategy, decisions, enhance customer experiences, and navigate the difficult balance between AI and human interactions.

Jim Marous (00:53):

Jeremiah will also share his expertise on developing effective data strategies, ensuring privacy and security, and measuring the success of data initiatives in an ever-changing environment. It's certainly not a secret that data and artificial intelligence are revolutionizing how banks and credit unions operate, make decisions, and serve their customers and members.

Jim Marous (01:19):

The challenge for most organizations is where to start and how to implement solutions at speed and scale. In other words, how do you deploy them to really make an impact as opposed to just talking the talk.

Jim Marous (01:35):

So, Jeremiah, before we begin, could you introduce yourself to our audience and share a little bit about how Velera helps financial institutions build a better, robust data strategy that can actually generate pretty quick returns?

Jeremiah Lotz (01:50):

Absolutely. Well, thanks for having me. Jeremiah Lotz, I'm the Senior Vice President of Product & Data Experience at Velera. We are an integrated fintech organization serving small financial institutions and credit unions.

Jeremiah Lotz (02:01):

And when we think about data and the opportunities there, really, our goal is to help financial institutions identify where the strongest use cases are in which they can leverage data to create great experiences, either internally, externally, and get the best value out of that data.

Jeremiah Lotz (02:20):

So, as a payments organization, bringing in multiple types of data and helping provide insights is really the goal that we have, and then obviously, advising and investing in technology to strengthen that.

Jim Marous (02:33):

So, your organization was previously known as PSCU, and you're a CUSO, or a credit union service organization, but it's interesting because this was actually education for me – you don't serve only credit unions, you also serve community banks in helping them deploy solutions and build solutions for their customers and members, don't you?

Jeremiah Lotz (02:57):

That's right. That's right. We are CUSO-focused in the credit union space, and that's been our legacy. Credit unions and community banks have similarities in the focus in which they have for their consumers, whether they be individual consumers or small businesses, and really that community feel that service that they intend to provide.

Jeremiah Lotz (03:18):

And so, we really believe the technology, the services, the products we have available to us, we're able to put those in the community banking space through our primex brand and create a significant opportunity for those financial institutions as well.

Jim Marous (03:31):

As we've talked about on this podcast in the past, and as we talked about prior to starting the recording, some of the best data maturity we have seen in banking and credit unions has happened at the biggest and the smallest financial institutions.

Jim Marous (03:48):

And I personally believe that some of that is because it really takes leadership to be able to embrace change for any organization to move forward in their digital transformation process. And it's interesting because through the years … I have way too many years in financial services.

Jim Marous (04:06):

But it's interesting because even at the beginning of my career, we were talking about trying to use data to build better marketing solutions, to build better overall internal product development and solutions, and they improve operations.

Jim Marous (04:22):

However, over the last few years or several years, if you want to take it, really, the importance and emphasis on data strategy has exploded. Everybody's talking about it. I talked to you about it before the podcast started that if it was a drinking game and we said, "We'll have a drink every time somebody mentions data or AI in a banking conversation," we'd be alcoholics.

Jim Marous (04:50):

So, as we look at this, how has the role in data within the banking industry evolved over the past few years, even during a time of economic uncertainty?

Jeremiah Lotz (05:01):

You know, significant, obviously, and we've been talking about data for quite some time, but we also have been talking about digital and digital transformation for quite some time as well, and that's where many financial institutions have put an emphasis over the last several years, is how to create digital experiences that maybe reduce operational impact, increase convenience for the consumer, et cetera.

Jeremiah Lotz (05:22):

When we start thinking about data, now we have an opportunity not just to look at data from a descriptive perspective, what can it tell me about what has happened, what my performance is as a financial institution – but now, we've got the technology and the skillset and the capabilities to start to leverage data to actually drive and change those experiences.

Jeremiah Lotz (05:44):

And so, the reason I bring up digital is because those two things connect significantly, and that's where you'll see great success is how can I use a data strategy, not just to look at what's happening in my financial institution, but how can I use data to actually change the experience that someone is having through their digital interactions with me, the consumer themselves, the operations team, or whatever it is.

Jeremiah Lotz (06:08):

And a solid data strategy has to encompass really three things. One, it's got to encompass governance, and that's critical to success, and that's where you see investment of where are we going to store data? What are we going to call it? How do we make sure that there's consistency in the data that we have? Creating an architecture and an infrastructure to consume and ingest and store that data is important.

Jeremiah Lotz (06:35):

But then the other piece to it that's critical in a data strategy and kind of where we're at and where we see these disconnections is what are the business objectives? What are the outcomes that we want to accomplish as the financial institution?

Jeremiah Lotz (06:47):

And that's where when you think about large institutions being successful and small institutions being successful, they both have had to be super deliberate in what the use cases and the outcomes are.

Jeremiah Lotz (07:00):

In a large institution, you might have the finances to go big and invest heavy in the technology, but you want to be able to provide and show a maximum return. In the smaller institution, maybe the investment opportunity isn't as great. So, you have to hone in on very specific use cases. In either scenario, the business outcomes are the important part of that whole data strategy, I would say.

Jim Marous (07:23):

It's interesting, I referenced the GPS system in a car quite often in our podcast, and you can't get to where you want to go unless you identify where your final destination is. You can't just simply say, "I want to go north." You got to be very specific so that the system can do what it's meant to do, which is get you there as efficiently as possible.

Jim Marous (07:44):

It's interesting when we look at data strategies. I think if I was to peel back the layers at most financial institutions, most financial institutions realize that their data right now is in silos, it's not clean, it's not effective enough to be used right now, but that's no longer a hindrance.

Jim Marous (08:04):

I think that's one of the things that your organization can really help financial institutions with (if I'm not mistaken), which is, "You don't have to come to us with clean data, we can help you make it deployable and usable in a very quick amount of time," which is really one of the value propositions you have, isn't it?

Jeremiah Lotz (08:23):

Yeah, it is, absolutely. I think when we first started talking about data as an industry, the concept was I need all of it, I needed it all in one place, and I need to do very smart things with it. And the reality is, yes, you need access to your data, but what you really need are those defined use cases and outcomes.

Jeremiah Lotz (08:43):

And then with that, you can identify, "Okay, actually I need this small set of data, or I need these very specific fields." And with that specific piece of information, then you can ingest that into a partner like us or into your own ecosystem, and you can start to run very specific models off of it.

Jeremiah Lotz (08:59):

And so, I think that's the part that an organization like us, we have the opportunity to say, "Okay, here's the engine in which we're going to run this data through, whether it be an insights engine, modeling engine for predictive insights, et cetera, and here are the data components I need to have a good outcome. I don't need every aspect of your data, I don't need every piece of it – I need these 10 fields if you will in order to have a solid outcome."

Jeremiah Lotz (09:25):

And I think that's where partners like us can kind of speed up the strategy and the outcome process for financial institutions, is being able to create those repeatable models and be able to define very clearly, “Here's the subset of data I need in order to reach that positive outcome.”

Jim Marous (09:42):

You referenced it and I find it very interesting that you keep on referencing being able to deal with the small problems first and then expand it. So, I'm sorry if I'm ahead of data in a financial institution, it seems to be an overwhelming challenge that I have. But you keep on referencing the breaking it down and dealing with specific solution sets or specific areas of the financial institution you want to deal with.

Jim Marous (10:10):

When you're meeting with a financial institution for the first time, if there's a universal one, maybe there's 10 of them – but if there's one major challenge that they have, what are they trying to take care of? What are they trying to solve for right now as you sit down with an organization for the very first time?

Jeremiah Lotz (10:30):

So, I think most financial institutions have data, have access to data, or know how to get the data, and they're trying to solve for what is the best use of it. In most cases, financial institutions are continuously looking for ways to fight against fraud. Most financial institutions are looking for ways in which to best protect themselves and their consumers against regulations and privacy and those types of things as it relates to data.

Jeremiah Lotz (11:02):

And then they're also looking for how can I use this data to change my operations, ultimately to enhance it, make it more efficient, and then how can I create a compelling and loyal experience for my consumers that make them not want to leave me.

Jeremiah Lotz (11:19):

The research we've recently done, we have consumers who will leave financial institutions because of the experience they have and the channels and that sort of thing. And so, now I know I have this pot of gold or this data, if you will, how do I use that to create the best experience to keep those consumers together with my financial institutions? So, those are kind of the key areas in which they are focused in on.

Jeremiah Lotz (11:47):

And when you break that down into, yes, I need a data strategy, but really what I need is a data strategy to solve for these primary problems that I'm facing against. So, how can I use data to provide better fraudulent tools and prevent fraud? How can I use data to organize my organization when it comes to regulation and compliance? How can I use data to drive very hyper-personalized experiences for my consumers?

Jeremiah Lotz (12:17):

When you start to break it down that way, then you're using the same sets of data but you're able to go solve these individual problems. When you talk about, "Hey, I need you to go run data and make it work and make it a revenue versus a cost for us," that's overwhelming. When you break down where are the places, the financial institution either struggles or has opportunity, then it's easier to find ways and how to leverage that data.

Jim Marous (12:45):

So, it's been a long time since I've been on the banking side of things, but I would think what's interesting right now is that organizations already have core providers. They have core providers that will say they can do almost everything that you are saying that your organization does.

Jim Marous (13:07):

How do you make headway with an organization where there may be some people within the organization saying, "We already have this, we already have our data, we already have a partner that helps in this.” How do you convince organizations to what I'll say, double down on the investment to then take this, what you do over, and above what their core provider can do?

Jeremiah Lotz (13:30):

That's a great question. I think it goes into those different areas of operations within the organization. So, when you think about the contact center and how the contact center and the fraud operations teams are being ran, what data do you have accessible to you to prevent a call from coming in and to provide a better experience on the front end?

Jeremiah Lotz (13:52):

What are you able to do once that call is in your operations group to either have your team or someone like our team leveraging the data to be ahead of what the consumer is asking for, and/or even get them to an anticipated step prior to them even knowing it. Those are the types of areas where we truly have the opportunity to change that experience for the consumer and the financial institution.

Jeremiah Lotz (14:20):

And so, really, the question is whether you've got data in different places or a data strategy with other partners, how are these areas being impacted in your digital channels? How are you bringing together data in your payments ecosystem, and data that maybe isn't even specific to your financial institution but talks about geographical data and individual consumer data and creating a profile to say, this is this unique consumer, and while today he has these products with me, I know based off of demographics in his area, the average income, the average type of spend, this is the thing that's likely to happen next for that consumer.

Jeremiah Lotz (15:02):

So, how are insights getting created and then getting pushed out to that consumer through the different channels? We host channels that are used by our financial institutions, consumers, and so how are these other partners putting those in the hands of your consumers?

Jeremiah Lotz (15:17):

And really, it's not necessarily that we believe we are the only entity that plays in the data space with our partners or our financial institutions. It is how can we leverage the models and the tool sets and the technology and insights that we have to take it even a level further.

Jeremiah Lotz (15:35):

So, if they've got something that's coming out of an application or tool set that they have, great, how do we augment that within our engine and make the model even stronger to provide yet a better experience, again, for the FI and/or for the consumer.

Jim Marous (15:52):

So, again, how your organization works with financial institutions, both credit unions and banks, community banks – I find a lot of challenge in that people can get data, they can get insights, but where I find the gap a lot of times is in the deployment of those solutions to bring results.

Jim Marous (16:12):

Do you help financial institutions because of all the different organizations that you partner with – do you help organizations actually with ideas around the deployment, how to use the data to solve for specific problems so that they can get the low hanging fruit that they're looking to get revenue from?

Jeremiah Lotz (16:30):

Absolutely, absolutely. We have a set of tools that really is focused around insights and using data to capture specific insights and predict actions. And then we have a set of consulting resources that go in and talk with the financial institution about where are the challenges like you were talking earlier; where are the areas that need to be solved, or where are there opportunities that you could see increase and enhancement, and then how do you leverage these insight tools to make decisions in those areas?

Jeremiah Lotz (17:03):

Or how do you use the outcomes of some of these predictive models specifically to say, "Okay, if your goal is to create lending growth, then what is that opportunity specific to your financial institution? And then how does the model we have bring in specific data, tweak that data, and provide a specific outcome or output that can be delivered to your consumers?"

Jeremiah Lotz (17:28):

So, it's not just about, "Hey, here's a set of data and some tools that you can leverage," it's really about, let's again go back to those outcomes that you want to achieve, and let's consult best on how to make those things happen.

Jeremiah Lotz (17:42):

And then one of the other things I think is important is when you look at the challenges financial institutions have and kind of the message you were sharing earlier, it's daunting to identify how to establish a data strategy. Part of that is governance and having a framework there.

Jeremiah Lotz (18:02):

We've done the work to define and establish what is a solid, strong data governance framework, and how can we provide that consultation and those tools that we've established to a financial institution for them to leverage internally, that's one of the benefits as well.

Jeremiah Lotz (18:19):

We also have gone through the effort of how do you bring data literacy into an organization? If you don't have some level of data literacy across the organization at kind of every level of role, then your data is not ever going to get used to its fullest opportunity.

Jeremiah Lotz (18:35):

So, as we've established some of those data literacy skills and capabilities and programs, how can a financial institution take advantage of the things that we've learned kind of on their behalf to shorten their journey, if you will, to some of that success.

Jim Marous (18:51):

You know what, you brought so many key elements in your discussion just then, because I think one thing that I've seen in The Financial Brand Forum, our event that we do every year, and we bring together all these solution providers, all these financial institutions – something I saw, gosh, I'm thinking it was around 2018, maybe 2019, were the actual integration and communication between solution providers trying to partner to bring better, overarching solutions to finance institutions to make them better at what they do.

Jim Marous (19:26):

And you're already talking about the fact that you help organizations through their path. And I was at an organization back in (I think it was a year ago) Amsterdam, and they said a really key element, which was if you are working with a partner organization to build a solution, don't try to reinvent the wheel and don't try to build from scratch when these partners, the value they bring is so many experiences.

Jim Marous (19:56):

And they said about 80% of what organizations want from a partner organization such as yours had been done already. We don't have to go through the iterations of it, we need to implement them, but don't spend time on that 80% that's already been answered for, spend on that 20% that's differentiating your organization.

Jim Marous (20:16):

I thought that was so key because as a financial institution, we so much say we're different, we want to start from scratch and you're going, "Man, the value you get from us is we have hundreds of organizations we've worked with in the past. Why rebuild what's already been built?"

Jim Marous (20:33):

So, from your perspective, I think that it's interesting. We get aha moments. Because of the co-collaboration and co-innovation that's being done between financial institutions and solution providers such as yours, there's a lot of times you'll come across something that you go, "Wow, that is really, really neat how that was implemented in using data."

Jim Marous (20:57):

Can you give some examples of how financial institutions that have been partners with yours have used data to really raise the bar on maybe customer experience, maybe it's back-office operations, just something that you just said that was perfect the way they did it.

Jeremiah Lotz (21:15):

Yeah, so I would say there's two examples that come to mind. One, we've seen some financial institutions leverage data and some technology around that data to change lending practices in a way that allow them to speed up that process, and go from what oftentimes is a manual review in credit union space specifically, but in community banking, manual review process to getting that consumer to a true near instant approval and even access to that card.

Jeremiah Lotz (21:52):

And some of that capability is there's features and functions that an organization like us have made available, but really, it's data that the financial institution has been willing to use to make those decisions.

Jeremiah Lotz (22:04):

It's not necessarily new data, it's not necessarily new technology, but it is getting financial institutions to a place of saying, "Okay, I'm comfortable leveraging this type of data to allow for a real-time decision or allow for a real-time interaction, versus everything having to go through kind of a manual touch."

Jeremiah Lotz (22:22):

Those have been the ones who have really kind of leapfrogged both the consumer experience but also, the operations and been able to kind of speed up and make more efficient the operational aspects of the organization.

Jeremiah Lotz (22:36):

And then the other aspect is we've seen some financial institutions do some focused, hyper personalization capabilities that were kind of mimicked off of other industries. If you look at even the pet medical industry, for example, pet care, and you'll see experiences of, they know I have a dog named Charlie who has chronic ear infections, and they know that every three weeks or every four weeks, it's likely to happen again.

Jeremiah Lotz (23:09):

They're in front of me talking about getting me in, getting an allergy shot, getting whatever it might be before that infection has actually come back around. And so, why not do that in the financial services space, and do it in a way that's not like, "Hey, I'm looking deep into your finances, isn't that eerie,” but do it in a way that's anticipating: “It's likely you're going to be encountering this experience, how can I be here to help you?” That's where consumers are like, "Oh wow, this is the financial institution I want to stay with."

Jeremiah Lotz (23:40):

So, if we're able to do it when it comes to your dog might be getting an ear infection, why can't we do it when it comes to, "Hey, you might need some extra help this month, or you might have an opportunity to save a little bit more next month, let's go ahead and get that in front of you."

Jim Marous (23:54):

That is so key. It's interesting you say that because there's two elements there. One, consumers, members and customers want to share information about their financial goals and what's challenging to them. We just don't ask them.

Jim Marous (24:10):

And in a digital world, we can ask them in such an efficient way, and then deploy and utilize that data in such a unique way that really can help them. We just are afraid sometimes to ask, saying, "You know what, consumers may not want to share it."

Jim Marous (24:24):

Look at what we're sharing to the corner retailer, even the restaurant we go to on an ongoing basis, and how much it makes us feel better, when let's say, we call for carry on and they go, "Okay, so do you want the same order you had last time? This is what you had."

Jim Marous (24:39):

And you go, "Man, you just saved me a lot of time. I don't have to go through and say, geez, I forgot to put the [inaudible 00:24:44] on the pizza that my son likes as opposed to …" they already told me and reminded me what's worked before.

Jim Marous (24:51):

The other element of that I think that's key is we don't look at all of the data we have available to us, and the one that really hits hard, both from the standpoint of helping customers with their journey is flow of funds. We don't look often enough at where does a customer go and what other providers are they using right now to solve problems that we could have solved for them?

Jim Marous (25:17):

It may be a savings app, it maybe something like Acorns, it may be a credit card app, it may be a new car that they're looking to buy and they've done some test drive, which means they've hit the credit bureau and we refuse to use the data that the car dealers are using to communicate with us, which is, “We know you're shopping, we can help you.” These are the key elements and new uses of data that is so effective.

Jim Marous (25:45):

And as you said, I want help. I mean, there's not a consumer out there that wouldn't like their financial institution to help think for them. I mean, again, was talking to somebody yesterday about the things that have been going on internationally around pagers blowing up and walkie talkies blowing up – I don't know if I could deal with my phone not being in my hand or my GPS system on my car not working. Well, if we have partners that can help us with that, that’s going to make it better.

Jim Marous (26:15):

So, we hear a lot about AI and advanced analytics. How do you see organizations combining both AI tools and human expertise in the overall implementation deployment of better solutions?

Jeremiah Lotz (26:33):

That's a great question. I think that the comments that you were just making leading into the answer of the question you just asked, one thing I'll speak to is one of my favorite use cases of advanced analytics and machine learning and all that, is a model that we've created that allows us to look at a consumer's transactions and realize the way in which they are spending is changing, what can we do to help them?

Jeremiah Lotz (27:03):

Suddenly they are using their open credit to pay for normal things like food and electric and that sort of thing. Is there an action that I, as a trust at financial institution can take to help them in case they need a bit of financial assistance? Or is there a change in behavior where we're seeing travel and different types of activities like that happen that I can give them a better benefit?

Jeremiah Lotz (27:30):

And so, back to the conversation of we are not only willing to give our data, we now are seeing all those examples of what you just talked about in restaurants and in shopping and that sort of thing, that it's becoming an expectation.

Jeremiah Lotz (27:42):

It's becoming an expectation of consumers where we're saying, "I know you have this data. I know you have this information, so why can't you help me? Why can't you give me this advice or tell me?" So, I think that's important and a strong use of advanced analytics and machine learning and that sort of thing.

Jeremiah Lotz (27:59):

But to the question of AI and how to combine the element of AI and not losing that human capability or that human touch is … the best use of AI is for it to help in a human decision, not replace every human decision.

Jeremiah Lotz (28:20):

And so, if I can leverage an AI application or ML, machine learning, that sort of thing, to run large sets of data and get to a place of very tactical actions where it can speed those up and make them happen in much less time, great, that’s a very practical use.

Jeremiah Lotz (28:38):

When it comes to exactly how it's going to impact the consumer or the end user, if I can see the output of that and then leverage that to make a quick decision, but still have that human connection tied to that, that's where I'm going to continue to have that trusted responsibility and really that responsible and ethical use of these applications.

Jeremiah Lotz (28:57):

Like I can see efficiency, I can maybe even provide something that I wasn't expecting to, but I'm not losing that human element to make a proper decision as well. And I think bringing these two together is key for financial institutions, and that's where they're going to see the strongest success. They're going to be able to create some efficiencies, but they're also going to be able to stand in front of consumers and say, "We're doing this with still that human power and connection to it."

Jim Marous (29:25):

So, Jeremiah, you work with a large number of financial institutions, both yourself and people within your organization. If there's one major challenge that you see financial institutions have with regard to their data driven approach or their data strategy, what is it?

Jeremiah Lotz (29:42):

I think understanding the desired outcomes and the goals. What do you want to accomplish? So, I hit on that at the beginning, I'll repeat that over and over, is if you understand specific goals you want to accomplish, then your data as an enabler becomes much easier. You're not trying to solve the problem of data, you're trying to solve problems with data, and I think that can help a financial institution kind of get out of their own way in many cases.

Jim Marous (30:11):

Boy, that is so key and it's so rudimentary. You’d think it's common sense, but again, I think that's where work with a partner such as yours that says, "Okay, before we even start, you may think I need data help." That's not an objective, that's not a really good starting point for a data-driven strategy.

Jim Marous (30:33):

But I think that you help organizations not only get to understand what they have to know to get to where they go, but also compare back the strategy and say, okay, we're not … saying you're going to use it to improve the customer experience is not a strategy, but saying that you want to do it as you brought up earlier, to improve the timing and the amount of time it takes to implement a loan strategy, we're getting closer then and I can get the return faster.

Jim Marous (31:09):

So, speaking of returns, how do you see organizations measuring the success of their data initiatives? Because we sometimes skip over that, but somebody within the organization is going to say, "Okay, what was the return?" And I think that's where the rubber hits the road. So, what are the ways you can measure success?

Jeremiah Lotz (31:35):

So, I think when you look at how you're going to use data, and the investment you're going to make, you've got to look at internal opportunities as well as the external ones. If you can measure and see both of those, it's going to just create a stronger picture, if you will, and a stronger story to feel as though, "Yes, I've made the right investment here."

Jeremiah Lotz (31:55):

So, when you look internally, some of the KPIs are around how is the data being enabled for business units for them to make decisions? Am I putting data in the hands of business unit A and giving them the tools to see it, the tools to digest it, and are they able to make stronger business decisions where they can invest a hundred thousand dollars in that business unit and it has some level of higher return than what was happening years past?

Jeremiah Lotz (32:26):

Because they're not just guessing at what the outcome will be, they're using data and the capabilities and the insight we've given them to say, "I can intelligently predict this is going to be the outcome based on this data." So, that's one area, is how are internal folks using it to make strong business decisions.

Jeremiah Lotz (32:44):

The other piece is how are we enabling and changing the operations within the organization to create efficiencies? When we talk about AI, machine learning, robotics – none of those are successful without a strong data platform to drive off of or a data set to drive off of.

Jeremiah Lotz (33:03):

And so, if you're investing in that data output, that data strategy, the value of efficiency isn't just the AI tool or the robotics tool, the value – that occurs because you've got a solid and strong data foundation.

Jeremiah Lotz (33:18):

So, how are you seeing those operational efficiencies leveraging your AI machine learning robotics on top of your data solutions, and you can measure those impacts and those outcomes of efficiencies.

Jeremiah Lotz (33:32):

And then the third is the consumer side, whether it is that you build a metrics that is, I want to increase — Wendy and I want to decrease the time for a transaction, or I simply want to go after satisfaction and longevity, and I can measure that my personalized or enhanced experiences are causing people to leave me less often; we can measure those or we can measure individual outputs of specific functions that are data-driven.

Jeremiah Lotz (34:04):

So, I think you've got to have a mixture, but you've got to look both internally and externally in order for that story to be well-rounded, and for you to really feel that you've got the investment because some of them are going to be soft opportunities and some of them are going to be very hard cost opportunities, and that full picture is really what's going to make it solid.

Jim Marous (34:23):

So, you've been in this business for quite a while, and a lot has happened, especially in the most near term. If you step back a little bit, what excites you about what is going to be happening in the data, analytics, AI area within financial services in the next couple years?

Jeremiah Lotz (34:44):

So, very selfishly, I would say data will become the center of organizations and how they make decisions, that's where we would all love to be is what we-

Jim Marous (34:55):

And that seems common sense, but it's not really a given at most organizations.

Jeremiah Lotz (34:59):

That's true. It's not. It's not. And I think now we've got to a place where if you're going to invest, and the industry as a whole has, and nearly every institution has in some way, directly or indirectly … we've invested a lot in the data infrastructure and ecosystem that's available to us (use it). Use it to make decisions, have an expectation of your data leaders that they are bringing out the insights, again, to use internally and externally.

Jeremiah Lotz (35:27):

And again, I go back to you have consumers who are willing to change financial institutions based off of experience. That's a pretty big deal when you think about, especially, in the community banking space and the credit union space, we want to hold on and grow those members and the best way to create an experience that they're looking for is through personalization. You get to personalization through understanding and driving data insights.

Jeremiah Lotz (35:54):

So, consumers are going to move, and are willing to move to other FIs because they can get something that feels more personalized, you've got to create that using the data. So, using data internally to make decisions, using data externally to drive experiences, I mean, I think those are going to be where we see game changers in the financial institution space, and really where you should see kind of data sitting in the center of your strategies as a whole.

Jeremiah Lotz (36:19):

When you look at your financial institution's strategies, each of them are likely to be powered in some way by the data assets that you have. And if they're not, then you should question what's the value and what's the reality of these strategies come into play if I don't have the right data behind them.

Jim Marous (36:38):

It's interesting, you keep on coming back to the deployment and we have a lot of organizations. I remember back in my days in the banking world and even serving banks, a lot of times the data solutions were just to know more about your customers or know more about your processes.

Jim Marous (36:58):

Really, rubber doesn't hit the road until you deploy, and until you do something with it. And we sometimes take that for granted, but if you look back and peel the layers back, unless you find a partner organization like yours that helps and really pushes for the deployment, because it's self-serving to a degree.

Jim Marous (37:17):

if you're not seeing that the organization is getting return on their investment as opposed to simply getting better data, you're sitting there going, "I'm one step away from losing this customer" because someone's going to challenge, why are we spending all this money if we're not getting a return? And I think that's such a key element that it's the hardest step, is actually doing something with what we know.

Jim Marous (37:41):

So, Jeremiah, one last question. I'm going to put you now on the other side of the desk, I'm going to put you in charge of a financial institution's data strategy: what's the one thing you have to do today to be effective tomorrow?

Jeremiah Lotz (37:57):

Understand what data we have and where it's at, and get it to the right place. Get it to a centralized place.

Jim Marous (38:04):

And don't fool yourself in thinking you have that already. I love the core providers. They're great organizations, but they can't be the best at everything. And I keep on saying pick your solution providers that you find you have trust in that will get you to where you want to go, that will help you find out where you need to go, which you brought that up more than a few times saying, "If you don't know where you're going with your data strategy, you'll probably never get there."

Jim Marous (38:32):

And then find those organizations that are willing to partner with other organizations on the deployment side. I don't think your organization (and maybe it does) offers, let's say a brand-new new account opening experience, but you provide the data foundation for that. We'll combine these because all your solution providers today want to work with other solution providers so that they can amplify the effectiveness of what they provide.

Jim Marous (38:58):

Jeremiah, it's been great having you on the show today. You've shared a lot of great information. I'm looking forward to visiting with you again because I think you have so much insight into the transformation of the industry and also where it needs to go to be effective.

Jim Marous (39:14):

And I think, we've kind of scurried above this and not really hit it, but at the end of the day, it's going to take leadership within the financial institutions to actually allow this to happen. Technology's great, data's great, all these things are great, but unless you have leadership that has a vision for what's possible in the future, we're going to have a challenge. So, thanks again for being on the show.

[Music Playing]

Jeremiah Lotz (39:40):

Absolutely. Thank you. I appreciate it. Vision and leadership are going to be key here. It's a fun journey that we can embark upon.

Jim Marous (39:46):

It is fun, I agree with that.

Jim Marous (39:50):

Thanks for listening to Banking Transformed, the winner of three international awards for podcast excellence. If you've enjoyed today's interview, please take some time to give our show a five-star rating. Also, be sure to catch my recent articles on The Financial Brand and the work we're doing with the Digital Banking Report.

Jim Marous (40:09):

This has been a production of Evergreen Podcasts. A special thank you to our senior producer, Leah Haslage; audio engineer, Chris Fafalios, and video producer Will Pritts. I'm your host, Jim Marous.

Jim Marous (40:22):

Until next time, remember, you'll never reach your destination if you don't know where you're trying to go. This is especially important with data strategy.

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