Getting to the truth about how peers are using data today

The financial industry has found ways to use data to their advantage, but how much of the output is talk vs. actual results? This interactive session will uncover the truth about how your peers are using data today. We'll look at ways in which you can tap data for use inside your bank or credit union and examine the kind of impacts it can make. Join us to identify use-cases for your data and help us pinpoint some interesting ways to put these use-cases to work as part of your data strategy.

Transcript:

Daniel Haisley (00:07):

So my name's Daniel Haisley. I Lead Innovation for Apiture. Apiture. We're a digital banking provider. We serve the digital banking needs of community and regional financial institutions across the United States. In that ends the sales part of whatever I'm going to say, as I mentioned just a little bit ago, this is way less about you hearing me talk about anything really and more having the opportunity for you to be able to connect with one another, to be able to learn from one another, to be able to commiserate on the problems that you may be experiencing together and then to be able to brainstorm through some of the solutions that may be forthcoming. So we titled it, getting to the Truth about how your peers are using data today, you are your peers. So as I said, I lead innovation group. Think of kind of building our zero to one products, particularly I focus around data and APIs, but we serve retail and business banking and account origination, all the above and have a mantra. One is that the best ideas come from neither a smoke filled room nor crystal ball. The best ideas come from talking to customers and from actually having experiences that you can share back and forth. It's not from getting some, I dunno, deified input from high on the mount. It's some conversations like this.

(01:31)

I like to kick off every discussion, internal or external with this meeting would be a raging success if, what do we want to get out of this that justifies the time that we're spending together? So to me, this meeting would be a raging success if we come away with some kind of shared thoughts around what are some of the data problems or problems that could be solved using data that each of you are experiencing inside of your financial institutions or inside of your service institutions. And then selfishly, we want to get to know you.

(02:11)

So often, particularly in the data space, we get really enamored with focusing on the tooling is a buzz phrase space. The number of times you hear AI and LLMs and machine learning and blah, blah, blah, blah, blah. And even our team who does this every single day, we get really caught up in here's this bright, shiny tool. How can we use this tool to go and solve problems, which is the exact wrong way. Instead, focus on what are the business problems, what are the problems that your organizations are experiencing? And then find the tool to fit that.

(02:53)

So we're going to go through an exercise here momentarily, and then I'll be quiet for 10 minutes, I promise. Whereby we are going to be talking about data problems. Here is a word of caution. Mind the five why's. So as you're talking about the problems that your institution's experiencing, think of why that is. There's an anecdotal story about Jefferson Memorial. Jefferson Memorial in DC was having problems where it was degrading faster than it should be. Why is it degrading faster than it should be? Well, because they're having to put harsh chemicals on it because there were birds. Birds were hanging congregating there and birds were doing what birds do. And so it needed to be cleaned up all the time. They're using harsh chemicals to do that. So what do they do? You shoot the birds. Don't shoot the birds ask, why do we need to clean it up?

(03:50)

Because the birds are there. Why are the birds there? Well, it turns out the birds eat these particular types of spiders. Well, why are the spiders there? Well, the spiders eat this other type of insect. Why is that other type of insect congregating the Jefferson Memorial? It's because the lighting conditions at dusk are just perfect to where these insects happen to congregate there. So what do you do? You wait one hour later before you turn on the lights. The insects are gone, insects are gone, spiders are gone, spiders are gone. The birds are gone. You don't have to shoot the birds as you're talking through potential problems and solutions to those problems. Don't shoot the birds mind the five whys. Get to the core of why you're actually having those problems.

(04:37)

Here's some examples of data related challenges that we've heard. We serve 300 plus community and regional financial institutions. We have these conversations constantly, things that we hear pretty consistently. We have a really hard time differentiating ourselves from the competition. We don't have a 360 degree view of our customers. We don't really know what's happening in their lives. We've got data. We have no idea how to actually use it. How can I make this actionable regulatory compliance? I don't understand it. It's lagging behind. I'm concerned that I don't know how to handle data related concerns with regulatory compliance in the concern here. How can I grow deposits through data? How can I reduce risk using data? These are all the types of conversations that we have pretty consistently. I'll come back to this and I'll leave this up now. It's your turn.

(05:44)

You have a task and your task is over the next 10 minutes, get to know one another and specifically using the prompt of sharing a data related challenge that your financial or your institution is experiencing. And I will bet that you will find that you will have some recurrent themes at the end of the 10 minutes. We're going to pick one of those for each table, one of the problems that you're experiencing, and then we're going to talk through some potential solutions for it. More specifically, you're going to talk through some potential solutions for it. So keep in mind what's going to be coming up next That said, let's take the next 10 minutes or so and just brainstorm what are the types of problems that you're experiencing that may have to do with or could be solved with a better use of data? Good, good. Somebody give me a good, good. Alright, thanks. Alright. I hear a lot of good conversation happening. Just a quick time check. In about three minutes, I'm going to ask each table to choose a particular problem and then there's going to be another brainstorming about what are some particular ways that you can improve data or that you could use data to go about solving that particular problem. So as you're having the conversation start to circle in on, alright guys, which problem do we do we want to run with here? We've got three minutes left.

(17:10)

Alright, so now I've taken the last 10 minutes or so to try to prod each of you just to complain incessantly to one another about what problems you're experiencing and hopefully we've developed some empathy where we're having some shared and recurring themes about the same sorts of challenges, be it access to data or be it how to go about using data or whatever it may be. So the next step of this being able to talk about problems is find, it's really what can we do about it? What are some potential solutions to the problems that we're experiencing? It's where the rubber ends up hitting the road. So what I want to do now is take the next 10 minutes and let's ideate on some potential solutions. What are some challenges to those solutions getting implemented? At the end of this, I'm going to ask for a few brave souls to get up and just talk about what your team went through. Talk about the problem that your team selected and the ideation that you went through around some potential solutions. The fine folks at the Boca Raton have been willing to offer up unlimited tea and bread to anybody who's willing to stand up and talk about the solution that they chose. So tip the scales, but you may want to stand up quick on that one. So it is five after. We'll take the next 10 minutes or so and talk through some potential solutions to the problem that your team has chosen.

(18:38)

Good, good. I heard that. Did we choose a problem? Come on, you had one job. So we'll take another two minutes or so. Be thinking through who the representative from each table is going to be, who the brave souls are that that you're going to deem from each table to get up and talk through what the problem and the potential solutions that your tables come up with. All right, so we have roughly 10 minutes left together. So this last section is, oh, got to hit the clicker. We're going to learn from one another. We've kind of talked through the plans. So what I'm going to do is pick on a table. They're going to choose the representative from that table. That table will talk through what problem they resolved around what potential solutions were from that table. And then at the end, that table is going to pick the next table to do the same thing. So you can't be mad at me. I'm going to start with the party table upfront. Is that you? Awesome. What's your name?

Amanda Cook (28:15):

Amanda Cook.

Daniel Haisley (28:16):

Amanda Cook? Yes. Awesome.

Amanda Cook (28:19):

Okay. Yeah.

Daniel Haisley (28:21):

We get your attention, so Amanda's going to talk through their table's. Problem.

Amanda Cook (28:29):

Okay. So we determined that our biggest problem that we have, well a few different issues, but one that a few of us definitely had was a 360 view of our customers. So I mean, of course we can see that from today's conference. There's so many different systems that you can utilize and take in and there's going to be data in all those different systems. So we really talked about the issue of seeing that full 360 view of our customers and also just ensuring which system or that all the systems have the accurate data for a customer. And so really our thought process was really having a good customer management system that really does aggregate and connect with all those systems, keeps them all updated, but also is kind of the core piece of the top level of data. So if an address or something is updated in one, it's updated in all and everything is kept together. So that was our biggest issue and just trying to get somewhere where it's all centralized data.

Daniel Haisley (29:26):

Awesome. Thank you so much. Alright, so now the hard part, which one of these poor souls are you going to call on? Who's the next table?

Amanda Cook (29:34):

I'm just going to write over here.

Daniel Haisley (29:35):

Next party table. Alright, so is that a recurrent theme? Did any other tables come up with the Yeah, we don't have kind of a central source of truth. We have data in all these different systems. They're not aggregated. I'd be willing to bet probably about every table mentioned that in some fashion or another, but Amanda's table already took it so.

Audience Member 1 (30:02):

Well, we also identified data silos as an issue, but we couldn't solve that. So we moved on to another problem and started talking about the lack of a central repository for small business data that's needed to underwrite small business loans beyond the consumer that is the owner of the business and verifying that the business exists, et cetera. But how is the business actually performing when there's no necessarily audited financial statements or central repository and didn't really solve that one either. But something about scraping the bank accounts of the small businesses in order to figure out how to put the cash flows together and underwrite.

Daniel Haisley (30:44):

Awesome. So my man Max back there I know is from Monnet. So yeah, ears we're tingling there about cash flows, cashflow for small business us. Alright, but hold on the hard part. Who's the next poor sold. Don't be too excited, all silent.

Audience Member 2 (31:08):

We're all same answers.

Daniel Haisley (31:09):

Yep. So this table chose the product or data silo as well, inability to aggregate data across separate systems. So I may go to this table.

Audience Member 3 (31:32):

Thank you. So one of the largest problems that we're having, sorry this was a little loud. In our core, there's multiple places to put the same piece of information. So something as simple as a phone number could live in four or five different places. So obviously this causes inconsistency, it causes challenges with the systems that are interconnected. So our EFT system, our online banking system really pull from one master place and we have users that don't use that place. So then we have customers that their information's not out in those different systems, which causes service impacts, etcetera. So I'm going to hand the mic off to an individual who is going to talk a little bit about the potential solutions that he sees to that data management problem.

Audience Member 4 (32:26):

I thought we were going around looking at the.

Audience Member 4 (32:34):

Well, I can think of three approaches. One approach in this particular case could be that you set up a centralized governance system. I mean, the fact that the code has multiple places store the data cannot be solved other than the code itself being changed significantly. If that can't be possible, then one approach would be just to set up a data steward approach and govern the data in such a way that these discrepancies do not occur. The second approach could be is a more cumbersome and a classical approach of choosing a master data management solution. In that case, the data that you have problems with, in particular in bad data, you could set up an independent source repository of storing that information using an independent governance process. And from that source, the data then goes to any other system that needs the data. So since you have a single source set up in the master data system, then that problem goes away. It's a very expensive solution. Some companies have these master data management solution that the licensing costs would be over a million dollars and several million dollars are needed to do those projects. The third approach perhaps could be that you take an API approach and set up one source or one API, which takes care of these discrepancies and then feeds that data to other applications. So that would mean some re-architecture and rework from the consumer side to pick up the data from the API.

Daniel Haisley (34:24):

Thank you very much. Do you have a particular table that you want to pick on? One behind us. You really don't like these guys? Did you pick these? Alright, perfect, perfect. That's not nice at all, but looks like I'm.

Audience Member 5 (34:38):

So hi, good afternoon. So I think our biggest struggle right now is we've began the process of centralizing our data, but it's enriching the customer's 360 degree view. So all our customers, we have a lot of disparate systems putting where our customer's data resides. So we started to put it into one, but it's now enriching it to include interactions and transactions. But also we're flipping our culture to be sales and service versus transaction, right? So managing that, but managing also being able to manage a core that we've been on for the past 38 years. It is very typical. We're a community bank from Guam, bank of Guam, and so all things being equal, I think it's just enriching that data, making sure that we can create a layer. We use API's as well as an operational data source, so real time versus batch. But I think we have so much going on and it's just a journey in figuring out what's the best approach, what's the most important. Because as I was sharing with the team, we're also going through a conversion of getting our core to the cloud. So lots of moving pieces, but also very important. I think the foundation fundamentals is going to be the data. I'll pass it over to.

Audience Member 6 (36:06):

Yeah, I think this is a common problem. Obviously it's many tables are talking about aggregating data from different silos, whether that is into a data warehouse, data lake middleware, and in this specific situation as they're looking at potentially a core replacement, then is it that middleware layer that we can then plug in whatever core, but they're also, I think the biggest issue at this point is the prioritization of the different data sources and then trying to figure out what comes first, which really it's almost a business process and then the technologies got to follow from there. You had brought up that one of the things in finding somebody that has some expertise, whether that's a consultant or somebody that knows those different systems or middleware might be able to help. But I think we thought stepping back, brainstorming, get the prioritization. They've got a vision for where they want to go, but it's the priority of the steps of going through that process. Is that fair?

Daniel Haisley (37:15):

Alright, so I want to thank you guys so much for taking the time to share with us over your lunch break. And we've heard some recurrent themes, right? Access to data. Once you have access to data that's coming from several systems, do we consolidate that data? We really didn't talk about using the data. We got focused on getting access to the data. I appreciate everyone's time today in sharing with us. Please, please feel free and reach out directly if Aperture can help in any way and will help you enjoy the rest of the conference.