Aired September 30, 2024

Unlocking growth for credit unions

With

How transaction data is driving member engagement and deposits.

Join Bud’s Account Executive Corey Horr, Great Lakes Credit Union's COO Elizabeth Osborne, and Keith Pfirrman, responsible for Growth at Akoya, for an engaging webinar exploring how transaction data can unlock growth, drive member engagement and maximize deposits for credit unions internationally.

This webinar explored how credit unions can capitalize on the transactional data they already hold to drive financial well-being and clarity across their member base and stimulate deposit growth.

[Mike Lawson: 00:00.1]

Hey everybody, Mike Lawson here with CU Broadcast. Welcome to today's webinar. Thank you so much for taking time out of your day to join us and talking about unlocking growth for credit unions today with the fine folks from Akoya, Bud and Great Lakes Credit Union.

[Mike Lawson: 00:23.9]

And yeah, we're very, very happy that you guys are here today. And yeah, again our topic today is going to be unlocking growth for credit unions, how transaction data is driving member engagement and deposits. And it's going to be a loaded, loaded conversation today.

[Mike Lawson: 00:40.2]
So we got a lot to cover and a little bit of time this hour. This 45 minutes is going to go by really, really fast here. But I want to get, let's get to some introductions before we dive into our chat today as well. Let's bring in our first guest today and that will be Elizabeth Osborne, Great Lakes Credit Union, Chief Operating Officer there.

[Mike Lawson: 01:02.8]
Elizabeth, how are you today?

[Elizabeth Osborne: 01:05.4]
Fabulous. Thanks for having me.

[Mike Lawson: 01:07.8]
You are welcome. You want to tell us a little bit about Great Lakes Credit Union and the fine state of Illinois? I always say Michigan and you always correct me, but it's in Illinois. I did.

[Elizabeth Osborne: 01:19.3]
We are, yeah, Great Lakes Credit Union. We are a low income designated financial cooperative. So credit union in the Chicagoland market. We have just over 1.6 billion in assets we offer. We're one of only I think six HUD certified financial institutions in the country.

[Elizabeth Osborne: 01:37.7]
So very excited to be here and share our story and how we're using transactional data.

[Mike Lawson: 01:42.1]
Fantastic. Fantastic. All right. And let's bring on Mr. Keith Pfirrman from Akoya. Keith, how are you sir? Good to see you coming to us all the way from Boston, Massachusetts.

[Keith Pfirrman: 01:53.0]
Yeah. Slightly earlier, slightly colder. Yeah.

[Mike Lawson: 01:57.1]
How are you today? Give us a little elevator speech on Akoya, if you will.

[Keith Pfirrman: 02:01.5]
Yeah. Akoya is a data access network. We've been built from the ground up with the aim of transforming the way people share their data, with the aim of making it more secure, private, reliable and transparent. We do that through an API only network that doesn't rely on screen scraping and ensures that the consumers permissioning where their data goes and has full transparency from the financial institution downward.

[Keith Pfirrman: 02:25.0]
So bringing security and transparency.

[Mike Lawson: 02:27.3]
Yeah, thank you. And you are in charge of growth there at Akoya. What does that mean? I mean obviously new business or share with us your responsibilities.

[Keith Pfirrman: 02:35.6]
Yeah, exactly. You know, it's a two sided network. So you know, our job, you know, consists of going out to, you know, banks, credit unions, other financial institutions, brokerages and making sure that they feel that they're being serviced through the... with the aggregation Market to make sure their data and their consumers' data is getting accessed appropriately.

[Keith Pfirrman: 02:53.1]
So it's kind of that side of the network as well as the fintechs and other credit unions and other financial institutions that want to actually consume data. So we're growing the network on both sides and then kind of developing the product accordingly.

[Mike Lawson: 03:04.9]
Yeah, good deal. Good deal. All right, thank you so much, Keith. And then our last but not least guest is Mr. Corey Horr from Bud. Now Corey, is it Bud Financial or just Bud or how should we, how should we name your, how should we call your company?

[Corey Horr: 03:19.6]
It's an often miscategorized name given the Bud name. But it is Bud Financial. But we do respond to Bud as well. So like whatever you prefer.

[Mike Lawson: 03:30.9]
Good to know, Good to know. So tell us a little bit about Bud before we get started because you guys are the fine host and we, and our guests from Akoya and Great Lakes as well. But give us a little bit of a, give us a quick 411 on budget.

[Corey Horr: 03:41.1]
Sure. Yeah. So Bud Financial, we specialize in transaction enrichment. So essentially ingesting all that data in cores or data warehouses or even from Keith at Akoya, pulling it in and turning that kind of messy transaction data into something that's actionable and driving a lot of different capabilities off the back of that, both for internal member segmentation and analytics as well as external member money management tools.

[Corey Horr: 04:11.3]
So savings, things like that.

[Mike Lawson: 04:13.3]
Right on, right on. And it sounds like you're working with the folks at Great Lakes as well.

[Keith Pfirrman: 04:17.3]
And partnered up with Akoya too, so

[Mike Lawson: 04:18.7]
This can be a good conversation. But before we get going to the conversation, just a couple of items here. So before we get started, we're going to have a Q&A at the very end here. We're hopefully going to leave a few minutes there at the very end for folks to chime in with any questions.

[Mike Lawson: 04:34.0]
Just throw those in the chat and we'll get to them. But yeah, so and lastly, if you, if anybody missed any of it or this entire webinar or a part of it, we're going to have a recording posted this afternoon of it as well. So you can go watch it on demand at your leisure and then obviously, obviously get in contact with any of our guests here today if you have any follow up questions. So.

[Mike Lawson: 04:56.3]
All right, with that said, let us, let's dive into our chat, shall we everybody? Let's dive into the deep end. Are you guys ready? Are you ready?

[Elizabeth Osborne: 05:07.0]
Ready.

[Mike Lawson: 05:07.8]
All right. Ready.

[Keith Pfirrman: 05:08.7]
All right.

[Mike Lawson: 05:09.1]
All right, Elizabeth, ladies First. All right, you're gonna be first up here. So when we talk about transaction data that credit unions have, what are we talking about here? What is this transaction data we're talking about here?

[Elizabeth Osborne: 05:22.2]
Yeah, and I think it's a great item to start with because I feel as if a lot of bank and credit union leaders don't always realize that we truly have the keys to the kingdom. Right? We have access to such a variety of different data points and information about our members or customers that give us so much insights into their behavior.

[Elizabeth Osborne: 05:45.9]
So think about, you know, your core system. So it's going to have your account information, member information, tenure, transaction histories, payments, et cetera, then tie in other things such as digital banking, when did they log in, where they logging in from, what kind of device do they have?

[Elizabeth Osborne: 06:02.5]
And then lending systems, if you're running a credit score in order to make a decision, things like that, the list is endless. And so it really is a great starting point.

[Mike Lawson: 06:14.3]
Fantastic. And then the next question is actually is for Elizabeth and Keith. Elizabeth, I'm going to stick with you for the first part of it. What transaction data? Or, or what can transaction data tell credit unions about their members? Because, I mean, I think this is like where you can see behaviors, consumer behaviors, all this type of stuff.

[Mike Lawson: 06:31.2]
I'm sure you guys know this backwards and forwards at Great Lakes.

[Elizabeth Osborne: 06:35.2]
Yeah, we are, you know, we're on a journey to use it more than we have through our data warehouse. And so areas that we're really focused on right now that give us better insights to develop better products and service offerings for members are things like what is the regular balance?

[Elizabeth Osborne: 06:52.9]
Is the member maintaining a regular positive balance in their account? What does their direct deposit history look like? Those are certain things that we're currently using to make decisions. When a member, let's say, is looking for like a small dollar loan; so we don't have to run a credit check, we can use our transaction history to make better decisions for those members and give them access to something that maybe they don't, they couldn't get from another financial institution.

[Elizabeth Osborne: 07:19.5]
And that's the kind of stuff that we should be using because we know our members, we have that data, we should use that to make those decisions.

[Mike Lawson: 07:27.1]
Exactly. That's the story that tells the whole story right there. Right. When you see that all that data like you referred to earlier. All right, Keith, same question to you, sir. So what can transaction data tell Credit Unions about their members?

[Keith Pfirrman: 07:38.7]
Yeah, I mean, I could say it from the perspective of the fintechs I work with every day I help them gain access to consumer financial data that really powers their use cases. And those use cases are all opportunities for banks and credit unions to kind of do that stuff themselves.

[Keith Pfirrman: 07:53.8]
As Elizabeth said in the first question, they kind of hold the keys. They have the data that all these fintech use cases are built on. And while you have relationships and check ins with your clients, the transactional data is a dynamic, constantly updating and evolving story of the consumer that allows you to provide so many opportunities, whether it be lending, budgeting, finance app kind of use cases.

[Keith Pfirrman: 08:19.5]
It's just all right there.

[Mike Lawson: 08:21.5]
Yeah, I heard a saying a couple of years ago and you guys have probably already heard this a million times, but it was new to me. Show me, you know me. And I think the transaction data really allows a credit union to know their member, obviously, because it's all there right in front of them. And then when you show them that you know them, then that leads to the trust and the loyalty that all credit unions are looking for.

[Mike Lawson: 08:42.4]
And I'm sure Elizabeth can attest to that. That's something else that really stands out for me when a credit union really leverages that transaction data, especially. You want to add anything there to Elizabeth. As far as the show, I just.

[Elizabeth Osborne: 08:56.4]
Wrote that down because I like that line, we should use that. Show me you know me. It's like show me the money, but show me you know me.

[Mike Lawson: 09:03.2]
Exactly, exactly, exactly. All right, so third question here and Corey, I'm going to start with you on this one. So what capabilities can credit unions deploy using this transaction data?

[Corey Horr: 09:16.7]
Yeah, the short answer is a lot. All right, good. Yeah, there you go. That's the whole entire show. But no, no, there's a ton of different capabilities, both internal and external member facing tools. But it all starts with that underlying raw transaction data.

[Corey Horr: 09:36.0]
And in itself sitting by itself, it is tough to leverage. Right? Because it's raw transaction data, it's messy, it's tough to understand what it really is. So it all starts with enriching that data. And what I mean by enrich, basically making sense of that data.

[Corey Horr: 09:53.5]
Whether it's understanding things like the merchant, the processor, the transaction type, or getting more advanced into things like location data, help understand fraud if something's outside of their area and then getting even more advanced and understanding regularity.

[Corey Horr: 10:11.0]
So being able to understand that this transaction takes place at the same time every single month.

[Mike Lawson: 10:16.6]
Right.

[Corey Horr: 10:17.3]
Once you start to understand that, you can start to build capabilities off the back of that, both internal and external. I'll start with internal.

[Mike Lawson: 10:24.3]
Sure.

[Corey Horr: 10:25.1]
As far as internal tools, advanced member segmentation and analytics, understanding things like characteristics of members. You just said, you know, show me, you know me well, what better way to show somebody you know them and understand that hey, this person is a renter, they have a recurring transaction that is rent.

[Corey Horr: 10:44.1]
So therefore we can position products that fit that renter, you know, more, more, more efficiently or better, you know, mortgage lending, it thinking about things like who has leftover money at the end of the month, do they have a savings account with us?

[Corey Horr: 11:00.7]
So you can start to build out all these different use cases both in lending, marketing, even risk, credit and risk, you know? With transaction data you can see people that are paying bills slower, not missing a payment that would show up on a credit report, but you can understand that they're paying it slower.

[Mike Lawson: 11:18.0]
Yeah.

[Corey Horr: 11:18.6]
Something that wouldn't show up on a credit report. So all these various use cases can all be done with transaction data. But it starts with the building blocks, companies like Akoya pulling in all that data, pulling it in from cores, data warehouses, enriching it with companies like Bud Financial and then building the capabilities off from it externally.

[Corey Horr: 11:37.0]
You know, the member facing tools, savings budgeting, we've seen those before. But when you start to pull in advanced things like regularity, you can start to really help that member manage their finances. For example, you know, I pay my mortgage every time on the first of the month.

[Corey Horr: 11:54.9]
I can now predict that on the 28th, am I going to have enough money in my account to cover my mortgage on the first. I'm big on story, so I'm going to tell a quick story.

[Mike Lawson: 12:05.7]
Go ahead. We love anecdotes.

[Corey Horr: 12:07.9]
When I grew up, my mom carried a checkbook, right? And she religiously would write out the check and then flip to the back of the register and write down the amount, her balance, what that transaction was, what it was due. In effect she was self categorizing all of her transactions.

[Corey Horr: 12:25.3]
She knew better than anybody what was going on in her bank account. When she received a check from her company, she would go into the bank, she would cash that check and they'd give her a deposit slip. She knew everything about her account. And then you look at today and we've tried to make everything so easy, right?

[Corey Horr: 12:42.2]
So automatic payments, direct deposits, I mean a lot of people don't even go into a branch anymore. So it makes things difficult to truly understand what's happening with your money. That's where you know, transaction enrichment and the likes of all these different capabilities and tools can help members to better manage their finances.

[Corey Horr: 13:02.4]
So sorry if I went a little over there but...

[Mike Lawson: 13:04.3]
No, no, I love it because your mom and my mom, kindred spirits because she would do the same thing. I remembered vividly. She would take me to the grocery store with her and I would remember her writing a check and then right after she wrote that check, put it in the register, boom. It was all everything she knew, the balance, all that type of stuff.

[Mike Lawson: 13:22.0]
So I totally can concur with that, that story. That's why we love anecdotes here. So Elizabeth, I always enjoy getting the credit union perspective of this because as Corey was talking, I'm thinking proactive, predictive, I mean you can tell if your members in trouble or not, that type of stuff.

[Mike Lawson: 13:39.5]
So give me share with us the credit union perspective on this because I find this super fascinating.

[Elizabeth Osborne: 13:45.5]
I loved Corey's points and they're, they're so accurate because it's information that we have already, but are we using it to its full potential? And so some things that we're looking at for instance is I would love to be able to set better limits for members based on their risk profiles.

[Elizabeth Osborne: 14:04.9]
So if we know they maintain a positive balance, we know they have a regular direct deposit, they, you know, pay their loan every time except a variety of different factors. Why couldn't we give them a higher overdraft limit for those situations where maybe they need access to that more like that value added benefits for that point where maybe they ran into a situation that's uncommon or why couldn't we use that to give them higher mobile deposit capabilities, ATM deposit capabilities, really improve it.

[Elizabeth Osborne: 14:37.1]
And then on the flip side, you know, for those members that are running into situations, maybe they're running into more financial distress using that information to trigger alerts to the credit union or certain individuals to maybe we reach out to them, right?

[Elizabeth Osborne: 14:54.0]
To kind of offer them some assistance or guidance because that's what we're here for. We should be doing that. And then also maybe it's to talk to them about some short term loans like I mentioned earlier, using that transactional data to make a better decision for them, but to offer it to them without hitting their credit score and going back to we know, you show me, you know me by doing that. I mean it's great.

[Elizabeth Osborne: 15:19.1]
And so yeah, you hear about segmentation, personalized marketing, personalized reach out, personalized product offerings that are really specific to the grouping of who you're reaching out to. You know, at Great Lakes Credit Union we have close to 100,000 members.

[Elizabeth Osborne: 15:37.1]
About a little more than half are low income and the other half aren't low income. So their needs really differ. And so we're trying to figure out a way, how do we best go about kind of segmenting so our messaging is more applicable to their needs at that point and also where they're at in their financial journey.

[Elizabeth Osborne: 15:56.4]
Are they renters that may want a mortgage at some point or maybe they're getting ready for retirement or whatever the case. And so that's where that's.... Using transactional data, I think the list is endless and it's a focus we have that we'll continue to leverage as well.

[Mike Lawson: 16:11.8]
Yeah, good for you guys.

[Keith Pfirrman: 16:12.7]
Yeah.

[Mike Lawson: 16:12.8]
And doesn't the, Isn't the. When you guys do this, isn't the trust factor just off the charts? Because I mean you're. Because it just proves that you're there for them, right?

[Elizabeth Osborne: 16:21.5]
Completely. I mean Chime is doing it. Yeah, they're. Chime, Corey, you brought up like if you have a little extra in your account each month, why aren't we sending an email to those members automatically to say, hey, we notice you have an additional balance of 200 every month. Did you know you can make money on your money by opening this account? Right.

[Elizabeth Osborne: 16:39.4]
So that's like getting there. It's just hard to, it's hard to find the right solution and partner and then also to actually prioritize it and make it happen. But we'll get there.

[Mike Lawson: 16:50.0]
That's a nice little value add for sure. Sure. Go ahead.

[Keith Pfirrman: 16:53.4]
On the show me, you know, me theme here is, you know, the ability to kind of see, you know, not just the, your client's financial situation as it pertains to your relationship with them and at your institution, but also you. What's going on elsewhere. I had a conversation with my 401k provider and I thought it was so interesting that they had no way of knowing or didn't give me the opportunity to share that, you know, I have a mortgage at this bank.

[Keith Pfirrman: 17:17.2]
I have, you know, my pay stubs coming in at this bank. They only know me with regards to, you know, how am I using this institution. But it doesn't show the entire footprint of my, you know, my financial profile. It's obviously a huge opportunity not just for, you know, the in-house transactional data, but also to pull in data everywhere where the consumer is willing to share it.

[Mike Lawson: 17:37.9]
Like a one stop comprehensive view view of your financial life, basically. Is that what you're referring to, Keith?

[Keith Pfirrman: 17:44.2]
Yeah. And I think it's something that fintechs kind of offer. It's the biggest thing, seeing all your financial information in a centralized place. There's no reason institutions can't also incorporate this very quickly.

[Mike Lawson: 17:56.3]
I couldn't agree more. Couldn't agree more. And Elizabeth and Corey, this next one's for you guys as well. What has the adoption been like with credit unions for PFM capabilities or customer analytics? So Elizabeth, you want to go first on this one? I see you nodding your head. You look anxious to answer this one.

[Elizabeth Osborne: 18:12.9]
It's, so I can speak to Great Lakes. You know, I'm not as close with other institutions and what their adoption has been like. I do see a lot of credit unions have partnered with third parties to offer this solution. I think for us. So PFM just to define it, personal financial management.

[Elizabeth Osborne: 18:30.7]
And so that's really for us, member adoption has been kind of static. You know, we offered it when we launched a digital banking upgrade years ago. We have members that use it, those that use it, use it consistently. But it's not as widely used as you would think it is.

[Elizabeth Osborne: 18:47.5]
So I don't. It makes me question, is it the solution we have? Is it the way we market it? Is it where it's placed in the app or. There's a lot of things we need to dig into. But you know, it's in my more banking experience we really focused on that and trying to push members to it.

[Elizabeth Osborne: 19:03.7]
So I think we, we should do it. It's out there, just not as used as I would think it would be. I use it myself.

[Mike Lawson: 19:09.6]
Yeah, I do too. I'm all over mine. I mean that just gives me a good look of where. How bad I'm doing or how good I'm doing, you know, so it really keeps me on top of, keeps me on my toes, if you will. So yeah, so it's a good, that's always good to stay on top of your finances in that way. So Corey, I want to get your perspective as well.

[Corey Horr: 19:26.3]
Yeah, yeah, happy to. You know, we're based out of the UK London, so open banking has been around since 2017. The market in the UK is very much more mature than the US in terms of sharing transaction data, leveraging transaction data.

[Corey Horr: 19:44.7]
So, you know, in terms of how has the adoption been in the U.S. it has been slower. However, I know we're going to talk about this with the introduction of 1033, the upcoming legislation, I do believe that we're going to see the US start adopting open banking and transaction data.

[Corey Horr: 20:00.7]
Much more aggressively. And you know, I think neobanks have told us quite a bit. Elizabeth, you mentioned Chime, Revolut in the UK is another example of some neobank that has really taken the market share from a lot of the UK banks that have done these PFM tools and capabilities very, very well.

[Corey Horr: 20:22.1]
So, you know, in terms of what has adoption been like, Elizabeth, you called out that, you know, PFM tools haven't been widely adopted by your member base. And I think that's something that's, you know, common with financial institutions in the US.

[Corey Horr: 20:37.7]
I do question, you know, how proactive those nudges and notifications are to those groups that could influence them to log in more. Certainly like a left to spend at any given point in the month would be a way to drive engagement.

[Corey Horr: 20:54.7]
But yeah, it's a common problem that we hear as well, driving adoption of the PFM tools in the US.

[Mike Lawson: 21:01.9]
We'll get to AI a little bit later, but just popped in my head, isn't this somewhere where AI could really be used, especially in this area? So I think it could be really leveraged effectively, especially in the PFM areas: sending reminders, keep you on track, that type of stuff.

[Mike Lawson: 21:17.5]
So, and being a credit union, oh my gosh, that would just be, that would be huge to get these alerts and stuff. So I don't know, just, just, just pointing it out. So, Keith, anything else to add from your perspective on this one?

[Keith Pfirrman: 21:30.1]
No, I think I pretty much covered it. I think, yeah, there's lots of opportunities there with regards to offering the same services internally at these, at these credit unions.

[Mike Lawson: 21:39.1]
Well, what could help speed up deployment in this area? What do you think, Corey? What could, or what has helped speed up deployment?

[Corey Horr: 21:48.7]
Yeah, I think obviously 1033, the requirement of doing this is going to speed it up drastically. But in terms of from our point of view, what can speed up deployment? It's really open communication with the client, understanding where the data resides, what is the fastest way to obtain that data, whether that's through a core, whether that is through some data warehouse, an aggregator like Akoya, and then mapping out exactly the plan of getting the data, receiving it and sending it back to whatever channel it needs to be sent to.

[Corey Horr: 22:22.6]
And obviously the open communication can drastically reduce deployment times.

[Mike Lawson: 22:28.1]
Indeed. And Elizabeth, same question to you. What could or what might help speed up deployment in this area?

[Elizabeth Osborne: 22:34.2]
Yeah, because today it's really disjointed. You know, it's kind of everywhere. If you look at it from a consumer perspective, I think the average consumer in the US today has 14 financial services apps on their phone. That's a lot of apps. Okay. So data is everywhere. Yeah, that's a lot.

[Elizabeth Osborne: 22:50.4]
So it kind of brings to question what would the impact be once open banking? Because it's coming. All right. I can't believe it's not here yet. It's coming. What that impact will be, it's very beneficial, but it's also very scary for a financial institution because it will make it a lot easier.

[Elizabeth Osborne: 23:06.4]
So they said we'll see. It's not, I think it hasn't proved out necessarily in the UK but for members to switch financial institutions. But something I think is important to, I was thinking about it over the weekend is there's like this race between open banking and DeFi.

[Elizabeth Osborne: 23:22.2]
Decentralized finance kind of takes it out of there. Right. Because you're removing the need for intermediaries. You're that financial institution no longer is really the, the kind of step that has to be taken. So it, it streamlines the entire process. So I think it's going to be a race between the two.

[Elizabeth Osborne: 23:38.3]
Who's going to hit first, where, which service or solutions that are out there that use that technology, whether it be through open banking or through DeFi that will kind of take the market faster, will be a big driver in this.

[Mike Lawson: 23:52.4]
Do you guys have an opinion on that? Who's going to get here first? Should we put any wagers?

[Corey Horr: 23:58.9]
No.

[Mike Lawson: 24:00.8]
If you look like you're ready to throw down five bucks on this one.

[Keith Pfirrman: 24:04.0]
I, I bet DeFi doesn't come in fast enough to motivate people to move. I think people are, there's a lot of inertia at play there. You know, people stick with their relationships and I don't know if they'll be able to move as quickly as they'll need to get on the DeFi, but we'll see.

[Mike Lawson: 24:18.3]
Okay, well, I think that's another webinar for another time. Hopefully soon because yeah, like Elizabeth said, this is right on the horizon and surprised that it's not here already. So Keith, you're up on this next one here. So how should credit unions, speaking of a post-1033 environment.

[Mike Lawson: 24:36.6]
And how will that impact approaches to transaction data?

[Keith Pfirrman: 24:40.7]
Yeah, I mean, as Corey was saying earlier, it's kind of the world that everybody's been living in over in the EU and the UK it's open banking coming to the US and you know, the rule is getting finalized in the number of days and then, you know, the largest the first bracket will have, you know, a few months to comply, followed by the next one.

[Keith Pfirrman: 24:57.0]
And then the smallest banks and credit unions will have a few years to get in in line and adhere to the rule. And what will happen, what will come from that was, is just that the, you know, the banks, the credit unions will have an obligation to make the data accessible through APIs on, you know, in the network.

[Keith Pfirrman: 25:13.6]
And it's not just that, but it's also, you know, being able to entertain, you know, incoming requests for API connections. So you're either kind of have to facilitate that inbound volume of request or go through an intermediary that kind of helps with that. And you know, it's really going to be the democratization of the data.

[Keith Pfirrman: 25:28.9]
The consumer kind of expecting more from the credit union or the financial institution. As far as, you know, does my bank connect to the apps that I use, you know, am I interconnected, you know, everywhere I want to be? And you know, as the banks and credit unions kind of adhere to the rule, they also stand to benefit from it as well.

[Keith Pfirrman: 25:48.0]
You know, there's the ability to kind of get on this network as well, pull in that information on the consumer wherever they permission it. So you're not only seeing, I think I was saying this earlier, you know, not just seeing the footprint with regards to what's happening at your institution with that customer, but know what's happening everywhere, whether through, you know, apps or other banking relationships.

[Keith Pfirrman: 26:06.6]
So it's going to be, the transaction data will be, you know, to the advantage of, you know, anyone that the consumer decides to permission to share it to, you know, bank, credit unions and banks should try to be part of that.

[Mike Lawson: 26:18.0]
Oh, no doubt, no doubt. Elizabeth, question out of total blue. But where is this on your radar? I mean, is it pretty high up as far as.

[Elizabeth Osborne: 26:24.7]
Yeah, I mean it's. So we are, we are looking into it. It's a lot to take in. You know, I think the, the purpose of 1033, I understand why the government is trying to enforce this, but the how is really challenging because enforcing this kind of level of regulation on older technology, you know, our core systems have all been, were built like around the time I was born.

[Mike Lawson: 26:52.7]
Ain't that the truth?

[Elizabeth Osborne: 26:53.7]
Yeah, I mean they're old, you know, and so they're, I mean we have great partners, they are updating it and so forth, but it's really hard to adopt. And so the technical standards I think are a big, big piece of this that's going to make it really challenging. I tend to lean more towards the DeFi route.

[Elizabeth Osborne: 27:09.4]
You know, Bank Social, big partner of ours, we're looking at that because I think that's going to get us there faster and simpler and easier because the onus is going to be on financial institutions and it's really hard to make it all happen with partners. API connection is our biggest hurdle today.

[Elizabeth Osborne: 27:26.1]
When we try to integrate different systems, different levels of information and try to make it all sense, we tend to be that middleman. And so we've partnered with, you know, organization/solutions that kind of do APIs as a managed service to help connect us to the cores and connect to financial services.

[Elizabeth Osborne: 27:44.1]
But it's just, it's clunky, it's difficult, there's got to be a better way. So I'm hoping DeFi is the way. But 1033 just kind of adds another layer of complexity to it.

[Mike Lawson: 27:54.8]
Oh boy. Yeah. And I've heard time and time again a lot of these legacy cores are one of the biggest hurdles in getting to that next step like you just addressed. I mean there are great, great technologies out there. Don't start shooting me hate emails here. There's some awesome, awesome cores out there, but there are other cores out there that have been there like, like you said, Elizabeth, since we were all born. So.

[Mike Lawson: 28:14.8]
Yeah, but, yeah, but fabulous ones out there as well. So, all right, let's go move on to the next question. Let's not go down the core processing rabbit hole too far. Let's go to the next question here. And this one's for you, Corey. So what are the benefits for customers and credit unions in this area?

[Mike Lawson: 28:29.8]
What are you seeing?

[Corey Horr: 28:31.2]
Yeah, yeah, you know me by now. I mean, I'm going to start with a story. My wife and I, we sold our home in Texas in 2022 just outside of Austin, deposited the money. My wife wanted to get out of the cold weather in Texas (that's a real thing) and move to Tampa, Florida where it was obviously warmer.

[Corey Horr: 28:51.5]
But anyways, she wanted to buy a home right away. I said let's rent, figure out the neighborhoods. So we parked the money anyways and I looked for an area to invest the money from the house proceeds, the home sale proceeds. And I was expecting a phone call from my financial institution because it was probably, I don't know, probably 10, 15 times more than my average balance sitting in there.

[Corey Horr: 29:16.4]
And never once did I hear. So I had to research another financial institution, ended up moving my money outside of the place I used to bank.

[Mike Lawson: 29:24.9]
Oh my gosh.

[Corey Horr: 29:25.9]
Altogether. So it seemed like a missed opportunity. So to start out with the benefit to the credit union, being able to understand what's happening with money moving around from your members is vital, especially in this landscape where I hear time and time again: deposits, deposits, deposits; we want more deposits.

[Corey Horr: 29:46.4]
Certainly you should understand when big deposits come into your institution and actively engage with that member to try and keep it in the institution. So that's one way transaction data allows you a lens into the member, to really understanding what's going on with their day to day.

[Corey Horr: 30:05.5]
If they're struggling financially, like Elizabeth said, you know, if they're living paycheck to paycheck and maybe a short term capital infusion would really help them out and get them out of that cycle, that's one way to do it. For the members: You know, certainly being able to manage your finances is the number one thing.

[Corey Horr: 30:26.4]
Knowing that your financial institution is watching for, you know, issues that you're having and proactively reaching out to you and delivering, I've been coining this term, this Amazon-like experience where, you know, they're, they're seeing the things happening and actively engaging through AI.

[Corey Horr: 30:48.7]
I mean those are some of the primary benefits that I see from, from, from transaction enrichment.

[Mike Lawson: 30:54.0]
Okay. The look on Elizabeth's face when you said that my financial institution didn't even alert me or offer, have any offerings was priceless. What were your first thoughts when you heard that, Elizabeth? Because.

[Elizabeth Osborne: 31:09.7]
Like, how many? How many? I hope, I hope we don't have a lot of members that have that same experience with us because we're not really using we, I hope we are, but we're probably not because we don't have those triggers. And that is scary because that's where you lose trust in your financial institution.

[Elizabeth Osborne: 31:25.7]
And trust is key. They're managing your finances. And so once you lose that, it's, you won't get it back. Yeah, so that's, it was a little scared. I was scared. Gotta fix that.

[Mike Lawson: 31:39.3]
No, because I can, I can attest to that because my, because I bank at different financial institutions because it's kind of my job to see what's out there, credit unions and banks. And so one of my financial institutions, actually, if I have a sizable deposit that's more than normal, they'll have an alert on my phone.

[Mike Lawson: 31:55.2]
They'll send it to me and go, hey, we saw. Do you want to put this in a CD? Do you want to do this with it? Do you want to do that? I'm like, whoa, that's pretty cool. So, yeah, so I'm like, okay, is this across all the boards? So with you saying that, I'm like, oh, this is kind of a little discovery for me. It's like, oh my God, it's not everywhere.

[Mike Lawson: 32:11.9]
So, wow, that's really powerful. Really powerful. Okay, now we talked a little bit about AI. I mentioned AI a little bit earlier. And Corey, you may have another story for us. I hope so, because they're bringing up some great conversations here. So does AI play a role in this capability?

[Corey Horr: 32:31.9]
Yes, the answer is yes.

[Corey Horr: 32:37.5]
Think about how many transactions you, you run through your debit card in an average day or your credit card even. You know, combine the two. For me, it's like five to 10 transactions. I can consistently predict that I'm going to have. Yeah, you spread that out over a week.

[Corey Horr: 32:53.0]
I'm not going to do the math. I'm terrible at math. But anyways, you start looking at your entire member base. Think about how many transactions are coming through on a weekly basis. I mean, it's an astronomical number. You physically cannot manually categorize every single transaction.

[Corey Horr: 33:09.3]
You can't expect your members to. We know from previously we're terrible at that. So you need to rely on AI to really enrich transactions at scale - with guardrails, of course, being able to test for accuracy and just simply making predictions on what that transaction really is.

[Corey Horr: 33:30.4]
That's the first component. Then obviously you can start layering in machine learning and really getting these models to be learning consistently and getting better and better and better at predicting what that transaction is. Technology today, you can even have it individualized for your member.

[Corey Horr: 33:48.4]
So if they alter a transaction saying, no, this is actually a grocery store. Locally, every single time that transaction comes up for that member, it will always be labeled as a grocery store. You can train these models to be much more efficient. Then you start incorporating this new term called large language models, the chat GPTs, the Copilots , the Bards of the world.

[Corey Horr: 34:14.1]
Now you're starting to add in this conversational AI element where the chatbots of yesterday at a financial institution kind of tell you, hey, if I lost my debit card, call this phone number. That's possible, obviously with large language models, but then you can start bringing in insights.

[Corey Horr: 34:33.5]
So you can now communicate and say, how much did I spend on coffee over the past two months? And here, that Starbucks number, that's way too high. For everybody. But it now starts layering in that additional intelligence to help members manage their finances even more.

[Corey Horr: 34:52.0]
You can turn that internally as well. So it could be an internal facing language model to help figure out analytics, customer segmentation. How many of my members are living paycheck to paycheck, having those tools tell you exactly what it is.

[Corey Horr: 35:07.3]
Turning a project that might have taken a month for a data analytics department into seconds. And then obviously the benefits for the members. A lot that AI is doing nowadays. There is a gray area of AI that we're starting to see formulate.

[Corey Horr: 35:25.2]
Where do we want AI actually making offers, product offers. For example, if somebody says to a large language model, how much do I qualify for a home loan? Having it actually do the calculations, coming up with the offer and submitting the offer.

[Corey Horr: 35:42.4]
And I think that's where a lot of people are raising their eyebrows and there's a lot of hesitancy. So it'll be interesting to see five, 10 years from now how involved AI will be in the decision making process and offering process.

[Mike Lawson: 35:55.9]
I think it's going to be 365 days, Corey. That's how fast this stuff is moving. Elizabeth, I have one word for you. It's Olive. Can you share with us, Olive? Because this is exactly what Corey was talking about. You guys have it? You guys have it.

[Mike Lawson: 36:11.3]
You want to share with us real quick?

[Elizabeth Osborne: 36:12.9]
Absolutely, yes. Olive is our conversational virtual AI agents. So anytime a member calls Great Lakes Credit Union, they first start by having a conversation with Olive. And so Olive really addressed one of several needs we have.

[Elizabeth Osborne: 36:28.3]
But the most critical at the time was on the member service support. And so our call center was just struggling with call volumes. We had a hard time retaining the right people. And it's a tough job like call center agents, no joke, that's probably the toughest job anyone's going to have.

[Elizabeth Osborne: 36:44.1]
And so we were hoping to take away some of that more mundane transactional type questions that they were receiving from members and letting a bot an agent do that. And so Olive does that for us today. She now speaks Spanish. She's great.

[Elizabeth Osborne: 37:00.6]
But that's like, that's one piece of it. But then what we don't have, we have the guardrails. Corey, I love you said guardrails. That's so key for AI that you have those appropriate guardrails, so you can really try to manage the output as much as possible because it is interactive for us.

[Elizabeth Osborne: 37:16.9]
But what we're not doing yet is we're not using AI to make decisions in which it's automated. Right? So an automated decision which would then result in an action like a lending decision, for example. And so that is a big question, you know, I see.

[Elizabeth Osborne: 37:34.5]
I think soon we're going to see AI is really is your new personal finance manager and that with open banking, all of this coming into play, scanning the environment to say, "hey, there's this great offer from here, click here to move your money" And so I think it'll slowly evolve into that.

[Elizabeth Osborne: 37:56.7]
It's going to take time, but that's where it's going. You know, there are hurdles. Financial institutions are nervous about it because a lot of it's unknown and there is, there is risk with it. And so making sure you find the right partner and go about it the appropriate way is it's, there's a lot of work and strategy involved.

[Mike Lawson: 38:15.7]
No doubt, no doubt. Keith, do you have. I mean, everybody has an opinion on AI and I would love to get to Akoya's perspective on AI and where it's headed.

[Keith Pfirrman: 38:23.2]
Yeah, I mean, it's just we're seeing so many amazing things happen with AI in the financial services space and we've touched upon it already, but just the enrichment or the added color you're able to put onto the data and what AI allows you to do there with Bud and with other aspects of that, it's pretty incredible.

[Keith Pfirrman: 38:39.4]
So kind of adding that color onto the data to actually be able to give more insights. And then on top of that, the biggest applications and things I'm most excited about is a kind of like identity verification and fraud detection kind of one pocket and that's all being really, really kind of taking advantage of the AI thing as well as kind of the advisory stuff, kind of the input based on your profile and how it stacks up into others.

[Keith Pfirrman: 39:05.2]
So there's a lot. And I think that, not to toot our own horn a little bit or kind of involve ourselves in this, I think AI tools are only as powerful as the data you put in them. So putting clean solid data in from the start, the confidence that you're, you know, you're getting something valuable and incredible out of it.

[Mike Lawson: 39:26.9]
And this stuff is going to use that transactional data that we were talking about earlier. So to kind of even predict or again show that credit unions know their member. So I mean, there's so many areas we could go down this, this, this topic for sure. So yes.

[Mike Lawson: 39:43.9]
And I've heard that AI is actually going to be like you're going to have your own digital personal assistant with you all the time, kind of like carrying around your phone everywhere. You're going to have this little digital assistant with you all the time and finance is just going to be one aspect of it.

[Mike Lawson: 40:01.3]
So credit unions, better get your ducks in a row now. So, because you want to be a part of that little revolution that's going to be coming here down the turnpike pretty quickly. That said, Elizabeth, should credit unions be intimidated by AI? Because you guys are already going down, you guys are already down that road.

[Elizabeth Osborne: 40:19.1]
Yeah, they shouldn't be, but a lot are. And it's because, you know, it's, it's kind of daunting. There's so many solutions out there, so many partners and trying to figure out what's the, like, what's the right use case. So I always tell other credit unions and banks, I'm like, think about what are your needs right now?

[Elizabeth Osborne: 40:37.9]
What are your worries? Do you want to go, what's your strategy, what's your vision? And then kind of segment it into, okay, what's the top priority and where does AI fit? And then once you have that kind of segment, create some

[Corey Horr: 40:51.2]
use cases and figure out who the best resource is.

[Elizabeth Osborne: 40:55.9]
But make sure you're finding someone that's going to stick around for a while and it's going to keep their technology up to date. I think that's a big risk that you just want to make sure whoever your partner is, is.

[Mike Lawson: 41:20.1]
Yeah, so I wanted to see if Corey or Keith wanted to chime in on the intimidation of AI or the possible intimidation of AI. Corey, you want to address that real quick?

[Corey Horr: 41:29.1]
Yeah, I mean, I think it's healthy to be intimidated, you know, until you start learning and understanding, you know, the benefits. And I think it's also important for, you know, you to understand who your partner is vetting, you know, the partner efficiently and. Yeah, that's probably what I would add. Keith.

[Mike Lawson: 41:46.5]
Okay, Keith.

[Keith Pfirrman: 41:47.6]
I would just echo that. I think there's also a little bit of a sentiment with AI or non person input or advisory. I think that, you know, you're trained to dismiss it based on the previous generations. So as you bring in something new, it's really trying to distinguish how unique and nuanced it could be versus what's been there before.

[Keith Pfirrman: 42:07.6]
Everybody remembers the little paperclip that pops up when you're trying to type a document. I think people sometimes associate AI services or chatbots with that, whereas the new stuff is obviously much more credible and useful. But I think that's some of the associations people have.

[Mike Lawson: 42:24.4]
Wasn't that Bob, or something like that? Microsoft Bob or something. Listen.

[Mike Lawson: 42:33.0]
Oh, good times. All right, hey, last question here for you, Corey. How can credit unions give or drive genuine ROI? And what does ROI look like?

[Corey Horr: 42:41.6]
Yeah, great question. So going back to the two different use cases, internal member segmentation and analytics, and then external on the internal side. I mean, there's just a wealth of different use cases, to Elizabeth's point, that you can target, whether that's growing mortgage lending, whether that's finding deposits, whether that's mitigating risk, just a lot of different angles that you can go.

[Corey Horr: 43:12.5]
And each one has its own form of ROI attached to it. So it's really identifying what your specific goal is and then using the analytics to support that or drive that. But with transaction enrichment and being able to comb through a million plus members in a second and identify the buckets of members that would be most impactful for each campaign, it's extremely valuable.

[Corey Horr: 43:39.1]
And it's also a great way to kind of pre-vet campaigns because oftentimes I'll hear we're going to go out and do this massive campaign. We've tasked our data analytics team to identify everybody and then the results come back after two weeks or month and there's only 55 people in the segment.

[Corey Horr: 43:58.4]
It's like all that time was wasted. If you could do that instantly, you could have saved a lot of time and resources on the internal side. And then on the external side, you know, it's no secret that when people are managing their finances better, they tend to save more money.

[Corey Horr: 44:14.4]
Saving more money leads to more deposits and also it also helps people improve their credit scores so it makes them more qualified for the other types of products you offer. So that's some examples.

[Mike Lawson: 44:27.5]
And again, that's where the transaction data comes in for sure. And Elizabeth, I want to get the credit union perspective on this question as well, about what does ROI look like in this area for you guys?

[Elizabeth Osborne: 44:38.5]
Yeah, so our Olive story was such clear ROI because of the automation with it. It addressed a member need, but also time savings. So then we're able to dedicate a higher, more specialized resource to supporting our members and it turns into more of like a consultative discussion instead of like that.

[Elizabeth Osborne: 45:02.7]
What is my balance? What are my last five transactions? And so you pay a lot less per minute for a service, transactional service like that, using AI than you would through persons. That way they're, they're able to spend time where they need to on the support and hopefully drive, you know, maybe a goal you could set is increasing your products per member or, you know, looking at really engaging their relationships, pulling some of those like direct deposits from other institutions and so forth.

[Elizabeth Osborne: 45:30.2]
So it's a really easy case to make if you just do a business case and figure it out. Yeah, never had a challenge.

[Mike Lawson: 45:37.4]
That's awesome. All right, guys. All right, come to the last closing question here and before we get to the last question for everybody else here on the panel, if you guys out there, any guests, any, any attendees have any questions, throw them in the chat as well. But anything else to add on this topic here?

[Mike Lawson: 45:53.0]
I know we could go into like a hundred different ways on this topic of the definitely the importance of the daily transaction data here that's so, so vital to so many areas of the credit union. But Corey, anything else to add from Bud's perspective here?

[Corey Horr: 46:09.1]
You know, I think the last thing I'll add is it doesn't need to be as daunting as people think it is. It's fairly easy on our side and other fintech providers to be able to leverage the transaction data, get it out of wherever it lives.

[Corey Horr: 46:25.8]
I understand it lives many different places at credit unions. So we're trained at this, We've been doing this a long time. So I don't want people to be thinking it's too big of a project to take on because it is fairly simple and in the minds of us that live and breathe this every single day.

[Mike Lawson: 46:43.3]
Keith, anything. Anything from Akoya's perspective?

[Keith Pfirrman: 46:46.6]
Yeah, just to echo the ROI bits that were said earlier in the last think that with the transactional data, you're putting yourselves in the position to be more informed about the customer and then it's going to make you more engaged with them.

[Keith Pfirrman: 47:01.9]
It's going to really create an opportunity to do more with each customer. And I think that's really kind of speaks for itself and really breeds the value of the data.

[Mike Lawson: 47:13.0]
Yep, indeed. Elizabeth, anything else here before we get to a couple of Q&A questions?

[Elizabeth Osborne: 47:17.9]
Yeah, there's just such a tie in between transactional data that financial institutions already have and AI. So figure out, how do you connect those two. Build a use case, treat it like an experiment, just try it with a subset, see what the results are like.

[Elizabeth Osborne: 47:34.4]
Fine tune it, but tie it to your Strategy and do SWOT analysis that might kind of help you. So it's not as daunting. Like Corey said, really drill into what's your biggest need, where are you going to see the best ROI and then take it from there.

[Mike Lawson: 47:46.7]
Okay, sounds very logical. And you guys are proof in the pudding that it's working really, really well. Speaking of which, we have a question in the chat here from David O'Dell. Elizabeth, did you build all of yourself or use a vendor?

[Mike Lawson: 48:02.1]
Can you expand on that?

[Elizabeth Osborne: 48:03.5]
Yeah, we did not build it ourselves. We used a vendor. We partnered with Interface AI. I vetted a lot of different solutions out there. Best advice I have for you is do not over customize. So we started by keeping it simple and using more of their what I consider almost like an out of the box, so already predefined solutions, they integrate with our core.

[Elizabeth Osborne: 48:29.6]
They are integrating with our digital banking provider and then using that data and what they'd already done with other institutions, institutions, we just replicated that and were able to launch really quickly. We saw much faster positive results because of that. So start, keep it simple to start.

[Mike Lawson: 48:46.3]
As with most things, you're starting out. Yeah, keep it simple for sure. And then it just grows from there. And like everything like especially this type of stuff we're talking about, it's only going to get better as time goes on, as you guys probably at Great Lakes are experiencing right now, Elizabeth. So yeah, a question that I had before I let you guys go is driving member engagement.

[Mike Lawson: 49:07.0]
How does it keep the member engaged here? So Corey, I mean how have you guys, you talking to financial institutions all over the globe here? How have you seen it, this type of transaction data, keeping the member engaged? Can we see how it keeps a financial institution engaged?

[Mike Lawson: 49:22.6]
But how does it keep the member engaged?

[Corey Horr: 49:25.8]
Yeah, yeah, I talked a little bit about it. You know, with the transaction enrichment and the insights that are derived out of those, you can now send notifications, nudges.

[Mike Lawson: 49:36.8]
That's right. That's right.

[Corey Horr: 49:38.8]
To those members to keep them engaged. But there's a lot of choices out there for members nowadays. Right. There's the Neo banks, challenger banks, high yield savings accounts being offered by non-banks, plenty of different options. I think it differentiates credit unions to be able to offer these types of tools and obviously the members are going to leverage them if they're available and they're being, you know, nudged and notified for those various transactions.

[Mike Lawson: 50:10.7]
Okay, good deal. You want to ask that answer that one real quick.

[Elizabeth Osborne: 50:31.9]
I think personalized, more conversational banking is really where AI would have such a great opportunity for that. And so something that we're trying to, anything we can do to kind of make that trust and like we know them that show me, you know me. That's, that's the focus.

[Mike Lawson: 50:50.3]
I think we're going to have to retitle the webinar. Show me, you know me.

[Elizabeth Osborne: 50:53.2]
I know.

[Mike Lawson: 50:53.8]
With data transactions. So with transaction data. Oh my gosh. Keith. Keith, we have a question for you. I hope I'm pronouncing your last name right. With data aggregation, do you have any tips on best practices to maximize consumer adoption?

[Keith Pfirrman: 51:12.0]
Yeah, I mean it's just making sure that the aggregator you choose is creating a network that's connecting to the end applications and other places that you're customers expect their data to be let too. So it's all about access.

[Keith Pfirrman: 51:27.8]
Is it a true network? Is the network consistent and trusted? Are the access points monitored but also not overly stringent? So that's really the name of the game. That's how we try to exist in the space. So we're trying to make it as streamlined as possible and make sure that there's no knock on disadvantages that should be considered.

[Keith Pfirrman: 51:50.6]
You know, it's not only screen scraping but it's, you know, who's looking at the data. Is the data being stored? Is a data lake getting created in the process of facilitating the transfer? You know, all things to consider and the fewer intermediaries the better for a lot of reasons but you know, then you have more control over where the data is being accessed and it's going to precise.

[Mike Lawson: 52:10.1]
Can you guys imagine the conversation we're going to be having a year from now on this talk topic? It might be totally different. So this is so exciting times. Oh my gosh. Alrighty guys. Well I want to leave plenty of time for folks to get to their, their next Zoom meeting or their next meeting or to lunch or whatever.

[Mike Lawson: 52:27.1]
So we always leave a few minutes as a buffer for folks to get to their next task. But I just want to say thank you to everybody for attending today's webinar. Some great takeaways for sure, some great insights with these three experts on our panel today and a huge thank you to Elizabeth Osborne again from Great Lakes Credit Union.

[Mike Lawson: 52:47.1]
Thank you, Elizabeth. Appreciate your time as always. And then also, Keith Pfirrman from Akoya. Thank you very much, Keith. Appreciate your time as well, coming to us on the other side of the country in Boston. And then Corey Horr from Bud as well, who is actually hosting the webinar as well.

[Mike Lawson: 53:03.1]
Thank you, guys. All three of you knocked it out of the park. And appreciate, appreciate your time. And again, your expertise today. Lots and lots of takeaways. And again, if anybody missed any of the webinar or missed the whole thing, we will have a recording posted a little bit later today for you guys to watch at your leisure.

[Mike Lawson: 53:21.2]
And on that note, thank you so much for hanging out with us for a few minutes and enjoy the rest of your day.