How AI Can Help Credit Unions Grow Smarter, Not Harder

Unlocking Credit Union Growth in the Age of AI

What does it take for credit unions to not just survive—but thrive—in today’s rapidly evolving financial landscape? According to Mitch Rutledge of Vertice AI and Fred Eisel of Vizo Financial, the answer lies in embracing artificial intelligence and data analytics. In their conversation with Doug English, they outline how AI is helping credit unions overcome staffing limitations, uncover usable findings from messy data, and re-establish relevance with modern members. Whether you’re part of a small credit union with limited bandwidth or a large institution navigating industry-wide disruption, this episode breaks down how data-driven growth is more accessible than you might think.

From Data Overload to Actionable Insights

Credit unions have plenty of data, but many don’t know how to use it. According to Fred Eisel, Vizo Financial surveyed 700 credit unions and discovered a clear pattern: nearly 40% were focused on one thing—growth. But lacking data scientists or internal infrastructure, many were stuck. Mitch Rutledge’s company, Vertice AI, was built for this challenge. Their platform uses a combination of traditional machine learning and modern generative AI to help credit unions “know, grow, and measure every member”—without requiring perfect data hygiene or an internal analytics team. “Data is a journey, not a destination,” Mitch explains. “If we wait for perfect data, we’ll never start. But if we work with what we’ve got, we can start learning and growing today.”

Solving the Right Problems First

What sets Vertice apart is its laser focus on helping credit unions grow their memberships and deepen relationships. Instead of trying to boil the ocean with complex tech integrations, they target a single, high-impact issue: member growth. This focused approach yields fast results. Mitch shared examples where clients achieved 2–6x higher conversion rates by replacing blanket campaigns with targeted, AI-driven member outreach. Fred echoed the value of this approach. “This platform is solving a small but critical problem at a price point that even smaller credit unions can afford. You don’t need a massive tech stack—just the willingness to get started.”

Why Personalized Engagement Matters More Than Ever

The shift from in-branch to digital engagement means credit unions must find new ways to know their members. AI helps bridge that gap by analyzing behavioral patterns, transaction history, and demographic data to surface personalized product suggestions. Instead of sending a spring HELOC promo to everyone, AI allows credit unions to target only the members most likely to be interested—just like Netflix or Amazon might recommend a movie or product. The result? More relevant messaging, better conversion rates, and a stronger member experience. “If you send a CD promo to an 18-year-old shopping for their first car, you’re showing them you don’t know them,” Fred says. “AI fixes that.”

Stream This Episode to Learn:

• How AI helps credit unions grow without needing a full data team Learn how Vertice uses AI to distill member behavior into action-ready insights.
• What’s stopping credit unions from embracing tech—and how to overcome it Fred shares why many institutions hesitate to invest in AI, and how Vizo Financial is solving that.
• Real-world results: Targeted campaigns, better conversions, smarter pricing Hear case studies showing 2x–6x higher conversion rates and improved deposit management.

Listen now! Mitch Rutledge and Fred Eisel are not affiliated with or endorsed by ACT Advisors, LLC.

Audio Transcription 

Doug English: [00:00:00] Mitch and Fred, welcome to CU On The Show. I’m delighted that you have joined me today to help, uh, capture some of the leading ideas of AI use in the credit union movement. So, let’s, uh, uh, talk a little bit about, uh, your background. How did you get started in working with credit unions way back, uh, if it is way back, uh, and then what are you doing today? Fred Eisel: I’ll start. Uh, Fred Eisel. I’m the president, CEO of Viso Financial work, corporate Credit Union based in Greensboro, North Carolina. And we help all credit unions large and small with, um, financial services, money management, liquidity and payment services are big, big things, education surrounding that. And then, uh, looking to be more innovative and looking at more AI solutions going forward. Um, I’ve been in the industry my entire career. I started in 93, uh, right outta college. My dad introduced me to this, the system because my dad was a part-time teller at his railroad credit [00:01:00] union back in the day. They asked him to be, uh, on the board, became chair of the board, became a league director, and, um, just knew nothing but credit unions my whole, whole life. And when I got outta college, I applied for this corporate credit union job in the investment area. Got the job in 93, and I’ve been a, a chief investment officer. I was an investment analyst in the, in the late nineties. Uh, I was the SVP, chief Investment Officer of the corporate here for 24 years and was just recently announced, Uh, and, uh, moved to the CEO role in January of 2004. So it’s been my whole career in the credit union corporate space. That’s all I’ve known. Mitch Rutledge: Well, my story, My name’s Mitch Rutledge. I’m the CEO and co-founder of Vertice Ai. My, uh, journey and story in the credit union movement is, is a little different than yours, Fred. Uh, I’ve been a member of my credit union since I was 17. Uh, I tell the story, you know, when I went to go get my first car, uh, my parents said, we’re not buying that for you, but we’ll come down to [00:02:00] the local credit union. They co-signed and helped me get that sweet ride. Um, and uh, so I’ve been a member for many years, but the journey with Vertice Ai, uh, is just, uh, about two and a half years ago, we launched, uh, creating this solution that’s focused on bringing advanced analytics and AI solutions to credit unions to help them. Uh, we say know grow and measure every member as they climb their financial mountain. And so that’s been, you know, the last two and a half years have been immersed in, uh, you know, how do we bring the power of data and AI to credit unions of all sizes. Before launching this company, I’ve been in the software and, um, AI solution space for my entire career. So, um, started off with, uh, um, a medium sized software company, uh, and have been with a couple of firms primarily in commercial roles. But, um, the last role before founding Vertice was with a very large, uh, platform, uh, [00:03:00] analytics and AI company. So was creating solutions for many industries to, again, leverage the power of their data, uh, in efficient and effective ways. So, uh, excited to be in the movement. It’s been an, uh, fantastic two and a half years of creating this solution for the movement and meeting great people like Fred and, and the team at Viso Financial. Doug English: So where, uh, you know, thinking about the whole industry and where AI may, uh impact the credit union industry first. What, what are you seeing, uh, and, and you know, this is maybe outside of Vertice, just industry-wide, where are you seeing AI first users starting to show up? Mitch Rutledge: Well, I’ll, I’ll start that one and Fred, you obviously talked to a lot of your, your members, but what we see is that, um, there are many credit unions starting to dip their toe in the water. And I think back to, you know, [00:04:00] 23 and 2023 and, and 2024, there were some projects, um, chatbots were the first and foremost fraud. But if you really go back, I mean, the concept of some of the traditional machine learning, uh, capabilities that are in the AI family have been used for many years around credit scoring, right? So if we really think that has been available in the market for many years. Um, that said, when we think about the more modern generative AI and some of the more advanced large language models and, and, and things that have come out in the last few years. Um, people, that’s where I think people are really dipping their toe in with chatbots and some of the back office automation pieces. We launched Vertice that uses all of these capabilities from, you know, large language models to traditional machine learning, um, capabilities and clustering all the different kinds of, uh, advanced analytics and AI solutions focused on primarily member growth. And we think that’s [00:05:00] a great opportunity to serve, uh, again, credit unions of all sizes, to really leverage the data they have in a new and efficient way to, to grow the member relationships, grow the membership, grow that wallet share, which is important to the growth of the movement. Fred Eisel: At, at the corporate side, you know, we are trying to help all credit unions large and small. Uh, we’re working with the largest credit unions in the country, billions of dollars state employees in North Carolina. Um, but we’re also working with the smallest credits in the country that are the small little church credit union that it might be two, three, $5 million. Um, and we’re trying to help them all succeed and survive. The reality is, if you look at the NCUA call data, the quarterly call data out there, it’s all negative growth, especially a hundred million and below, even if you get to the $500 million credit union in assets and below negative asset growth, negative loan growth, negative membership growth, and especially if you’re in certain pockets of the country, [00:06:00] uh, you know, in Pennsylvania, in certain areas that the railroad’s gone, the coal mining town’s gone down here in the south, the textile, uh, mill is gone. Some of these areas where the credit union is still is part of that community is struggling to grow and so. We know that and we’re trying to help all of our members, uh, try to grow and survive and not merge away. That’s the easy solution. Um, and we’ve talked to our credit unions and said we know that you need to embrace technology but also understand the credit union struggles with the resources that they don’t have to embrace technology. Um, but what are you trying to do? What are you trying to solve? And for for many of them, they’re just trying to grow. They’re trying to increase their asset size and loan share and grow their credit union, but they have what they told us and, and we met with a lot of credit unions over the last couple years. We have data. We’re just not sure what to do with it. We don’t have the resources internally to, to really take anything from it. And we need to grow. Okay. Uh, we talked internally at the staff level. What, what can we do outside [00:07:00] of the money management payment area that we’re at and we’re in. What’s the future of the corporate? How do we help credit unions in the future that’s embracing and investing in, in, in solutions and in partners that, that embrace this technology and telling credit unions, Hey, look, not only with this AI technology do you become more efficient and smarter. Um, you’re, you’re gonna be smarter at looking at your member behavior. It’s no longer looking at your member demographic. We’re trying to get the millennial. That’s, that was the talk for years. It’s really understanding and embracing the member behavior. And this is part of the reason why we like the verus ai no platform is you get to know your member. You may have 5,000 potential members and, and only 2,500 members that are active. So a lot of these credit unions are trying to go, uh, association, trying to go community charter. You got so many members that are in your shop right now that you need to embrace and get to know better and really capture more of that business. And, and the way to do that today is with technology and they have to embrace that and versa. AI is the perfect [00:08:00] platform for that. Doug English: So how have you, uh, so your partnership between the two firms is, is, uh, recent, right? This is new. Fred Eisel: Mm-hmm. We just, we, we met, um, kind of the timeline there is again, we talked to our members, latter part of 22, early 23 internally with staff. We talked about this, the need for some of this technology and to, to embrace it. Uh, we had the corporate during a very good position, uh, capital wise. So we had capital that we wanted to deploy in either buying subscriptions, investing in a qso. We were open to all kinds of solutions. Uh, we surveyed our members and they. Said the same thing. Uh, what are you, we asked two questions on our survey besides some other, um, scoring questions. What are you struggling with right now? What are you working on for next year and grow? The word grow was mentioned probably 30 to 40% of the time grow members, grow loans, grow, market share, grow, grow, grow. Um, our board said we need to look at and, and we internally, as an executive [00:09:00] team, said, as a corporate, we need to look a little differently. We’ve been in the payments space, which is evolving and more of instant payments. Um, money management space is evolving. We as a corporate also have to evolve in a way to help our credit unions somehow partner with. We’re not creating this, uh, any of these platforms. We’re not a technology company, but we can partner with folks. Um. Have these solutions that we can deliver to our credit unions. And so, uh, through Mark Haverty, we were introduced to Mitch and Vertice ai, uh, last spring. Had a great meeting and, uh, took it from there and, and really developed a really good relationship and agreed part with them, partner with them, uh, middle to late last year and as, and really went full bore the first of this year. Very excited about that relationship. Doug English: So, so that’s an interesting way of getting there. So you surveyed your members. How many, tell us how many that, how many folks that was. I’m just trying to figure out how representative of the industry as a whole is that data set. Fred Eisel: Our, our, we have 700, [00:10:00] 650 to 700 credit union members, and we say members of the corporate fully capitalized us. Mm-hmm. And that’s predominantly in the East coast, Pennsylvania, New Jersey, down, uh, through the Carolinas. We had, I want to say, I’d have to look at the numbers. Uh, the 700 credit unions, uh, probably had six to 700 individuals respond to that survey. We had a very good uptake on that survey, uh, and most of them c-suite type executives, um, that, that know what the strategy is of the credit union, what they’re trying to shoot for. So we had a very good uptake, uh, on the survey. And a subset of that membership was predominantly east coast. We also, we just know we have, we serve credits throughout the country that are maybe not full-blown members have the same challenge. So if you’re a 50 to a hundred million dollar shop, you may have some situations, uh, that are unique to you in Pennsylvania or North Carolina. But generally speaking, again, if you look at the aggregate data, a hundred million dollar shops are struggling with pretty much the same thing. And what we’re finding is they’re all struggling with a lack of [00:11:00] resources. They have a lot of data. I don’t have a data analyst or data scientist, nor can I hire one, but, but I want to grow and I wanna do it in a cost effective way. Can you, the corporate help, we find that we’re in a very good position for that. And, uh, from all that data and all the just one-on-one discussions, uh, with our members throughout the last couple years, we’ve come to the conclusion we needed to do that and partner with Vertice, uh, which just kind of checked all those boxes of what we’re trying to solve for the credit union. Doug English: So this event, the credit union movement wants to grow through ai. So we’re kind of looking at it from the lens of what the credit unions want from ai, not necessarily what AI is the most suited for, which I think is an interesting kind of coming at it from the other direction. Uh, which I’m, I’m not qualified to say maybe you guys are, but let’s, let’s go down this path, Mitch. So let, let’s go down the path of what is, uh, how is, uh, Vertice, uh, and AI as a, as a technology gonna help credit unions grow? Like, what, what does a [00:12:00] credit union need to be able to be positioned to do that? And then what can they, uh, what, what can they sort of be thinking about to, to get ready, uh, to be able to lean in to this initiative? Mitch Rutledge: Sure. Um, so I would say that re understanding that growth is that priority. I mean, that is, again, the foundation of what verti is about, is we say that we’re a member growth solution and, and it’s really about distilling data that credit unions already have, right? We have member data, account data, transactional data, but we need to distill that into something that we can take action on. And as Fred said, the, you know, the reality is most credit unions aren’t in a position to go hire one or many teams of, of data analysts or data scientists to do that work for them. They could outsource it to third parties, but, uh, again, understanding how do we build that business case around it. And that’s what our vision is to, again, give them the power of a team of data scientists in a solution that’s built [00:13:00] for credit union marketers, uh, you know. Branch teams, retail teams, strategy teams to really help them understand their membership. So when you say what do they need, I think the first is they need a, a will and a desire, right? We talk about our, our best prospective credit unions are those ones that are data curious and growth driven. Um, that’s first and foremost. And, and that data, data curious. Data curious is a fabulous, fabulous phrase. Um, but, but you know, the understanding that we, we think there’s something there. We just don’t know how to unlock it. And we need, you know, some, some help to go, to go mine it, um, is first and foremost, um, what I would say, and this could be contrarian, you know, you’ll hear people say, we need to get our data clean and right. Um, I think that is a fool’s errand. Um, ’cause there’s always gonna be more data and you’re never gonna get it right. Um, our mantra is data is a journey, not a destination. Um, can we always do better? For sure. Is there always more that we can [00:14:00] use? For sure. But if we try to get to quote right and perfect data, um, that’s, we’re gonna be waiting a long time and we say, work with the data you have and you likely can get great insights. Um, you know, perfection’s the enemy of progress and we wanna make sure that we can give them insights that they can take action on. Uh, quickly. And that’s a big, again, sort of core to our, uh, philosophy is speed to value. We can get insights and we can take action and we can continue to evolve and use more data and get deeper insights over time. So that might be a little contrarian of, you know, get your data perfect is not something that we believe in. There’s plenty of data that we can take action on about your members and, you know, their accounts and their transactions to give, um, actionable, meaningful insights. Um, Doug English: well the speed of ai, uh, isn’t the, uh, isn’t it going to get so intelligent somewhere around next month where, uh, it’s gonna be able to work with messy data and, and unstructured data and, and figure it all [00:15:00] out because it’s going to learn from what you trained on. Mitch Rutledge: For sure. Right. And this is the sort of the, the promise and what we’re all hearing about in these generative ai uh mm-hmm. Landscape that it can be, you know, it trains itself in this self-learning concept, which is very powerful. And that’s a big part of what we’re trying to bring, uh, in the use cases that we’re solving for, um, that it can learn over time. And again, back to it’s learning across all of the credit unions we work with. Right? Yeah. So, you know, my, again, my call out is the more credit unions that join our data-driven movement, we will be helping the, the, the collective. And we’re seeing that already, where, ah, some small credit unions have never had credit cards before. And they think, well, who should we launch this with? Well, we have a model that can score your members because we’ve scored it on many other, you know, credit unions around the land, and you get the benefit of the, you know, that network effect across the movement. So that’s a big part of what we think back to your big, bold movement, that we want to be providing [00:16:00] data-driven insights and actions that we can learn across the entire movement. And that’s how we go be, you know, the big four banks as a movement together. Um, is, is that collective use case and, and vis o’s helping us drive that, right? I mean their, their membership, uh, and their desire to bring it to all of their members is helping us to, uh, expedite that, that growth of the data set and the training set that we can use to support everybody. Fred Eisel: This is where we think AI and data kinda, they, it doesn’t maybe level the playing field entirely for all credit unions, but, you know, there was a time when the credit union industry was way more cooperative and aggregated and here and for the, and I’ve seen it been in my whole career, I’ve seen that. Aggregation cooperative spirit kind of gets separated a little bit over the last number of years. I don’t know when you pick when that started, but that’s been happening. Um, and so this kind of brings it back to where the more data we have in aggregation, this really helps the smaller creating that may not have the wealth of data, but they can, they can really [00:17:00] utilize the data that’s out there at the macro level and kind of see what other 10 to $30 million credit unions are doing or other credit unions in a certain region are doing. That data just becomes more and more powerful. And what I, what I like to add onto what Mitch was saying earlier is we have credit unions that are just fearful of diving into ai. It’s just, man, it’s just intimidating. ’cause I gotta get all this data together. I gotta get my data right. If some of our larger credit unions are thinking, well, I gotta write something internally, it’s like, no, no. Um. Credit unions get stuck with trying to get their data clean. They may work with a consultant that’s spending, you know, months, years trying to get their data lake together and everything talking to one another and they’re on a lot of Zoom calls. And after two years, what have you gotten? Uh, we’re still working. We’re through Zoom calls and trying to get it figured out. What we love about the verus model is verus is trying to solve one problem and that is helping your credit union grow, which is a big problem for a lot of credit unions. The data shows that. So, um, we’re trying to get that little slice of cake, um, [00:18:00] solved. We’re not trying to make the whole cake all at once, ’cause that’s gonna take a lot of time. And the bigger the credit union gets, the more people in that kitchen trying to make that cake. And it gets a little messy. Verus is solving one problem, which is a, a problem for many credit unions, and that data that it gets aggregated becomes more powerful. So if you’re a $50 million shop or a hundred million shop, our message to our members and our credit unions is you can get into AI technology. We have folks here at VIS O that will support you down that path. We have a partner in a verus AI that will support you in that path. And it’s a, it’s a, a slick, easy model to get, uh, onto very quickly without having a huge data or anybody really, uh, in the data space that your credit union, with the support of us, we can get you there. And the aggregate data that you get from that, it makes you the 50 million credit even more powerful. So it’s, it’s very exciting, uh, for us to partner with a, a solution like verus versus trying to help Crane get their data together and their data clean. That’s that. As, as Mitch said, you can [00:19:00] spend years doing that and get nothing. And, and a lot of this technology and other creatings are using this technology, you’re gonna pass you by. Doug English: Uh, can we go into some like actual use cases? Uh, you may or may not be, uh, be able to say the names if you can. Great. If you can’t, that’s fine too, but let’s, let’s talk about some cases that you guys have been involved in. Like where, uh, especially with, uh, once they use the, uh, your tools, what’s the pickup and effectiveness as far as, uh, deposits and loans and, uh, new members? Let’s hear it. Mitch Rutledge: Sure. I’ll, I’ll pick up on that. So, you know, at the core of what we’re doing is helping credit unions be more, uh, targeted and personalized in the products and services that they serve up to their members. Right? We know that many credit unions still are in a very kind of, uh, mass market, one to many member engagement strategy, right? Where we say it’s spring, we’re running the HELOC campaign, send the HELOC content to [00:20:00] all of the members. Um, kind of scenario. Yeah. And we live in the Amazon, Spotify, Netflix era where we expect personalized recommendations from everywhere, including our financial institution. And that’s what we’re helping marketing teams and, and retail teams distill and put the right members into the right programs based on their, so we’re distilling all of that transactional data, account data, you know, again, member demographic information to say what are the right programs for these members based on all of these, all of this information that we have. And doing that in a way that a marketer can say, just give me the people that show, uh, that look like they would be a good prospect for the HELOC program instead of everybody. And we can easily give them those, you know, target campaign lists and audiences to focus on. So we want to do that, you know, that used to, if we wanna do, to do any kind of personalization, many credit unions said that could take weeks to get those lists. And we want to help them get that in, you know, minutes through a intuitive [00:21:00] experience. So, you know, credit unions like Duke University and Coastal Credit Union, uh, and Wellbe Financial are all clients that we work with and many of the ones that are coming with VI through VIS O today as well are gonna join that movement to be able to do that. And they see, you know, we see anywhere from, you know, two to six times higher conversion rates when we focus on the right members. And, and that’s, again, very powerful. We all know that if you serve up things to me that are relevant to my life and my behaviors. I’m gonna respond better. Mm-hmm. And we’ve seen that across the board with our, you know, more than two dozen credit union clients that are, that are doing this with us today. And so they see higher conversion rates. We see, you know, in some cases we’ve seen over 40% higher initial deposit rates on new accounts. Um, so, you know, the click through rates are higher when you get campaigns. So, you know, we get to a point where the credit unions are back to becoming that trusted advisor with their content. Instead of we’re just blasting, you know, all sorts of [00:22:00] content every day, every week to people, which we don’t want. We want that relevant, personalized recommendations. And that’s how we get higher take up rates, response rates, you know, and, and ultimately, um, values for, for deposit products. So yeah, all of those are, are examples that we’ve seen. Fred Eisel: We, we had a presenter at our financial conference a couple years ago. Um. He was generally our age. He had a young daughter, probably 17, 18. And you know, his comment was, this is a couple years ago before AI was really taken off about sharing of data. And he said, you know, I, it’s not comfortable. Our generation and older just, eh, we don’t wanna share our data. We wanna keep it all under the, uh, you know, kind of private. And, and his daughter’s like, I want everybody to have my data. I want you, whoever I’m buying from, telling me what I should need or what I should buy. And that’s where the retail space has been for years. As, as Mitch said, so. The younger generation is very comfortable with the credit union having the data, uh, you should [00:23:00] know me, that’s, that was the power of the credit union for many, many years, is I know my members well, with, with the online, um, you know, banking nowadays and everything in an app, you don’t go into the branch anymore. So that connections kinda sort of kinda lost. But if I have the data and I know the member’s behavior, I can be way more impactful for that, for that member, young or old. Um, and, and then younger generation, for sure. Once you, you better know my data, you better know my behavior and that is gonna help me work with the credit union a lot more in the future. And that’s where the credit union’s gotta be comfortable embracing the technology. Just not because of that. But as we more move into more of an open banking world, and that’s coming. It’s already here and it’s coming quickly. It’s, it’s, it’s um, uh, open banking. Flourishing in, in Europe and the UK and everything in the financial world kind of starts over there. Here, open banking has begun. It’s going to become a thing where your data is gonna be. Your data’s just gonna be shared amongst everyone, uh, to, because the consumer wants everyone to [00:24:00] have my data so you can serve me better. So the credit union not only needs to embrace the technology to grow and better serve their member and know them better. As we, as an industry in a, in the financial industry move towards open banking, you, you’ve got to know your member and their behavior and their life path to effectively, uh, serve them and, and have that technology’s gonna be critical going forward. So if you’re smaller, mid-size, like I really don’t need it, you, you’re gonna need something to embrace now because as financial industry changes more to of an open banking platform, uh, it’s gonna be critical or you, you will not survive. Doug English: So you, the first thing you mentioned, I think was a, a HELOC campaign, and is that typically the side of things that, uh, you’re going after? Is the lending side or, or, um, or did I get that wrong? Mitch Rutledge: No, we’re, we’re focused on all products. Right. And I think that’s a, that’s the most important part of, there are many solution providers that are very product specific, right? They, I can help you with HELOCs or I [00:25:00] can help you with checking accounts or auto loans. What is unique about Vertice is we want to give the credit unions a view across all products and services and find what’s important to the credit union and how that overlaps with what’s important to the member, right? What are the, what are the needs and what’s of the member, and what are the needs and what’s of the credit union? And we can help you find those intersections so you can have more effective. Uh, programs and campaigns for the members, right? Because again, you think about last year there was a time where I could show you all the people that wanted HELOCs and, and all and lending, and it was like, well, I’d love to do that, but we need deposits. Yeah, deposits now. That’s right. It’s still today. And so we want to, there’s people that are in a good place, you know, that are very focused on deposit oriented products. Let’s, you know, engage those people with deposit products and let’s engage people with lending products where they need it. So we’re really trying to. Find those overlaps of the needs and wants of the members and the needs and wants of the credit unions, and that changes over time. So we have the flexibility to, [00:26:00] to ebb and flow and find those opportunities. Fred Eisel: Yeah. At Duke University, I think, uh, they have mentioned the marketing person there can’t wait to get the dashboard from verus whenever they update every two weeks or every week. I don’t know what it is, but they can’t wait to get that dashboard that first part of the week to kind of see where they’re gonna go. Uh, just something simple as a 13 month CD special. For example, you know, credit unions do a blanket marketing campaign. Well, the 18 to 21-year-old member’s not really looking for an investment CD special, right? They’re looking to save money, maybe a new used car, their first new car, Mitch’s new car. Um, so just basic things like that where you’ve gotta be smarter in how you’re marketing to your membership and looking and drilling down who’s got deposits, where the deposit’s going, um, and having a very specific marketing campaign to those individuals that where, where we, you can really make a difference in growing those deposits rather than a blanket campaign. That half that membership is just not, that’s not. That world. And then if you send me that and I’m a 17-year-old looking for my new, for, for my new, uh, first new car, then you, you’re telling me you don’t really [00:27:00] pay attention to me. You don’t know me. I’m not, I don’t have an interest in a 13 month CD special. What do you have? A used car special that’s, you know, a lower rate up to five to 10 grand, whatever that is. Um, so it’s just, it’s gonna show to your younger member or, uh, your entire membership do, are you paying attention to me and my behavior and my life path or not? Um, and, and for us, what we’re trying to do at the corporate is given the, given the credit union, the tolls, because again, what they’ve talking to some folks, uh, at a conference just last week, what are you struggling with? I want to grow. I don’t have the, I have data, I don’t have the resources. If we need to do it fairly cheap, well that checks all the boxes of what we’re trying to do at the corporate. Working with verus is we got a platform that will solve the growth problem. Get your data ready, uh, you understand your members better. And because they’re working with vis o they’re gonna put at a pretty damn good price, uh, point for the, for the platform. So we’re trying to help and solve. What a lot of credit unions are struggling with right now is trying to get into the [00:28:00] space, knowing they need to get better at data. Knowing to get better, knowing the member, preparing for open banking, but in a way that’s kinda solving a small problem, uh, at a, uh, at a reasonable price. Doug English: And you, I think you started just a few months ago and you already have five, uh, folks signed up. Now. How long does it take? What’s the cycle time from when you sign up to when we, we’ve you’ve got the data, you’ve analyzed the data provided. Uh, it’s interesting, the insight. Fred just provided a dashboard, so you’re making them a weekly. Dashboard of, uh, of, of ideas. Tell us about that. Mitch Rutledge: Yeah, so that’s a great point. So several question. There’s there, um, so one of the core premises at Virtus was speed to value, right? Again, past life in big analytics projects, as Fred, we get as stuck in this data, data data takes months, you know, or years we said, no, we want to, we wanna, how can we get people value in, you know, weeks is our, our, our measurement. Um, and so we [00:29:00] do need to get data from the core as a key source for that. Um, depending on the core, we know that’s, you know, easier or harder for difference. Uh, if it’s Simar, uh, we have a standard integration to that, right? And we had a credit union that onboarded and we had data outta Simar in two days. Wow. And once we get it into, verus exactly right in two days, and then once we get it from there, we can have them up and running in, in a week or two’s time. So our goal is in, you know, less than four weeks. Uh, from the time that we get that data that they’re in there getting value and it’s more than a dashboard, I want to be clear that is one piece that is valuable. Um, but there are many dashboards and, you know, things that you can do, uh, that are valuable. But the real action is give me those targeted lists. How do I build these actionable, um, program audiences, uh, for di you know, all the things that we’re looking to grow at the credit union and that again, in, in four weeks time, we’re, we’re trying to get people up and running on this. Now we are, we do [00:30:00] need to get that data. So I, you know, that’s not, sort of, mileage may vary based on when we can get the, you know, when we can get the data. So there is some need for the credit unions to do some of that, but we are, like I said, we’ve added the standard, um, connector to Simar and we’re working on that with the other major course to continue to make it, uh, easy and efficient for, for all of the credit unions. So, so that’s a big part of it. So, um, goal is speed to value. We’ve seen it as, uh, you know, as few as two days to getting the data and, and a few weeks to getting up and on the solution. Um, and it’s more than a dashboard. It’s really a, it’s an actionable set of recommendations. Um, usually for marketing, but we’re seeing it in retail as well, where the branches say, give me those target lists that we can use for our engagement with, with members as well. Um, and, and now we’re seeing lending teams as well saying, Hey, help us. So everybody wants to be able to get to this data within the credit union to impact their, their business functions. Doug English: And, and, and in an AI fashion, do you [00:31:00] take the effectiveness of the campaign and pay attention to, uh, what did and didn’t work, and then, uh, train the model to be more effective? Mitch Rutledge: Absolutely. Absolutely. We’re t typically getting refreshed data from the credit unions on a weekly basis. And so we see what is, what are the actual products getting opened, right. What’s actually happened. Mm-hmm. And the models are retraining and rescoring on that regular basis, so we’re giving updates for them to be able to, you know, make new decisions on those. Um, you know, target audiences when we see something, right? We need to take the person on the auto loan campaign if we saw that they took the auto loan, right? Yeah. This week. That’s great. So, um, and, and we can do more frequent, but back to, we think this is a journey, not a destination, right? So if you’re coming from nothing. And getting this kind of weekly insights on the strategic decision making is very valuable. Are there credit unions that could use, you know, uh, more daily refreshes for sure, and we’re ready to support those. But, and that [00:32:00] was one of the big things about the partnership with VIS O was, look, we have a lot of credit unions that are very early, or haven’t even started on this journey. Mm-hmm. So we need to have the, you know, classic crawl, walk, run, um, approach to be able to support them. And that’s what we’ve structured as a program that they can, you know, getting to back to the know my members first, right? So we have an offering that’s like, let’s just start to know my members and then we can get into some of the more advanced AI modeling to grow and some of these measurement things, uh, over time. So we’ve really tried to make it approachable for credit unions, one of all sizes, but more importantly of all, you know, where are they on the maturity scale of ready to adopt some of this. Fred Eisel: That’s where I think we’ve really enjoyed the relationship too. ’cause verus gets it. Um, we, we know our members extremely well. We know the crediting is extremely well, and while they want this and they need to have it implemented, we know the path for crediting typically is slow to implement, uh, budget [00:33:00] wise or resource wise or strategically, or we’re going through a core conversion, a card conversion. So the, the take up is slow even though they do need it and are requesting it. So yeah, we do wanna take the path. It’s, it’s, it’s pretty methodical and understand that, that that’s the way it works in our industry. Uh, but you know, verus has a, some folks, uh, at their shop that are very supportive of the member of the credit union. Uh, we’ve hired some staff internally, an individuals just started this month, uh, that’s gonna really gear up and, and manage this product for us who, uh, was a former CEO of a credit union. So he understands the challenges of a mid-size credit union, the need for this product. Uh, so there’s a lot of support between verus and vis O to help the credit union. Take, dip their toe into ai, into technology with a platform like verus it’s solving a solution, uh, kind of the crawl, uh, piece of it before they walk. And there will be support at both in, uh, institutions to really help the credit union kind of go down that path. Uh, we’re also looking to help credit unions, uh, in the [00:34:00] future develop an AI policy. A lot of folks are like, okay, I got this model, but I don’t have a policy. I don’t have a program, I don’t have a digital roadmap of how this is gonna integrate with other AI type platforms. So that’s a piece that we’re gonna help with as well, is kind of develop AI policies, procedures, a process, a roadmap of how other things integrate. Um, we, the corporate are also working in an innovation lab to, to spin out other new products in the AI space that, again, solve certain situations and problems that the credit unions are looking at. So, uh, we’re investing some time and effort and money, uh, in, in developing future solutions, uh, to help the credit unions that is easy and affordable is what’s key for most of our members. Doug English: Yeah. Yeah. Well, you know, you mentioned a couple of big, of big names and I’d really love to, if you could share some sort of the story of the, the experience for one of ’em. You know, like they started, uh, at this point in time and, you know, we got the data and identified the first action item was [00:35:00] locs or whatever it may be. And they did that and then, uh, and then went on to so and so, like, and especially if you had any, any metrics around the effectiveness, that’d be really, uh, neat to hear. I understand. If you don’t, it’s fine. Mitch Rutledge: Well, I, I mean, I’ll, I’ll give you a, I’ll give you an example. We, um, well be financial, right? There’re a credit union down in, in Texas that we work with. Um, they, you know, it started with, can you help us with the, the age old indirect programs, right? Uh, can you help us with the indirect conversions? Which is, you know, we, we were probably naive, like, oh yeah, of course we can help with that problem and, and. And so we dove into it and we did, you know, we clearly identified there is a group of indirects that are gonna convert, and there’s a group that are not going to convert. And, and the models can, um, do a pretty great job of identifying who shows higher propensity for indirect conversion to others. Um, and, and the numbers were significant there, right? You [00:36:00] know, two, two to four times higher conversion rates on the targeted groups down there. Now look, it’s on a small number, like let’s be honest, uh, with that, but, but those are the kind of results that we see pretty across the board when we focus on targeting the right members with the right products and services, um, across the board. And again, I think in, in some of those scenarios when we were targeting deposit products, that was the example where I think we saw roughly, um, you know, 30 to 40% higher deposit rates in terms of initial deposits when we got this product. So. Very sizable impacts that we’re seeing. Um, back to if we talk to the right members about the right products. Now we have examples too where, you know, again, with, um, certificates where we’ve seen, you know, six, eight times higher conversion rates. And one of the things that I also say is like, recognize. When we, when we talk about some of those numbers, we’re changing the denominator, right? So what [00:37:00] does that mean? It used to be we sent it to all a hundred thousand members. Mm-hmm. Well, I’m gonna say only send it the same 20,000 and we’re gonna get the same conversion rate, so I’m gonna get a five x, you know, in that scenario kind of conversion rate. Um, but that’s important because it goes back to Fred’s point, which is we’re talking to the right members about things that they care about or are meaningful to them. And so now I have, you know, the rest of the 80% of the membership that I can target with what they care about and back to building that trusted relationship. So that’s a big part of where, you know, focusing, focusing the funnel on talking to the right members about the right products and services. Doug English: Yeah. And it’s a huge message of I know you, I’m here to serve you. That’s right. I’m here to help. I’m not just blasting out marketing to grow the institution that I send to everybody. I’m here to serve you in your situation today. Mitch Rutledge: And, and I believe every credit union wants that and, and at times tries to do that and, [00:38:00] and, and at times does it well, but to do it at scale all the time. Is the challenge. And that’s what we’re trying to bring, is leveraging AI to make that a repeatable, scalable, mm-hmm. Program. Mm-hmm. Um, you know, I, I tell the joke, you know, if I put Fred or some credit union, CEO and their CMO and a data analyst and we lock him in a room for a week and say, give us all of the people that we should really focus on for the HELOC program, I’m sure they could come up with a fantastic list. Right. Nobody, no, no interruptions, right? They could crunch through all the data and figure all that, but that doesn’t scale, right? And so the power of AI is scaling that kind of capability to run it for every product, for every member all the time. And then we can choose how we wanna, you know, how we want to engage it. Get marketers back to their focus of how do we create great products and great collateral and great messages to engage our members. That’s what we wanna, you know, uh, empower them to do. Um, so yeah, [00:39:00] that’s the real, you know, what we’re focused on Fred Eisel: that, that’s the important part is this is where we’re at today. Again, 20 to 30 years ago, we were co into the branch and we knew the membership and we knew Doug, that you were looking for a used car. You’re looking to refi a car, buy a new house. We knew that through. Conversation ’cause we had it face to face. Fast forward for the last 10 years, and especially now, it’s you’re, you’re banking through your app or, or the website or home banking. And some folks, some credit unions have branch activity and traffic. Yes. But many do not, especially for the younger generation, they’re not stepping into a branch. So, well, you know, while you think, you know your members and many credit unions do know their members, there’s a lot of members that are, you’re just not tied into just because you don’t have that interaction anymore. Given the way we’re banking today, uh, and as Mitch said, you could, you could put us in a room together and figure it out, but in today’s environment, you’re gonna need something smarter technology to. Do that analysis of for you and understand the membership behavior. I may not know Doug personally, but I know what his [00:40:00] behavior is given his age and kind of what I see transactional data telling me. I am smarter to know what the propensity and try to project what you’re doing in your next stage of life. You’re graduating college, you just got married. Um, it’s data driven now, uh, because we don’t have that relationship anymore given the way that we bank today, especially for the younger generation. So having data to help you get through this and be smarter with your member is critical. And that’s what your younger member especially to, to retain them. They’re expecting that from from the credit union. Absolutely. So that’s the crate has to embrace the technology. Doug English: Yeah. And then, and then, and then it needs to get better. It needs to get smarter. It needs to know me more. So have you seen that already? Measures it too early to have seen where the campaign. Was, you know, uh, three times as effective as the internal campaigns the first time. And then we dialed the algo better and then it was four or five. Have you seen that, or is it too early for that? Mitch Rutledge: I, I think it’s a little early to see that. I mean, you, you get into [00:41:00] the, uh, other bit of, now we’re talking about, yes, we wanna grow with the existing members, but there’s also new members that we need to bring in, so mm-hmm. We’re starting to think about how do we empower new member acquisition growth as well. Um, so we’re, we’re thinking about how do we expand to use these kind of capabilities for other, other use cases of member growth. Doug English: Mm-hmm. Yeah. I mean that, that makes sense. I would think the, the biggest use case would be around, uh, lending products because that you could see the transactional data of something that was going on for sure. That would lead you down that path. Right. That’d be the most obvious, uh, solution. Mm-hmm. And the one that the credit unions need the most. Right. That and member growth. Right. Wouldn’t those be the tip top two? Fred Eisel: And deposit deposit growth. I mean, you know, there’s a lot of crates that, uh, have said in the past prior to having data, um, you know, the fed raises rates overnight rates go higher, money market rates go higher, and they see money leave. Um, one example, not using verus, but just looking at their data, old [00:42:00] school, you know, the group, the alcove said we probably should raise our, uh, money market rate to kind of retain those funds. And the CFO went back and scrubbed through data the old school way and realized, you know, the money didn’t leave here and go to a money market fund, a Fidelity or somewhere else. Uh, some money was went, went somewhere to pay off a mortgage, some went another place within the credit union. Another one was wired out to a different location, so they didn’t raise their money market rate and it saved themselves money ’cause the money leaving was not. Because of higher rates elsewhere. There was other ways the behavior was different. And so this individual, very good example of somebody that wants to embrace a verus AI because it makes ’em smarter on, uh, decisions with deposits, deposit rates, and how to price more effectively and save money that way. If you don’t raise your money market rate five to 10 or 20 basis points, you’ve paid for the model, uh, on 5 million or $20 million that you’ve didn’t reprice ’cause you thought you had to, you’re looking at your data and being smarter and saying, you don’t have to reprice our rates. We, [00:43:00] that money’s not gone to money market. We just gotta be smarter by being smarter with your cost of funds. You’re saving money there and paying for the model. So on the deposit side, it’s just as impactful. If you want more deposits or need to bring, bring deposits in, you do the 13 month or 18 month CD special to your targeted market, maybe you bring five, 10, 20 million in, uh, for deposit growth because you need to fund loan growth that’s going through the roof. So it, it works both sides, uh, on the asset and liability side, and both ways can be very effective. So when a crane looks at the cost, oh, it’s gonna cost me some money for the software solution you have, but a couple decisions on either side of the balance sheet that you make that are smarter more than pays off that cost of that software. Um, the ROI, that’s very significant, Doug English: very different angle that I did not anticipate. So that, that was instead of marketing, it’s it’s pricing. Uh, in, in the strategy side for sure. Yeah. The strategy side, I assume of both, uh, [00:44:00] the deposit and the lending side. Fred Eisel: Definitely, definitely. ’cause you know what your members are doing, um, and you’re not reacting to what Mitch, Mitch is cra down the street did. Mm-hmm. Or Doug’s offering a 1 99 car loan right down the street. So we gotta, we gotta do the same thing. What is our membership? Our membership doesn’t, they’re not have the credit worthiness of a 1 99 or if we do a 1 99 car loan, right? Like they did in the past. Maybe we offer some type of credit card, uh, or they’ve gotta sign up for a credit card or whatever. So you’re with the data, you’re way more strategic and you should be way more strategic on your pricing. And that pricing being more strategic, makes you a smarter credit union. Uh, and that in then in turn increases your margins and easily more than pays the model that you’re utilizing. So there’s, there’s so many ways, there’s so much data and so many ways to look at it and spin it. So many decisions to be made strategically. And then once you’re in it. You start to look back and like, how did we make these decisions before you just luck that what the competition [00:45:00] did down the street or what you thought was happening, and they probably weren’t, some decisions were fine, but some were not made probably with accurate information. Uh, and with AI and technology today, you gotta have it ’cause that’s gonna make smarter decisions, which will in fact allow you to save some money and, and grow the credit union. Doug English: So if a, if a credit union, uh, leader has been listing the podcast and wants to learn more about this, where’s the best place to go? We all just got back from GAC where there’s probably great opportunities to learn more about, uh, both organizations. Is it, is it the website, is it a webinar? Where should they go to to learn more about, uh, uh, virtus AI and, and your partnership? Fred Eisel: Uh, we were at GAC as well. Uh, we had a, a double booth vis o did in partnership with v, uh, verus, and we had a, a rep there at verus as well. We also had a session on ai, uh, talking about technology. So those that attended GAC, um, they should have got an email of recorded sessions that sessions out there. So if you didn’t attend that, our [00:46:00] session, uh, you should be able to download that recorded, um, session. And the, uh, the PowerPoint presentation, obviously you can, uh, also go to vis o financials website, um, vcu.org and, uh, go onto our website and get. A bunch of information, uh, on there. If, uh, or just reach out and call our 800 number, uh, talk to our staff, we will line you up with one of our account managers who can, uh, line up some more information discussion points around it. And if you’re interested in the demo, uh, we can line that up with the verus, uh, group as well. Mitch and his group are out quite a bit as well at a variety of conferences. So same thing applies there. If you wanna reach out through verus by all means, and Mitch can give you that information Mitch Rutledge: for sure. Verus analytics.ai is our website. Would love to hear more, reach out to me on LinkedIn and we’d happy to tell you more about the partnership and, and what we’re doing and, and support your credit union. Awesome. Well, Mitch and Fred, thank you for, uh, your work in supporting and growing the credit [00:47:00] union movement. Uh, I hope that, uh, AI can be the key to reverse the shrink, uh, and, and, and to help credit unions, uh, grow faster in the future. Thank you so much. Thank you very much. Thanks Doug.

Episode Links

Vertice Vizo Financial | Back-Office Solutions & Support for Credit Unions Fred Eisel | LinkedIn Mitch Rutledge | LinkedIn