This is the CU-2.0 podcast with your host, Robert McArvy. Big new ideas about credit unions. CU-2.0 podcast. The answers you need to optimize member service and to keep your credit union on sound financial footing are already available, already available. It's just that they're hidden in your data and you've got lots of data. That's where Mitch Rutchledge, CEO, co-founder of Vertis AI, comes in. His company are data scientists who specialize in finding useful information. And the vast data stores just about all credit unions have. Quiz time. Let's a Vertis. His name of the company. Don't sweat it if you don't know. Geometry wasn't a strong suit of mine either. But yes, a Vertis is a determined geometry and what it is is a place where two or more curves, lines, etc. meet. What Vertis Litch wants to do with a credit unions data is sift it in search of places where a presently unmet member needs aligned with credit union offerings, just that they have never connected. Vertis AI wants to help with that connecting. Sure we know. You're hearing lots about AI these days. And past years, you heard a lot about big data. But this show is different. It's practical hands-on. Definitely no need for Carnegie Mellon or MIT degree. Vert Litch is talking really simple, well not simple. Vert Litch is talking how to use big data tools, data sifting tools to deliver meaningful results in real time for credit unions. He's also talking about an eight week startup time from the moment a contract is signed with him until that credit union begins to see some results. Vertis AI, by the way, already has credit union customers, including one very big and very high prestige institution. Listen up to find out what credit union already is seeing real value for Vertis AI's work. You will be impressed. Listen up. Start by talking with your name and your company. Mitra Litch, Vertis AI. I'll ask you a question. I don't think I've ever asked before. What is a Vertis? Vertis is an intersection. So we talk about the intersection of marketing AI and members. I think in mathematics, I think it's where two lines meet. I think. Yes. So how do you create vertices for credit unions? So we're focused on the intersection of predictive analytics, members, and growth. And what that really means is, using the data that credit unions already have to find opportunities for growth or risks for a term for losing members and serving that up to credit unions so they can take action and make member decisions, better decisions on how they engage their members. So for us, as long as I've been involved in credit unions, which is approximately 20 years, people have said, well, we're going to, we have ways to use your data so you can get better results. And for 19 of those 20 years, it simply hasn't worked very well. What's different today? Well, I think what's different today is that obviously the modeling capabilities are more efficient and more effective than they've been in the past when you just think about the growth of advanced analytics. And all the talk we can talk about, all the chat GPD and large language models and what's available at your fingertips today that wasn't available in the past. That was a much harder lift to do this kind of analytics in the past. So I think the technology has come a long way and obviously the compute powers become more cost efficient to do this kind of predictive modeling. So that's a big part of it. To put it in a consumable and efficient way to do this. And we are focused, I mean, that's a big part of our value proposition is making this consumable analytics. Right? I mean, the term AI I think can be scary to some. Hopefully it's becoming less scary. But we want to make it to put it in the hands of marketers and credit union executives so that they can take action on this kind of advanced analytics. So we think the technology and the ability to do this has become much more accessible. What distinguishes your company from all the many other players in AI? Yeah. So I guess for some force, I would say that we are, we wouldn't claim to be an AI tool company. We're a member growth engine that uses AI and advanced analytics under the covers, under the hood, so to speak. So we want to be known as a member growth solution for credit unions that leverages this capability. And what's different is that we, my background, I used to work for them, the big analytic solution companies. So I've been in this space for many years. And the challenge has always been finding the people that can build models and, you know, turn data into insights. And what's different about us is that we're making that consumable for credit union. So they don't need to hire a team of data scientists because there's a shortage of data scientists already and they're hard to come by. So we want to do that heavy lifting for the credit union so that they can just take the action on the insights and what they've learned from the data. So that's first conformist is that we've built a series of advanced analytics models that get served up and we say, a B2C user experience for marketers and executives to understand their membership and take action on it. So that's what we think we're really focused on. The second big piece of it is they have data, you know, credit unions have data today. And so we want to use the data that they have. And there's always been this concern about getting the data into a format to actually take action on as difficult and takes a lot of time. But we think that we've come up with what we call our minimum data requirement to give value. And that is data that they will have accessible behavioral data about their membership and transaction information that we can give them real insights to understand who are the right members to offer the right products at the right time. So really map a member's financial journey so that they can take action on it. So run me through a simple scenario. And credit unions says we'd like help on what and then you say, okay, here's how we can help you and and tell me how you do help them. Sure. So so first and foremost, our solution is meant to not be just a, you know, a consulting engagement to solve one problem. We want to give them a solution that they can over time, you know, map map and grow each member within the membership. And so let's use the most, you know, the topical one, which is deposits, right? Everybody's saying, how can we grow deposits? What can we do with deposits? And we're working with a with a credit union client right now that has, that we've got a new money market product. We want to get out to the membership. Who are the right members to put this in front of? Because we know if we send it to everybody, which is probably what most credit unions are doing, they send everything to everybody. Today, you get communication fatigue, offer fatigue and, you know, people truly say, you don't know me, you're not giving me the things that resonate with me. So if we can give them a refined, focused, targeted opportunity, right? Who are the high growth potential members for this product? Then they can put the right content in front of the right members. And so that's what we're doing for this, this particular credit union. They said, we've got a new money market product. Who should we be targeting targeting this with versus sending it to everybody? And we're using a set of predictive models to that identify who are the members that have the highest propensity to take that particular product. And also what is the right channel to engage them through? And then they can put that into their execution system, whether it's email or maybe it's calls through the branch or maybe it's through you know, online banking platforms or maybe all of the above. But we're giving them a refined targeted list of members that they should put these different products in front of. So they can be, again, that personalized experience, which is what members want. You know, we referenced a survey, I think from the last year or so that said 66% of people went their financial institution to be more like Amazon or a personal shopper. And we think that to do that and need to understand your members and then find ways to put the right products in front of them at the right time. Makes sense to me a couple of years ago, my credit union offered me a credit card and I didn't have one from that. And I said, fine, I'll take it. And I use it exactly one time. I'm pretty much immediately when I came. But in the while, I totally forgot about it. Great credit card. However, what was great about it was the low interest rate on any balance. I have an out of credit card balance in decades. How you don't have it. So wow, there's a wonderful feature, but who cares, not me. Then I happened to notice on the website that I could swap this credit card instantly for a cashback rewards credit card. So okay, that's more peel like. So I did. But it's only because I happened to be looking at the website that I saw that offer. They didn't offer this to me. I just found it just by happens.
Well, you've hit right on it and that's what we believe that we can predict, you know, with some accuracy, what are the right products to put in front of the right members? And obviously with any it's not a crystal ball. Let's be clear, but you know, we think we can give them much better precision and understanding of what their members want so that they can that exact example is one that that we're focused on. I'll give you the example that I use. I've been a member of a credit union for You know over 30 years I bought my first car, you know, I say bought one for my parents coast sign of my first car with the credit union You know over 30 years ago and I Just got an offer from this credit union that I've been a member with over 30 years In fact, not just one I've gotten you know emails I think almost every week for the last month that says Do you want to apply for this undergraduate college scholarship that we're offering? That they would know that but I'm probably not the right target for that undergraduate scholarship But they clearly don't know me and so I think there's so much opportunity to help the credit union movement with Understanding their membership better. We talk about no Grow and measure each member as as what we're focused on helping credit unions do Now what size credit union or size range of credit unions do you see as your target market? So today we're targeting credit unions with More than 50,000 members, but we think our sweet spot is the you know greater than 100,000 Members because that's when you have enough Data that you can you know do these kind of predictions and do this predictive modeling when you have a large enough sample size Our vision is that there will also be the you know the network effects that once many credit unions have this then the models can train across A broader data set and so that then we think that you know smaller credit unions will We'll be able to give them some valuable insights around them But you know if you have a too small of a of a of a data set then we can't really get great predictions about those those financial journeys But so our focus today is you know sweet spot is probably a hundred thousand or more members and and that's where we're you know Trying to get in front of in our first couple of clients or in that world. So your first couple clients have more than a hundred thousand members What's the what's the asset size of those clients? So the 1.5 billion up to 4.5 billion as where we are today Okay, so that you're basically looking at so pick round numbers maybe 10% of today's credit unions 500 or so out of 5,000 that that's what we're starting we think again as we get more data We'll be able to serve the you know down to probably 500 million We're starting in the greater than one billion We're talking about members because we think that's where they're really is when you talk about a membership Where we can make you know decisions and give those prediction? Well, as we've talked to other credit unions and you you will know this is that it is very common that any new offer or product Very often goes to the entire membership, right? Maybe there's some rules about limiting, you know a few based on a few attributes or few variables but it's it is not They're not doing a lot of targeting in terms of the products and and offers and Messages that are going out to their membership and we think that we can help them with that and we know that that comes with the challenge of the more Campaigns or messages and and different varieties and and versions of things that I want to set out now I have to do that makes it more difficult on the marketing teams and we think over time we you know We can give them a smarter and more efficient way to you know do more with the resources that they have today in terms of more targeted messaging and marketing Tactics that when when you have a smarter list and a smarter storyline to engage with How long have you been working with the credit unions that you're working with? Yes, we're a fairly new company. We Started talking with our first development partner client which I can share as as coastal credit union, you know the conversation started over two years ago But we've really been kind of in production with them just since this year So really you know last year was about building and learning and creating all of these productive models and now They're in production this year. So if I called them up and said what's the best thing you've gotten out of this? What would they tell me? Oh well, I would encourage you to do that But I think they would tell you that they're getting a true understanding of Who are the right people to be targeting? With the products that they're focused on right and every credit union may have a slightly different focus if it's on deposits or specific loans or specific New offerings that they have now they know who are the right people to engage with with those products and services and that goes back to the core of the If I want to grow my Member economic participation and help my members in their financial journey I have to know what are the right products to put in front of them now Coastal is a coastal CEO check purpose and I think it's retiring is Just retired as well Has has been known as a guy was willing to go out on technological limbs from time to time You need that kind of CEO to embrace what you're offering that well You know you Robert you talk to many more credit union CEOs than I do and I think My answer to that would be We know that it is challenging times and that credit unions need to continue to stay relevant and I think this has got to be a requirement for them to Think about how they use the data they have to know their members Better more intimately and give them the products and services they want because there's more competition And they have a much more demanding Membership right? I mean I say that we live in the Amazon Netflix Spotify world You know generation and they have an expectation that you know what I want and you're gonna serve me what I want and That's the new reality that I think credit union CEOs need to think about how they're gonna serve their membership That has that expectation That's exactly what I tell credit unions is that we live in a world where Amazon and Netflix pretty much know what interests me Because they have a lot of data and they know how to use that data But I now expect pretty much every business I do business with to have that same level of insight into me and very few of them do Yeah, they have the data the data is there You know my credit union could have looked at my monthly Cash out and they say god. He pays a lot of money to American Express discover blah blah blah obviously uses a lot of credit cards Why isn't he using ours That would have been a good question and your data probably could have discovered that I don't know if you're looking for that specific case, but you you could have discovered that if you were looking for it You're exactly right those are the use cases that were continuing to build into the solution to serve up in these what we call Hyper-with opportunities for credit unions and I think if you ask any Credit Union executive they would tell you that they want to do that But what are the constraints which is probably resources? Right, we don't have teams of data scientists and you know teams of data analysts that can pull all that together So that's again when you asked about what is the Focus and and what is verticei Trying to differentiate on is to make that an efficient outcome And without having to hire a team of data scientists right? So we want to do that heavy lifting and serve it up to the marketing teams the Customer success the growth teams to say who are the right people That we need to put these products and services in front of and and the alternative which is where do we have risks? Right? Where are people no longer using products right? You're your credit card example right? You were you basically you had churned from that That credit card, but they hadn't taken any action to to do anything with it right? The right scenario would have been identifying that and doing some kind of outreach campaign to you to say This might not be the right product for you, but we have an alternative and suggest that or put it in you know into a customer service Person to to do outreach to you to say can we get you the right card? And that's what we're trying to do that in a way that efficient for credit unions to take action on and we think this is a journey right? It's it's you know You don't have to solve it all. Let's not boil the ocean right. Let's let's You know one camp at one campaign at a time one Group of our of our membership at a time. What can we do to again? get more engaged and And grow their economic participation and how long does it take you to get up? So credit union calls you up So that's fine. I got it checked here. It's your name is not it come down pick it up How long does it take to for you to start producing results that they can use? Yeah, our goal is to have them have an actionable member opportunity lists within eight weeks. Oh wow. That's that's That's that's cool. Yeah, that's two months Because I remember a lot of these big data projects would have well maybe six months from now We will have something for you to look at maybe and and and robertgers
your spot on and that's been in my past life. It was always, you know, data was the long poll in the tent. And that's why, again, one of our differentiators is, we don't eat all of your data, right? We need a minimum data requirement to give you predictions of who are the right members for the right products. Can we take more data over time? Absolutely. Would we love to get more? Absolutely. But we know that we need to give you, you know, outcomes that you can take action on to justify this investment. And so we want to get that to you quickly. And then over time, we can, you know, I always say that data is a journey, not a destination. And so let's start with something and then we can grow over time. So that's our goal is to get you outcomes quickly. And again, we talk about no grow and measure. So we want to get you opportunity list to help grow and particular products and member groups. Learn from that. Again, the AI comes in at the self-learning system that the results of the outreach you do on campaign one, we take that into learn over time of what is your membership respond to. And that's not just about products, but it can also be, you know, what are the tactics that you're taking? Or is it email? Is it through the mobile app or whatever the tactics and channels that you're engaging with, we can learn what are the right tactics for your membership? And, you know, historically a lot of data scientists, they're kind of like orders with data. They love to accumulate it. And, you know, so why do you have those 17,000 copies of Time Magazine? And some companies like Google, Google's always seen them be very pragmatic, Google collects data to use it. And they pretty much know how they want to use it before they even collect the data, but then they're a bit flexible and they will change if they see more uses emerging. There's a lot of data scientists just say, oh, wow, we have all these data, this is cool. And they don't have a plan for using it, I don't think. - I completely agree with you. I use that metaphor often that, you know, I feel like a lot of organizations, and not just in the credit general, in many, many worlds, again, in my past life, I, you know, those extreme hoarding shows. That's what I feel like. They have all of this data and then they just become buried in it and they don't know where to find anything. And, you know, we've got terabytes and petabytes of data about we, you know, can't find what we need to take action. And so that's why we believe, you know, there's a minimum data requirement to get us some insights to take action. And then as we learn, we can grow and determine what the next best data elements that can predict our members needs and wants, we'll add those over time. - Yeah, I think I have my various computers that on each of them, I think I have at least a terabyte of storage, which is just stuns the hell out of me because that I'm an old guy. I remember when a floppy disk would have, if I remember correctly, 360K. And so now we're up to a terabyte. And I, I, hey, look, I like data as much as the next guy does. So it's, but the data quantities are enormous. Now, security of the data, why should I trust you with my data? - Well, so Robert, you can trust us for two reasons. One is that we're actually not taking any PII data. We're doing everything with anonymized member data. And the second is that we will be socked to certified in May of this year, which is socked to it, if you're not familiar with, is the standard for, you know, essentially secure data tracking and an organizational process to ensure data security. - I'm sure you already know, but if you don't, so know it big times that you will hear security, security, security on sales calls to credit unions. - Absolutely. And that's why again, the first point of we're not taking any of that personally identifiable information into our solution, we can still give these recommendations and predictions with non identifiable information. - The odd thing about AI and related topics is a year ago, it was too early for most credit unions. Now, the title is switched completely where if anything, they're overwhelmed with AI offerings. And credit unions are overwhelmed as the credit union goes into paralysis. It's like, huh? Let's just table this. But let's wait to see what coastal does. Let's wait to see what Navy Federal does. Okay, fine. We're not gonna do anything until I do run it to some of that. - We do, but I also, you know, I think the counter to that is, it is becoming much more mainstream, right? I mean, you think about the number of, I listened to your podcast the, from a few weeks ago about the chat GPT and what credit unions should be thinking about that. It is becoming more mainstream. And so I think people are understanding and hearing it more. So they're, you know, I think becoming more comfortable with it, which is exciting. And I think that the opportunity is so great now to really help them serve the member better. And then these, you know, the realities of, we have to do more with less. And AI is one of those technologies that really can help do that. Are there a lot of offerings out there? Absolutely, right? And so I think that the credit unions need to be thoughtful about how it aligns to their, what are their strategic goals and what are their strategic priorities that they have on their plan for this year and making sure that they can make the connection to, you know, meeting and exceeding the goals that they've laid out. But I absolutely think that every credit union needs to be finding ways to put this technology into their strategic plans to serve the members. Because that, you know, I think they expect it. And it can, you know, again, help them do more with less. These seed a chat, GPT and Google's product, Bard, et cetera, helping you with your sales pitch or hurting you. Well, I mean, we, I can tell you that we're, our release that comes out at the end of this month, we're gonna have chat GPT integrated into our solution. And the approach that we're taking is, again, similar to like if you're familiar with co-pilot or what, you know, Microsoft is releasing with the office assistant, that it will be a assistant to a marketing analyst who says, okay, I've identified a group of members who are propensity for this money market product. Now I can use chat GPT to give me guidance on, okay, what should be a marketing campaign or a set of strategies that I might take and it will serve that up to them and give them a starting point of what could become a campaign for them. So we see it as a value ad for our solution. And, you know, this is, you know, this will be our MVP version of this or, you know, kind of our first release of this and we know it will evolve over time, but we absolutely think it can add value to our solution and to, you know, our credit union partners to, you know, help them, again, be more efficient in how they think about engaging their members. - So if I ask your tool and you identify for me how many members have a credit card that we've issued that hasn't been used in the last year, how long would it take to get an answer? - Well, so, you know, in full disclosure, we don't have that exact use case of how many, in the last year, but our goal is to say who are the people that have a high propensity and you can choose the level of propensity for a particular product? And in that model of determining propensity for, you know, this cashback reward card, one of the elements of the predictive model may be has another product that hasn't used in next period of time, right? And we let the, again, this is where we let the power of the analytic models determine what are the right variables to say, are you a good candidate for this new product? It might be, has another product that doesn't use, but it might say that's really not a predictor of likelihood for this product. So that's a kind of a long answer, but we let the power of, you know, all of these advanced predictive models that are out there now determine what are the attributes for your propensity for a new product. - When you're selling this, who do you talk to first to the credit union? What position? - So I think that we're seeing that credit unions have a chief growth officer or, you know, a head of growth. That's obviously first and foremost to us. Marketing is very much at the top of the list, experienced officers, CEO. So it's those people that are really responsible for growing member engagement and growing the credit union are target. Do you see the CEO playing an important role in the yes or no to this product? - I think so, you know, in our first couple of clients, they've been involved and they've been understanding of what the need is, but, you know, I think the reality is the CEO has a lot on their plate and some of them that are really believe in the power of new technologies will be interest in.
but I think every CEO that wants to serve their members better, grow their credit union more efficient and how they engage, this should be on their agenda. Well, my guess would be that with your first couple credit unions, it was almost necessary for the CEO to be involved simply because they were the first. Now from the CEO, I say, so who else is using it? You say, "Coast, oh blah blah blah." I'm okay fine. I'm not talking to you. I'm talking to probably my CTO or maybe my VP of marketing or growth. And if he or she says, "Well, blah blah, I'll get stopped. Okay, cool. That's all I need to know. You make your own decision. Give me a recommendation, we'll decide if we go forward with it. I don't see the CEO being much more involved in it than that." Right. I would agree with that. Particularly in these big institutions where they have the money to actually afford the higher good talent in those positions. So if you have a good talent and you're paying them, well, listen to them for having sex. Absolutely. This is, what gave you the idea for this offering? Well, my two founder and I, I was knowing each other a long time and we had worked at a large company that created and sold this kind of technology. Honestly, too a lot of the big banks and I didn't work in my background as an financial institutions. We worked a lot with retailers and consumer goods and retail is probably the most advanced one when they think about knowing their customers and thinking about targeted product and engagement. And so we learned in that retail world. And so we're bringing that to credit unions and it really grew out of early conversations with Coastal of how can we serve our members better. And we said, "There's something here, right? Credit unions aren't going to be most credit unions aren't going to be able to hire teams of data scientists. So how can we empower them and give them something that gives them all the power of data scientists and all of the big data solutions that are out there. But in a consumable way that they can really action on. So when you think about that, mid-size or billion dollar credit union, how can we give them a tool and a solution to really engage their members and serve them what they need to grow in their financial journey?" So that's where we kind of this came out of and again early conversations with Coastal and what we had learned in retail. Now we're seeing a lot of headlines about downsizing big tech companies. Does that downsizing include data scientists? That's a great question, Robert. And I don't have any hard facts about that. I would suspect that this, that data scientist, and I think if you both did a search out on LinkedIn, there are a lot of open jobs for data scientists. I mean, my data point is that we, the students that come out of the Masters of Data Science program at NC State and some of these other universities, they have over 100% job placement. So there's a very high demand and still very low supply for. That would be my guess. This is not effect data scientists. I have six months ago I was hearing from Credit Union CTO. They're staff were getting recruiting calls from places like Google, which is unthinkable. Google did not recruit data scientists from from from credit unions. That would have been like, you got to be kidding. But yet the shortage of talent was so severe that they were routinely getting that I had hiding calls from Google, etc. So that was that was my experience in the past is that data scientists are very high demand and the salaries that are being paid to them. It is hard to keep them around. They they're the rock stars of the industry right now getting pulled around and poached into different organizations. So that's why we think that we want to serve something that doesn't require you to have a team of data scientists. No, I like this idea. I'm basically you're you're selling a box full of tools and and if these tools are calibrated to solve common problems in credit unions and you know what I'm played enough with Google's bar to know that the the the number of the question the worst the information that you get. But this this is a user. It's not a it's not a barter data scientists. Yeah, it's so what you're doing is you're you're helping to minimize the amount of user silliness that produces just bad results. A year from now if we talk how many credit unions will you have. Our goal is to be at 10 a year from now. And how makes your staff? So today we are four people. We've got some growth plans for this year to continue to to hire. But yeah, we're we're still early days but we're pretty excited about the future. So an obvious question would be from credit unions. I say, okay, yeah, these two pretty big clients. Yeah, four people. What makes you think you can serve me if I come on as a client? Well, it's a great question. I mean, our goal is that we are creating a solution that will allow credit unions to become self-sufficient, right? So we're giving them a technology solution that you can put in a hands of your your marketing team and they can run with it, right? So we're very much of the we're empowering you with this with this solution and this experience that you can go know and grow and and enhance your member experience with it. So, you know, once these folks are up and running, we're ready to move on to the next one. Before we go, think hard about how you can help support this podcast so we can do more interviews with more thoughtful leaders in the credit union world. What we're trying to figure out here in these podcasts is what's next for credit unions? What can they do to really, really, really make a difference in the financial scene? Can't all be mega banks, can it? It's my hope it won't all be mega banks. It'll always be a place for credit unions. That's what we're discussing here. So figure out how you can help get in touch with me. This is R.A.J. McGarvey at gmail.com where we're at. McGarvey again and that's
[email protected]. Get in touch. We'll figure out a way that you can help. We need your support. We want you to support. We thank you for your support. The cu2.0 podcast.
PodcastTranscript AI