Generative AI is making waves across various industries, and credit unions are no exception. In this episode of “CU On the Show,” Brian Scott, Executive Vice President and Chief Growth Officer at PSCU Co-op Solutions, delved into how AI is transforming the credit union landscape. From improving customer experience to enhancing operational efficiency, the potential of AI is vast.
Generative AI: Transforming Customer Experience
- Setting New Expectations- Generative AI has raised the bar for customer experience, influenced by industries that have leveraged AI effectively. Credit union members now expect the same level of service and convenience. AI can dramatically enhance the member experience by offering personalized, proactive solutions.
- The Expectation Gap– While some competitors have begun implementing advanced AI solutions, many credit unions have not yet caught up. The challenge lies in meeting the elevated expectations of members accustomed to seamless, personalized experiences.
- Creating a Personalized Experience- AI enables credit unions to offer hyper-personalized experiences by leveraging detailed member data. For instance, knowing when a member gets paid, where they shop, and how they spend can help tailor services and communications to individual needs.
- Clean Data: The Foundation of AI- For AI to be effective, it needs clean, well-structured data. The credit union industry must prioritize data standardization and cleanliness to unlock the full potential of AI. This involves industry-wide collaboration and adopting best practices for data management.
Unlocking the Power of AI in Credit Unions
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- Proactive Financial Solutions- How AI anticipates member needs and prevents financial missteps.
- Fraud Detection- The role of AI in identifying and preventing fraud across multiple credit unions.
- Operational Efficiency- Using AI to streamline back-office operations and enhance overall efficiency.
Generative AI holds immense potential for credit unions, offering opportunities to enhance member experiences, improve operational efficiency, and stay competitive. To fully realize these benefits, credit unions must prioritize data cleanliness, embrace collaboration, and be open to experimentation. Listen to the full episode with Brian Scott to explore these insights further and discover how AI can revolutionize your credit union.
Brian Scott and PSCU Co-op are not affiliated with or endorsed by ACT Advisors, LLC.
Audio Transcription (pulled from the podcast)
Doug English 00:28
Well, welcome to CU On the Show, Brian Scott. And we’re delighted you are with us today.
Brian Scott 00:33
Hey, it’s great to be here. Thanks so much for having me.
Doug English 00:36
Brian with PSCU Co-op. Before we get into our discussion for today, tell me how did you first get started working with the wonderful folks in the credit union movement.
Brian Scott 00:48
It started as my college internship. So I was a freshman at Drake University. October of my freshman year, I got an internship that was with a company called the Members Group. And the Members Group was at the time kind of a startup company providing services to credit unions in the state of Iowa. And that was really my start. I was working in finance and accounting. And I think I was employee number 29 there. That company has, ultimately got acquired by Co-op solutions, and now is together in the PSCU family. So in some way, shape, or form, I’ve spent 30 years at the same company. And it all started from a college internship. So I did not know what a credit union was when I got that internship and fell in love with the business model with the people with the industry. So that’s my start is college internship.
Doug English 01:39
Well, so today, we’re gonna talk about some cutting edge stuff. First place, tell our listeners, what do you what is your current role at PSCU Co-op? And then what are we going to talk about today?
Brian Scott 01:49
Yeah, sure. So I’m Executive Vice President/ Chief Growth Officer. So I lead our sales our account management teams, our consulting teams. And we’ve actually branched into having a division focused on banking. So I kind of lead those four areas at PSCU Co-op Solutions.
Doug English 02:06
That’s a lot of stuff. That’s a lot of stuff. So that puts you in the perfect position to talk to us about some cutting edge technology ideas. So I know the first thing we talked about discussing is generative AI, right. It’s something we’ve covered a few times on this podcast. Let’s just start with how has it changed things? How has generative AI changed the customer experience and payments?
Brian Scott 02:30
First of all, Generative AI, AI in general is not a perfect solution for everything. And I think right now in the marketplace, a lot of people think about, oh, we’ll just solve that with AI. And one of the things that’s important to understand about AI is it’s fed with data. And whatever you feed into it, it’s really good at processing, but you feed in bad data, bad information, you can get bad results. But I think on the flip side, to answer your question, how has it changed? There’s an expectation among the marketplace that’s been set by other industries that have used artificial intelligence well, that it creates a dramatically better and improved experience for the end consumer. And so that’s what we’re dealing with, as credit unions is our members have an expectation that we’re providing that same level of great service, great support of flexible use all of these things when it comes to not just payments, but everything around the credit union. So it’s that big expectation gap that we’re dealing with.
Doug English 03:32
Can you come up with any on the spot specific examples of payments, improvements, or ones that are in development? What effects directly the member in this space?
Brian Scott 03:44
I can come up with a bunch of them. So I’ll just rattle off a few. How many times does a member walk in somewhere they go in to buy groceries, they’re at the checkout line and realize as they swipe their card, don’t have enough money. AI and AI tools in general. Yeah, you’re raising your hand say, Yeah, me, I’ve done that before. Everybody has, whether it’s unintentional, or you just forgot to make a payment, whatever it is, AI can solve that. You’re walking in, we know you’re walking into Whole Foods. You shop at Whole Foods every Sunday, it’s a data point about you, you spend about the same amount of money. And we can look before you even walk into the store, we know your location from your phone, we know what you’re going to do, hey, do you have the $300 that we anticipate you’re going to spend? If you don’t, I can give you a notice before you even walk into the store, hey, you normally spend $300 at Whole Foods every Sunday, it’s Sunday, you’re walking into Whole Foods, you only have $100. In your account. Do you want to move money from one account to another? Do you need to change your shopping habits today, like give you some sort of proactive warning, like you’re about ready to make a mistake that you don’t know that you’re about ready to make that mistake. That’s one example. That’s a super simple example. Another maybe on the positive side is, I’m a credit union member, but I have a card, let’s say at Chase or City or some large bank. Hey, did you know we have better rates on our cards than Chase/ City/ Wells, we see you paying off a credit card at Chase. If you move it here, we can help save you money. These are our rates. This is how you would fit into our structure. We can help you save that difference into a savings account, and help you just better your financial journey through life. And there’s so many ways that AI can positively impact that financial journey that somebody is on by knowing what are your habits, predicting what you’re going to do in the future. And knowing where are you on that curve on that journey? Are you going up because you just got a new job that pays you more? Are you going down because you lost your job and help you make those better financial decisions? That’s one of the most powerful ways that I think AI can positively impact the member throughout this.
Doug English 05:59
That’s truly exciting. Is that happening? Are credit unions delivering this member experience now?
Brian Scott 06:05
So are competitors of credit unions delivering that experience? For sure. There probably are not many, I’m not aware of any that are delivering that experience. But I think our competitors are, I know they are. And there’s probably a credit union or two that’s using it. I just don’t know about it. But it’s certainly not widespread. And it is certainly a massive opportunity. And this is where we talked about at the very beginning, that AI is changing the expectations for our members. And so this is just one example of those expectations that are being changed.
Doug English 06:37
But that’s a wow member experience, you know, like if a credit union or non-credit union can deliver that level of personalization and save a person either inconvenience because they brought the wrong card into the retailer like I do, like I forget that and oh yeah, that’s a work card can’t use that to buy the groceries, right? Or person that is actually running out of resources, and needs to react to prevent that inconvenience. That’s a wow member experience that I would think would really bump the loyalty. So is that something that maybe the largest credit unions in the country can deliver on? Or is that something that that your credit union partners are partnering with PSCU Co-op to do?
Brian Scott 07:22
Yeah, these are the things that we’re spending a lot of our time and calories as a CUSO as somebody who’s owned by credit unions, and you know, collaborating with credit unions to be able to offer these solutions at scale. So individual credit unions, maybe save for one or two or three of the very largest, probably don’t have the scale, the resources, the expertise to be able to do these things. Chase as an example, is spending billions literally billions with a B on AI technology. The credit union industry hardly has billions to spend on this kind of stuff. So that’s where companies like ours are really getting involved in leading some of this development so we can bring those solutions broadly to the credit union market space.
Doug English 08:06
So we’ve spent a lot of time talking about fintechs on this podcast, and you know, various forms of partnership, various forms of ownership. And I assume that PSCU Co-op has probably got some involvement with the FinTech industry in looking for some of those entrepreneurs to help accelerate this change?
Brian Scott 08:25
Yeah, one of the best things about our two companies coming together is the scale that we’re able to provide to the marketplace so we collectively become interesting to those fintechs. So if I’m a FinTech that started up something incredibly cool that can, you know, benefit credit union members, if I can come to a company like PSCU Co-op solutions that has 3000 credit unions that we touch, I can deliver my solutions as a FinTech to many, through a delivery mechanism a distribution network, like we have now. Versus going individually to 3000 credit unions. And I think there’s 4500, give or take in the entire marketplace. It gives us as the credit union industry, the scale to be interesting to those FinTech providers, and even to established companies that are creating great technologies. So I think that’s one of the benefits that we bring to the marketplace is we’re not out to make a huge amount of profit from this, we’re out to deliver solutions that credit unions need broadly in the marketplace, and collectively bring our scale to bear. So we can use that scale with those fintechs to drive in new technologies to the marketplace. Last year alone I think we examined 80 different FinTech companies and their solutions for both, you know, are they technologically fit? Are they great solutions for the marketplace? Can we integrate those broadly? So that’s kind of the role that we serve in the marketplace right now, around fintechs.
Doug English 09:58
Yeah, seems like, you know, that version, your version of working with the fintechs is one great way to bring scale to it. And then the Curql Fund and the interesting way they have that structure, and actually investing in the fintechs is another way and then TruStage is a third way that is also very much a scale players game, to be able to understand those things and understand their capital structure and figure out whether or not you want to participate. It’s a complex, very different world, right? Let’s switch sides of the fence. We started out talking about AI improving the customer experience. And I love your example there. How about payments? How about the payment side of things? What cost savings or time savings, or accuracy? How do you see it transforming payments and what’s happening now in that space?
Brian Scott 10:50
Well, first of all, fraud is one of the big areas, right? So fraudsters are using AI to commit fraud more frequently. And at scale. So it used to be you know, a fraudster finds a way to commit fraud. They don’t have ways historically to do that at scale. Now, with technologies like AI, they can do that. So we’ve built AI technologies on the back side, that actually look for, search for, prevent those types of fraud from happening in the moment. And so, as we’re again, aggregating thousands of credit unions payment data, we can see when a fraud trend might start happening for one credit union, and prevent it from happening for thousands. And we can look for those things like, Hey, Brian lives in Iowa and now all of a sudden, his information is being used to open accounts in New York and California. And because of our network of credit unions, we can see those things happening and find those links of hey, Brian’s data is being used here and here and here. Where individually, my data being used to open an account in California may not look fraudulent, but when we look at it collectively, we can say, Oh, we see what’s happening, and prevent that type of fraud. And so that’s just one way, you asked specifically around payments, that it can be used. But it can be used broadly in the marketplace as well. On the flip side, again, around payments in particular, how people pay is really interesting and important for credit unions. So if I’m paying with a card, credit unions make money off that through interchange through other sorts, if I’m paying via Venmo or other sources, there’s less fraud protection when I pay via Venmo or things like that. And the credit union is not making money. So I can enhance the experience by saying hey, we’ve built a payments hub and we can manage all of the payments you’re making. It could be billpay, could be I’m paying Doug for a drink that we had. It could be whatever. But that payments hub experience where I’m connecting all of my payments together and looking at them holistically, not just individually bill pay, who am I paying via bill pay? Who am I paying one off the guy to mow my yard via Venmo? Where am I buying groceries with a card? Look holistically at all the payments and create both a better experience. Maybe it’s creating rewards around those by rewarding you for making payments smartly through the channels that you should be making payments through, and then overall just preventing fraud on each of those payment channels.
Doug English 13:26
Now that exists or that’s in development.
Brian Scott 13:28
Some of that does exist right now. Broadly, that whole payments hub concept is under development. And, you know, it’s one of those things that that’s probably a long journey, because there’s always going to be new types of payments. And so creating the hub is in process today, making sure all of the different payment types as they develop over time Fed Now all of those things get incorporated into that, you know, that’s the ongoing development that we’ll need to keep doing.
Doug English 13:58
And is that core specific or you’re developing that independent of different cores?
Brian Scott 14:03
I think that’s the key is we in our industry, we have to be core agnostic. We have to be banking provider agnostic, we have to be able to serve and support all the credit unions, not just those that use a particular core, a particular digital banking provider, a particular loan origination system, whatever it be. That’s the magic is being able to work across and interface with all of those different third-party partners in the market.
Doug English 14:27
And difficult as heck.
Brian Scott 14:30
I think the industry has tried this numerous different times, CUFX trying to get a common API structure across all credit unions. And I mean, those things are great ideas. The reality is, it’s hard to do, like you just said, I mean, it’s really hard to get 4500 credit unions all on the same page with which API’s they’re going to use and to get providers. So, you know, it’s incumbent on a company like us to make sure we can integrate with all those providers.
Doug English 14:56
That’s a really interesting one. And the idea that as a industry, we’re so small, relative to bankers, that we need some commonalities in order to achieve some scale to predict member behavior to kind of get to the right place at the right time with the right member service. And that immediately makes me think of the data that you do already have with 3000 credit unions. And generative AI being able to help maybe with some predictive member behavior. The my shopping trip on Sundays to Whole Foods was a predictive behavior. I’m going to ask a question in a question. I hope you can follow that for a second. Because, you know, I used to be in a credit union branch. I was a members financial services fellow a long time ago. So the credit union branch staff would refer someone that had a deposit over a certain amount of money for potentially investing. And for the rare person that goes into a branch these days, that may still be occurring, but it seems like across selling would be a very appropriate place for a generative AI to participate, what’s going on in that space?
Brian Scott 16:08
So I think, first of all, you have to understand the types of data that go in and you started going down that path a little bit. There are really three big types of data, right? There’s descriptive data, data that describes who a person is and what they do. I’m a middle-aged white male, I live in the Midwest, I have five kids. That’s all pieces of descriptive information about me. Then there’s predictive which you started going down that path, the data is highly predictive around people, we go to the same Starbucks, at the same time, every day, we generally order the same thing. We fill up our cars with gas at the same gas stations, we generally put the same amount of gas in them. You can start to predict those things. Where I shop for groceries, what day I shop for groceries, do I go everyday after work, because I don’t plan ahead, and I just want to buy what I want that day for dinner, or do I shop every Sunday? And so that’s a second type of data, predictive data. I can predict with a high level of certainty what’s going to happen next. And then there’s prescriptive data. So prescriptive data is like what a doctor does, prescribe a solution to a problem like that example early on with the payments going in and saying, hey, we prescribe, you should move money from one account to another because you’re going to have a problem. So those three types of data, descriptive, predictive, and prescriptive, work together in AI to create those experiences. This is where this wasn’t your question, but I’ll go down a different path for just a second. This is where if you have the right data, and each of those, you could in some ways still make a bad decision. So, there’s a historical example from a number of years ago where Target took a lot of their data. They had descriptive data, they had predictive data and they prescribed a solution to a problem. And their prescription was the right prescription. It was just to the wrong person. So they accurately predicted that a 16 year old girl was pregnant, they did the marketing to her because they know if we market to a female who’s pregnant, and we can gather her shopping at that time, they essentially have a customer for life after that. Well, they started marketing baby bottles and diapers to a 16 year old girl, the father was upset about this, like, Hey, are you encouraging my daughter to get pregnant? Well, target knew before he did that his daughter was pregnant. They took the right action, marketing to her and marketing the right things, just to the wrong person at the wrong time. And it created a horrible experience for the family for Target in general, like it became a lawsuit. And so these are the things that we as an industry of credit unions look out, could we make bad decisions around loans who we market to? Are they at the wrong lifecycle stage to be marketing a certain type of loan to them? All of these are things it could be the right data, but it could make the wrong solution at the wrong time to the wrong person. And that’s where it’s really hard to manage all of those outcomes with AI. And so there has to be a healthy dose of kind of human intervention. Making sure is this really the right experience to the right person at the right time? So that was a way longer answer that I think you wanted.
Doug English 19:17
No, I want to go deeper because I think this is really interesting. I have the good fortune, maybe the misfortune depending on your opinion, to have a Tesla, and I’ve had it for five years now. It drives for me quite often, it hasn’t killed me yet and hope to continue in that way. When you put it in autopilot, it drives away. And then if you take it out for whatever reason, it wants to know why. Why did you take me out of autopilot? What was wrong? And it just wants you to talk to it to give the Tesla that feedback. I imagine that sort of a loop from the members, especially for those that might like to kind of be on the front end of things like me might opt in to be a part of things like that. So again, I’d say, is anybody in credit union land up to this yet? Do we see this happening? Is this also in development? Or is this in the action stage?
Brian Scott 20:04
Some of this is in the action stage. So as we get to things like lending, we can be really good at predicting outcomes, whether it be of auto loans, personal loans. We can get really good at that. And I’ve seen a lot of credit unions do great jobs around predicting auto loans and who’s going to default and who’s not by the type of car they’re buying, and they’re using all that data. But again, it gets down to, could I be using that data and could it be discriminatory? And I don’t even know it. Could it be causing me to make loans to a certain type of population more than another? And those are the types of things that I think, again, the data can only go so far in that. It can’t go all the way to, is this causing me problems that I may not understand further down the road? But when you get to that, to your Tesla asking, you know, is this the right thing to do right now, why are you doing this? I think that’s where we as an industry have to be asking those questions and open to that feedback that we get like, oh, yeah, my loan losses have gone down, because I’m making better loans. But am I negatively without knowing it impacting a certain population that I don’t want to be?
Doug English 21:17
The data, as you said earlier, the guy go principle? Is the data clean enough and credit unions to be able to do this? Are we past the stage of the stuff we’re putting into these predictive models is clean enough and broad enough to get good predictive outcomes out? Do we have enough? Or is that the value of putting 3000 credit unions together? Tell me about that?
Brian Scott 21:40
Yeah, the simple answer is no. And any one individual credit union might have their data clean enough. But I would argue and I think most people would agree. One credit unions data is not enough. For sure it’s not clean enough as an industry. You think about again, take 4500 credit unions or whatever the exact number is. Is their data formatted the same way? Are they collecting it the same way? Are they ensuring that it’s stored the right way? Absolutely not. Could one credit union be doing it the right way? For sure. And is that enough for them to be sure about the data they have to be able to use it in a tool like this? Probably not? I mean, you’d have to be a very large credit union, a Navy type size credit union, for that to be true. We’re not there yet. Certainly, when it comes to the cleanliness of the data, the storage, how it’s tracked, how it’s entered, how it’s gathered. All of those things are inputs into having clean enough data to make AI truly useful.
Doug English 22:40
Yeah, that’s step one. All this stuff we’re talking about predictive and prescriptive is two and three, like we’ve got to have clean enough data to predict that we’ve got to have no scale to predict. So how do we get how do we get there? Do you have standards that you’re suggesting for how credit unions collect the data or any other characteristics that I might not understand about the way the data needs to be used or controlled or kept or any of the characteristics?
Brian Scott 23:04
Yeah, again, I think this is where we, as an industry it’s incumbent on us to truly get together on something like this. So we can use that data and we can use it for the betterment of our members, we can use it for the betterment of the entire population, but first and foremost for our members, and make sure that we don’t use it in a way which is harmful. Whether intentionally or unintentionally harmful. You are spot on that is job number one.
Doug English 23:29
I’m going to be exploring that in future podcasts. I’ve had some guests in that world that I didn’t ask because I didn’t know it was a problem. I think we’re gonna have to get them back on here. CU Collaborate very much comes to mind, as a entity that is doing some work in this area. I need to find out like, alright, so how are we going to help this industry get clean data? What do we need to be doing there?
Brian Scott 23:50
And then actually have the right tools to be able to use it the right way and make sure we have outcomes that aren’t the Target example from a few years back.
Doug English 23:58
Yeah, all right. So let’s move on into some of the other areas of operations, financial analysis. Talk to me about what you see currently happening in generative AI in those areas.
Brian Scott 24:11
Certainly, when you think about back office operations of a credit union, I don’t think there’s a credit union out there that would say they’re as operationally efficient as they need to be. There are just so many manual tasks that happen behind the scenes at a credit union, that not necessarily put people out of jobs, but to make more efficient. So the people that are there can actually do the things I mentioned earlier, like, are we taking the right actions to the right people at the right time? Are we creating the experience we need and want to? Instead of, I’m just a person who crunches the data and crunches the numbers and runs a formula and runs a spreadsheet to come up with those answers. So I think there’s a huge advantage or use case for AI in the back office and creating much more efficient operations. You look at 2020 through now, all financial services but credit unions in particular, have ridden a roller coaster of markets changing dramatically. All of a sudden, they’re flush with cash and looking to make loans, then they make a bunch of loans, the market tightens up rates go up, and now it’s like, oh, I just made a bunch of loans that are lower than market now. And so this roller coaster that we’ve been on, could AI have helped predict some of that, could those efficient operations had people looking at it saying, ah, maybe we’re out in front of our skis here a little bit instead, it’s like, we’re just trying to respond to the market instead of where’s the market taking us and really be looking at those kinds of things. So many examples of where AI from a back office operations perspective, could have a dramatic impact on the market.
Doug English 25:50
Yeah, now, I’m expecting the answer that no one’s doing that yet right?
Brian Scott 25:57
Right. No one’s doing it effectively otherwise, we hear about it. By the way, there’s a lot of large banks that are trying to solve that, too. So this isn’t just a credit union problem. But as we look at the potential use cases, man, that’s an incredibly use case, you know, we’re using we’re using retroactive data to tell us is somebody doing something right? We’re not looking at anything proactive out into the future.
Doug English 26:19
That of course, makes you think of the regulators. So, two questions. One is, are there regulatory hurdles to integrating AI into some of the operations? Like, where are those and what’s being developed? And then, we’ve all heard that the chat especially will create absolute garbage sometimes, just complete falsehoods. How do you how do you deal with that?
Brian Scott 26:40
First of all, regulations are super interesting to look at, and how different countries regulate technologies. Take Australia, as an example, Australia was very proactive in regulating use cases for AI. And the US, we take a more referee approach, like, we’ll tell you what you can’t do, we won’t necessarily tell you what you can do. Where countries like Australia, they’ll say, hey, there’s this new technology, it’s AI, here’s ways in which you can use this technology. We say a lot in the US we’ll tell you what you can’t do, and then we’ll watch what you do. And if we don’t like that, then we’ll create more regulations around that. And we’ll have bodies like the CFPB, that are the referees sitting there looking like, oh, I don’t know if I like that. So I’m going to do something about it. It would be much more helpful for the entire market for consumers in general, if our regulatory bodies would look at let’s tell you what you can do with these things. Let’s tell you the areas where you can play with these. And then it would allow industries, companies, Fintechs, technology companies to actually create knowing what’s allowed. Versus only looking at what right now is not allowed. I don’t think it’s any secret that regulations certainly in the US are much more retroactive. You look at some countries, like a lot of the Nordic countries, they outlawed checks years ago, right? That’s a very proactive thing to do around, like, hey, we can provide you a better experience as the end consumer if we outlaw checks. And actually use money movement technologies that are in place today. Let’s do that, and create a better experience. So that was kind of a long way of saying, we don’t have the types of regulations today that we need to truly use everything available in AI at scale, and to use it for positive things. And I think a lot of people are super nervous, like I could go create something but am I going to be told later that just one regulation could put me out of business? I’ve talked to a lot of fintechs. I mentioned earlier, we look at somewhere between 70 and 100, I think it was 80 last year, fintechs. And almost to a one the founders, the CEOs of those companies said their greatest competition is the US government and what they regulate, and they could be regulated out of business, essentially overnight. And that’s their biggest fear. And it’s I think it’s the fear for anybody who’s creating new technologies is, what happens if there’s a regulation put in place that puts me out of business?
Doug English 29:13
Let’s bring this to a couple of closing ideas around hyper personalization. Maybe you’ve already touched on that with your shopping example. What does it mean? How is it shaping consumer interactions? What’s coming forward in hyper personalization?
Brian Scott 29:32
First of all, your dentist, your veterinarian, they’re personalizing experiences. Your dog gets a birthday card when it’s your dog’s birthday. Your dentist tells you when it’s your birthday, because they want you to come in, right. Your veterinarian, your dentist know a fair amount of information about you and they’re using it for good. They’re personalizing the experience you have by sending you a birthday card. Super simple, super easy example, how many credit unions are doing the same thing? Very, very few. The example I use a lot is I sat down on an airline seat, and it welcomed me to the seat. Welcome Brian to seat three II. My airline seat knows the movie I was watching on the screen the last time I took the flight. Do you want to pick up watching the Jason Bourne movie you’re watching? Of course I do. Is it that hard though to do that to provide that experience? I easily know who’s sitting in seat three. It’s not super hard to say welcome Brian on the screen. And all of the data that comes out of just me touching the screen and watching a movie, super simple piece of data. You were watching Jason Bourne, you didn’t finish it, do you want to finish? Like that type of personalization makes me feel like the airline knows who I am. You take that you think about all the data and information a credit union has on their members, I know when you get paid, I know how much you get paid. I know where you shop. I know the grocery stores, when you go. Think about all of the opportunities we have to create personalized experiences. So that when a FinTech, another competing financial services company comes in, I’m not oh, these guys seem like they know me really well. And they’ve never met me before. Versus, hey, I feel like the credit union really knows who I am, not only did they send me a birthday card, but they know, when I get paid how I spend money, when I should make a payment to my mortgage, they’re helping me manage my personal financial journey through life, and they’re doing a good job at it. That’s the type of experiences those hyper personalized experiences that I think could attract and retain the numbers that we have, and create those experiences, that honestly, they’re not that hard to think of creating those experiences, but right now, nobody’s doing it. But our competitors in the marketplace. And our competitors aren’t always financial institutions, you know, that could be somebody like Starbucks, Venmo. I know, I shouldn’t keep a balance of money in my Venmo account, but I do. And, like my credit unions looking for deposits, and I’ve got some deposits sitting in my Venmo account, like, it’s those types of things, like, if I was thinking credit union first for everything, because they know me, I wouldn’t do that. I wouldn’t leave $200 in my Starbucks account, I’d buy it every time. It’s those kinds of things that, again, the credit union could be creating a much more personalized experience that makes my financial journey through life way better.
Doug English 32:29
This, this topic is everywhere, it’s very noisy, and it’s hard to cut through all the things that a credit union executive needs to be paying attention to, to find the actionable ideas that are actually going to take flight. Like any suggestions for our listeners as far as where to listen, credit unions maybe to follow, some best practitioners in the space, or conferences, ideas, just anything, you can provide our listeners for ideas for how to find the real actionable ideas in this noisy, noisy environment.
Brian Scott 33:08
So I’ve got a million of them, one of the first things I would recommend is go to conferences, conventions, outside of financial services. I’ve talked to a few credit unions, they go to the National Retail Foundation, they’re looking at what’s retail doing right now to drive consumer behavior to change consumer behavior, to bring more people into branches. You look at places like Europe, they’re really focused that we want consumers in our stores. And we’re going to bring them back to the store by providing them a much, much better and differentiated experience in-store than they can get digitally. And it’s not because they already have this expensive footprint. I mean, part of it is, I suppose, that they do. But it’s, if they can provide that better, higher touch in person experience, they can actually sell more and do more and do better for their consumers. And it may not be I want to make the sale in the store, I want you to come in and touch the merchandise. I don’t care if you still buy it online. I’m okay with that. But if you come in and touch it, you’ll like it more you’ll buy it, you may feel inclined to pay more because you’ve come into the store and touched the merchandise. And you know, think about a credit union. What could you do in branch to provide a differentiated way better experience? And so, you might get those ideas at the national retail foundation or those conferences and conventions that are outside of our industry. So that’s the first thing I’d say. The other is you have a ton of great experiences inside your four walls of your credit union, talk to your employees. What are the experiences they’re having at different online retailers with different fintechs and how could you think about testing those? You don’t have to be a multibillion dollar credit union to test and experience at one teller line. Any credit union try out a new experience inexpensively at one teller line, at one loan officer, and just test and try those experiences. If you try it with three members, and it doesn’t work great, hey, we try that. And I think there’s a little bit of an idea that we can’t try things if we don’t have a bunch of money. And I think there’s a mindset that we have to wrap our heads around as a credit union industry that says, we can try and create new experiences and tests without having a lot of money. Sometimes, a restriction, like not having money, or not having time is actually a benefit to the creative process. So I’d say those two things, I could come up with a bunch more, but those are two that I think could really benefit credit unions in particular.
Doug English 35:49
That was a great one, Brian. Very much what we’re looking for is some ideas that are out of the box that are saying, okay, credit union industry, this opportunity is here, and we need to find ways to go after it. And it’s not easy to figure out what to do. And maybe my biggest takeaway from this conversation is, we need to go after the clean data starting point, before we go too far down the path because we can’t get to running if we’re not crawling first. We gotta start out with clean data. So I’m going to try to dig into that in a future podcast and maybe Brian Scott will have some ideas or even join me again.
Brian Scott 36:26
For sure I’d love to. This has been fun.
Doug English 36:29
Awesome. Well, thank you for your insights. Thank you for your service to the credit union movement. I enjoyed our conversation, and I look forward to talking to you again soon.
Brian Scott 36:37
Yeah, thanks so much. Take care.
