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The CU2.0 Podcast
This podcast explores contemporary, critical thinking and issues impacting the nation's credit unions. What do they need to be doing to not just survive but prosper?
The CU2.0 Podcast
CU 2.0 Podcast Episode 375 Casey Boggs on AI as the New Super Villain
“Prepare: AI is the new CU crisis super villain.” That’s the title of a recent CUInsight story authored by Casey Boggs, founder of Reputation US, an d of course we had to get him on this podcast.
Understand, I am a strong supporter of AI in general and AI in particular inside credit unions. This is a life and death matter.
Yet there is a possible downside to AI and we already know that the main AI tools have played substantial roles in teen suicides, in creating false “facts,” and in many other ways leading humans astray.
While I may disagree with many of Boggs' conclusions, his core advice - proceed with AI cautiously and thoughtfully - is on the money.
Only fools rush in.
Read Boggs’ CU Insight piece, listen to the podcast, read a recent CUInsight story by me where I explore CU use of AI with CU2.0 founder Kirk Drake, and keep on learning and experimenting and using AI.
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SPEAKER_00:Hi, and welcome to the CU2.0 podcast with big new ideas about credit unions and conversations about innovative technology with credit union and fintech leaders. This podcast is brought to you by Quillo, the real-time loan syndication network for credit unions, and by your host, longtime credit union and financial technology journalist Robert McGarvey. And now the CU 2.0 podcast with Robert McGarvey.
SPEAKER_03:AI is the new CU Crisis Supervillain. That's the title of a recent CU Insight Story authored by Tasty Box, founder of Reputation U.S. And of course we had to get him on the podcast. Understand I'm a strong supporter of AI in general. And AI in particular inside critics of this is a life and death matter. Yes. Yes, there is a possible downside to AI. We already know that the main AI is falsified substantial team suicides and false facts and many other ways of leading humans of strife. Well, I may disagree with many of boxes, conclusions, of course, but see what AI costously and thoughtfully is on the mind. All the fools are too read boxes to you insight, please just a link to that in the show notes. Listen to the podcast. Read a read this to you inside story about me or the force to use the way I was to you to the old founder portrait is a link to that story in the show notes. And just keep on learning and experimenting and using AI. It's the future. Learn how to use it intelligently and wisely. Now, what kinds of AI disasters or mini disasters or acts of bond villainy are do you anticipate seeing?
SPEAKER_01:Yeah, anticipate and already seeing, I would say probably in three to four buckets here. One is just simply the cybersecurity and data privacy is a big piece of this. So the world of cybersecurity has been going on for quite some time, right? And there's been no shortage of an industry that has been there to stave off these bad actors, as they call them, or malicious actors that are trying to essentially come in and take data and ransom your information and try to make money, right? So similarly, you know, this emerging AI is really uh mishandling member data. And you know, they're manipulating the information so uh that they're just simply trying to uh gather something of value from the credit union that is you know that at some point the credit union has to want that back. And so using AI in different forms to uh penetrate those uh those the member data or uh financials, that's where we're seeing probably the most penetration early on with AI. And then another subset to a little bit of that is deep fakes. And to what degree the word deep fake is essentially just a way to falsify uh a person's voice or image, and we're seeing more of this just to falsify when we get an email or a voicemail or sometimes even video of a person that pretends to be someone from a credit union saying, hey, uh give us a call, give us your information, your social security number, your uh financial information, and that I haven't seen it as much, but it is still happening, and that will essentially cure losses for the members because they think it's someone from the credit union, right? And they're getting they're going after the vulnerable folks. The um the bad actors are going after the folks who might be vulnerable or um I don't know naive or just simply um, I guess crazy enough to give their information to someone who seemingly is the uh executive or member services person that they know, trust, and like. So that's uh that's one we're seeing as well. Another one in all this is lending discrimination. And that one is about it really fits in not only credit unions but banks as well and just other folks who are in the risk business area. But AI models um you know really they look for uh these uh loans or impose unfavorable terms on protected groups, and uh with that becomes uh there's scrutiny behind it because they are uh being biased or there's discriminating against members of a certain class or marginalized group. And that's a big no-no. And uh and that happens to be something that not only from a reputation side, but certainly from a regulatory and even lawsuit-wise, could be uh bad for a credit union.
SPEAKER_03:Now, obviously, discrimination lending was invented by human beings who to some extent perfected that until some federal laws came along and said, no, no, you can't do that. It's uh I don't see why the machine can't be taught that it can't do it in the same way that banks and credit unions have taught their lending staff that no, you you can't make decisions based on race, race, age, blah, blah, blah. It's uh um that shouldn't be that difficult a thing to teach the machine.
SPEAKER_01:You would hope not. You would hope not. I think it still applies because this is when human uh oversight is very important. Uh so like a checks and bounces. So if you are crediting and using AI for the first degree of um determining if someone is a risky or non-risky lender or you know uh client, uh, that's one part. But if you're not necessarily going underneath the hood to find out more information about this person, uh and you're taking the AI recommendations for what it's worth, then that's on you. That's on the credit union for not doing its due diligence. But part of the the missteps that are actually happening here is you know, humans are becoming, dare I say, lazy or don't do their due diligence and using the things that are afforded to them, like you know, AI tools, uh and um the checks and balances of this um are in jeopardy, which essentially, again, on the fault of the credit union or the the lending bank.
SPEAKER_03:Yeah, that that said, we use machines to make a lot of credit decisions. I mean, I applied for a new credit card uh a week or so ago. And within a minute, I was approved. Um how did they approve that? They got my FICO score, period. I mean, there's there's no other way they could have gotten any information about me that was useful in that time frame, other than uh a FICO score. And they said, oh wow, we'll give this guy a credit card, of course.
SPEAKER_01:Right. I think it might be I think that's you know, that's credit is one part, and then the other aspect is just lending money, you know, you know, the equity line of credit, you know, lending money for a car loan, that type of thing. Uh for just a credit card, I think that is automated based off what you just said. Uh you know, numbers don't lie. If they have you know FICO score that's you know in the 700s or above, that immediately qualifies you to take out a credit card.
SPEAKER_03:But I think it gets a little more sophisticated when it comes to well, also like the credit card, you might have a$10 or$20,000 credit limit, but it's highly unusual to take that card the day you receive it and run out and put the maximum amount out. I'm sure Kirks would do that, but that's not normal. And if you're missing payments along the way, at some point they pull a plug on it. That's right. And they're not gonna lose the whole amount in most cases. So, I mean, yeah, credit lending, this has been going on, credit cards have been going on for at least geez, I don't know, 60 years. They've gotten very good at this. And uh a lot of it's machine learning, though, that's actually pretty good. I mean, I give the example that if I walked into an Apple store in Detroit, Michigan, and bought three super expensive um uh Apple computers, that charge would be declined uh by my Apple card, which would say Bozo's never been to Detroit. He don't know where Detroit is. And furthermore, he's never bought this kind of computer, he doesn't seem to like it. Uh and that would be a smart move on that that machine's part, saying, nope, decline. There is there are places where the the the learning makes sense, you know, to use the machine learning. That's right. And you know, an issue that interests me is that let's say you apply for uh uh$75,000 home equity loan, and I'm looking at your application, and I say, geez, I don't know if I should say yes or no, but I'm a smart guy. So I take your name out of it, but I put a lot of other information into Chat GPT and say, should I give a should we give a loan to this guy? Question What happens to that data? Now, Google says we will never train the system on your data. Your data is your data. That's what Google says with with uh uh Gemini. ChatGPT, there's a toggle you can set which says, Don't use my data to train. Can you rely on ChatGPT? Well, chat GPT has made some egregious mistakes in recent days, recent weeks. Sure. Advising uh maladjusted teenage boys about how to commit suicide, and some do at least.
SPEAKER_01:So right. It's a whole nother crisis for sure. Um because not just corporate parts, but there's a lot of AI issues uh surrounding those matters. Yeah.
SPEAKER_03:Well, so that that that raises the question of can we trust their word? Can we trust their ethics, which is uh scary stuff to bring up, but in the case of the teenage suicides and the advice, I have advice and and quote marks that have been given by by the machine. You say, wow, this is not this is not good advice. Yeah, you don't give a 13-year-old who's depressed tips on how to commit suicide. One doesn't do that.
SPEAKER_01:Or in general, you know, I'm a father of twins, uh, teenagers, and not my teenager per se, but uh in general, uh kids are looking for important counsel on where to go to college, uh what to do in certain circumstances. Uh, when we as a parent were the that sanding board based on our experience, now they are looking to these you know AI tools for pretty much everything, personal or otherwise. And that gets very uh challenging because it looks good, it seems logical, but doesn't necessarily fit into what they know about the human being, uh, that teenager. Uh and so uh information is being shared while it seems convincing and well thought out, or at least it is uh curated to something that uh bite-sized for the teenagers, it doesn't factor in that teenager themselves. There's so many different variables that fit into that. Um, and it's you know, to rely on AI to provide those insights is so limiting. Very much still, I don't care how much information you put into AI, it just cannot uh go into those nuances of uh the human capital and the uniqueness that every person is involved, including like you know, go back to the credit union line. If you're if you're providing uh lending to someone, there's a story behind those people, right? They might not necessarily have the best credit for whatever reason, but they're doing other things like you know, looking for work, they are doing four or five different jobs, they just had you know uh some other issues that prevented them from having a good score. So credit union and knowing a little bit more about that human being on a one-on-one basis, they'll say, Yeah, uh I get their circumstances, I'm willing to give them this this loan. Uh, if you based purely on just the numbers or some other factors, then the biases will will surface.
SPEAKER_03:Well, that's you're talking about the old credit union tradition. I know when Jim Blaine was CEO of state employees at the Credit Union of North Carolina, he adamantly insisted they would lend to the human being, not to a piece of paper. And yeah, if you'd had uh a health crisis and you miss missed paying a few bills here and there, that would be taken into account. And you might get the the money to buy a car that would let you get work drive to your job. You might get that from St. Employees because the judgment would be based upon the human, not the fact that you had a run a little run of bad luck. That's right. A lot of credit unions have moved off of that, unfortunately. But what can I say? I don't run a lending committee on a credit union. So yeah, and it it's harder to factor the human stuff in. You have to talk to people, you have to call for the person's foreman, like in the old days of the select employee groups. You know, tell me about them. Is it reliable? Well, you know, that kind of stuff.
SPEAKER_01:So well, I do think that and one of the areas that we brought up as well that yeah, the the the chatbots, you know, are efficient, right? But members um are often perceived them as cold. And I don't know about you, but if I call up uh a company and it's always simply get as an automation or even a an online version to give me the answers, it's really impersonal, or there it's limiting, or it's got you know air written all over it. And if you are perceived as an institution that relies solely on AI-driven chatbots, you are impersonal, right? And the member might resent you, or might oh, I can get this anywhere, where the pendulum is gonna hopefully swing from AI to people who are authentic. That's where I think uh there's gonna be more of an appetite. As I I'm you know, I'm guessing here is purely on opinion, but I think at some point we're like, all right, this is ridiculous. I want to talk to a human being, I want to go into the branch, I want to go and meet these persons because there's too much at risk here. And if they're only going to be a lender or a relationship manager purely based off of numbers and using AI just to make money, then that's not going to be my financial solution. I need a human being.
SPEAKER_03:There's debate within the credit union world as to what are the best tasks to first pursue with AI. And to me, the winning argument is to use AI on not on customer, not on member-facing stuff, but to use it on back office stuff. The most interesting example I know of a successful AI project is that one Navada credit union. They took all of the information that call center people consult when you call up with a question, fed it all into AI, aiming to simplify simplify. There were many several documents often dealt with the same topic, sometimes gave contradictory advice. I mean, this is information collected over 30 or 40 years. I mean, I'm I'm sure I contradict myself over things I said and wrote 20 years ago. So they cleaned all of that up and uh have uh resulted in terrific uh time savings. So instead of an employee sitting there flipping through pages, getting confused because they're contradictions, you just ask the machine when will we repossess this guy's car if he misses payments? Within 10 seconds, you got an answer. So the the member's not sitting on the phone for three minutes as you dial around. Boom, answer. So does the member know that AI has played a role in this? No, why should they? It's irrelevant. You're you're still talking with a person. I mean, you don't I assume you don't have any issues about those kind of projects.
SPEAKER_01:Not yet. And I and I say that because uh those probably aren't as exposed because if it is it is an error uh that's going to be tracked probably internally, and I can't imagine any institution is going to track and tout some of the errors they've done internally, or they're they're seeing some flaws in that system. They might you know uh talk to the vendor who they're using or the AI aspect that they're they're using and saying, hey, this this does have flaws, but I don't think it's one of those things where they're going to uh uh highlight some of the you know inefficiencies that are taking place that's gonna be forward-thinking to you know the public or uh prospective members.
SPEAKER_03:So I know another place where AI is having significant impact at credit unions is automating collections. So if you're late, instead of a human being calling up, leaving a message, because you're not gonna answer the phone, sure, a machine will call up and leave the message. And one of the most interesting things about that is there so far I have not heard of any collection staff complaining about this because usually most of the people doing that find it to be um work they don't really want to do. And these credit unions have not had any staff reduction. So they've given people other tasks to do. So it's not like, oh, we're gonna fire you and replace you with a machine. It's uh, hey, you don't have to make those collection calls anymore. How's that? You know, wow, this is great. I came in, I had to eat Tums all morning before I made my first call. No, this is wonderful. Thank you. God, I love AI.
SPEAKER_01:There's there are some good, yeah, there are some good parts of AI for sure. And uh it's not primarily to poo-poo on it. In fact, uh it's gonna be an important uh engine to different aspects, you know, and augmentation to uh things that we're already doing and taking taking the lead on. There's no shortage of examples to that end, uh, but it's it's not where it needs to be at all. And I'm not sure if it'll ever be, but in the same vein, because of something that's new, there's going to be malfeasans, nefarious actors out there, uh, and folks who are essentially looking to game the system. And that, you know, based on what we've seen so far, that's already happening. And it's gonna probably be more sophisticated and it's kind of scary. And so part of what we're talking about today, about you know the AI uh crisis of sorts, is that you need to be vigilant. You know, what's going on right now in September 17th, 2025, uh is likely gonna be a lot more different than September 17th, 2026. And uh it's it's it's scary, daunting. Um, it's going to be a little bit exciting, but we all have to be participants and being vigilant and being ready because we don't necessarily know what we're gonna be um up against. Right. Yeah. Crisis prep is gonna be important and not just for preparation parts, and that's what we advocate for, but I think just you have to get educated to it. It's one of those things where when I talk to someone about what is your degree of uh knowledge of AI, adoption, and other things, it's still a slow burn for a lot of people, and that's okay. Uh, but for companies that are dealing with records, uh medical records or financial records or just information or there are in the business of customer service, this is has to be paramount because there's a lot at stake here. And in one little slip or uh mishandling of how you uh handled the situation, it's going to be a dark mark on on you uh as a company. And that's what we're just telling folks and scream from the rooftops now is that it it is here. Keep an eye out for it, but not just simply like waiting for the crisis to happen, but having you know safeguards in place. And that's essentially having almost a team ready for being vigilant, uh, ready to prepare, and to hopefully to mitigate against any of the damage that might come your way.
SPEAKER_03:Well, go back to 1995 when every company's rushing to be online. They pretty quickly realize that I I don't want you to take your personal computer go down to local Starbucks, use their their Wi-Fi or whatever they had at that point, to get online and dial into my mainframe and start downloading stuff. I just don't want this to happen. And they implemented ways to make that not happen. For instance, you have to use a VPN, you can't use a personal computer, you know, stuff like that. A whole bunch of steps designed to keep the connection and thus the data secure. I I see companies needing, I think they need help, but I I just see companies rushing to figure out how, okay, we're gonna use AI, how can we do it more securely? Um and I think you're right that the answers to that question aren't that clear right now. It's we're so busy getting the system up and working. Uh security, we'll worry about that later, man. I mean, who worries about that in Silicon Valley at the beginning? You know, we worry about it later.
SPEAKER_01:Yeah. It's uh you know, selling preventative medicines uh a tough sell in general. Uh we just need to get up and uh get up and ready. And if uh something goes sideways, then we'll go sideways, we'll deal with the ramifications. Uh however, those are costly maneuvers because there's a lot at risk here. That's the challenge on so many different aspects. Um, a couple of different things, Robert, you know, like uh about being prepared or not, that um, you know, I have a few stats that we have on our website about AI, but according to this Oxford uh metrica, uh companies that handle crises poorly lose 15 to 30 percent of their market value within days. So, you know, that's a that's a lot. So, you know, the whole idea, let's we'll just handle it when it comes in. Well, if uh you're you're gonna lose 15 to 30 percent of your market value when something happens, uh, are you okay with that? You know, so that's the the the bell we're ringing here. That preparation doesn't really cost you much, but a crisis costs you a ton. It's really important for uh organizations, AI or otherwise, to be prepared. And I'm just shocked that most aren't. Yes, they might in the credit union world, yes, they might have a um, you know, some in you in C UA um disaster recovery plan in place, but that's more lines of a checklist they have to do, but no one really knows where it is or what it's all about. But to actually practice on it uh and be prepared for it, um that's the the key element that we find daunting because we're called in the in the uh 11th hour or we parachute in during a crisis and like, okay, let's just take care of the situation. Hopefully we can mitigate against any damage. But um, at that point, we're the firefighters putting out the fire as opposed to we could have really uh saved a lot of the embarrassments, uh a lot of the data and materials if we were just prepared. And I'm not just saying it's just because it's a part of our job, it's just real, it's just sad that um most organizations aren't prepared for a crisis. For some reason, they buy insurance for things that are that might happen, but uh for reputation, which actually, of all things, reputation is an insurable asset, they are they're willing to roll the dice on um the that kind of damage, and that's proven a bad move.
SPEAKER_03:To my knowledge, uh NCUA hasn't issued meaningful documents regarding AI usage and credit unions. I might be wrong on that, but I've been talking to so many people recently so often about NCUA. I don't think I am.
SPEAKER_01:Well, you're you're right. Um uh in fact, I was just just before our meeting today, I was on a AI conference call with Beasley's, which is a you know insurance carrier, and they were underscoring that there aren't much regulations right now at all. Uh in the Europe and the EU, they have quite a few already, which is good. There's some oversight, but in America, there's not much. And then if you get a little more granular, like a credit union industry, there's not much at all. I can't speak specifically of uh every industry, but I think it's still so nascent and uh probably still up for debate to what degree it can be regulated, that it's still the Wild West, which when things are not regulated or there is no oversight or really strict policies in place, that is definitely a prime opportunity for crime and just malicious uh activity.
SPEAKER_03:The unfortunate thing here is that this sets it up for the uh mega banks to get a bigger lead over smaller institutions like the unions. Because Chase and Bank of America have so much money. Many years ago, I talked with the guy who was charged with uh building the first mobile banking app for Chase. And he went out and he recruited like the Kansas City Chiefs of ITEC. I mean he had so much money to spend. You know, this was we gotta do it, we're gonna do it. Like they had a real short time frame, they wanted it done. And uh no credit union compete with can compete with that kind of bank book. It's uh and these guys, when they see a need and there's urgency, they spend money, money, money, money because they got it. Right. I don't see any credit union having that kind of deep pocket, unfortunately.
SPEAKER_01:Yeah, it it's uh it's the the nature of the beast in general. Um the bigger companies are putting money toward this and preventative measures and to safeguard and it may be a false sense of security or maybe a right sense of security. But if I'm a customer or a member, and probably one of the most important things in my life is the safety of my money, and I'm looking at it over time, I'm probably gonna start gravitating toward security than even the friendly customer service. Like, you know, the Sally knows me really well. That's that's important, but you know, more important than that is security, false or otherwise. So these larger banks are all in on putting that extra layer of protection, and that gives us as consumers some degree of comfort.
SPEAKER_03:And knowing Sally the teller and standing in Sally's teller line is something that your teenagers will never understand and never do. There might be an avatar named Sally that they're dealing with. Yeah, but it's not quite the same as knowing Sally and knowing where she lives and how her kids are doing, and she knows how your kids are doing.
SPEAKER_01:Yeah. And that might that might change when you you become, you know, you hit 30 and 40 years old, and you kind of see this in that that precious millennial age that they're now in their 40s now, that uh they're they were so doted on, uh, and their their mindsets were so I don't know, progressive and have a change. But these folks become adults and they they want things that we now want as we we grew up as well. So I still think human touch is gonna be important, authenticity, and uh that's gonna be uh a critical partnering. As I mentioned, the pendulum swing to the one-on-one and personal information. I think that's gonna be needed. What is gonna probably be lacking is that that firewall of protection uh for your for money or the perceived protection that that might be the the game changer and what people would will you to sacrifice than what they really need for the level of uh comfort with when it comes to their money, if that makes sense.
SPEAKER_03:Well, and you know the personal attention, yeah. Chase puts A heck of a lot more emphasis on building up its personal banking portfolio than it does getting retail banking customers or opening an account with 50 bucks. And but but Chase is all in on that personal banker stuff. And you don't have to be a millionaire to be uh Chase personal banking customer. That would help, but it's not it's not necessary. So lesser amounts will do, but you can't be bouncing your rent check every month and have a personal banker.
SPEAKER_01:So yeah, that at some point they're gonna red flag that and say, hey, we we gotta we have a have kind of a conversation.
SPEAKER_03:So yeah, I I I see the personal touch remaining, and it continues to be something credit unions can can uh succeed at. And I don't see AI taking that over just yet. How about using AI to do essays, for instance? I know many people who now turn over most of their email to Gmail, which has an AI function. It'll answer the things for you. It also will summarize them so you don't even have to read them and write an answer. Now it gives you the answer, do you you know, is this okay? Yeah, sure. I didn't read the email in the first place. So yeah, it's okay. I I don't know how I feel about all of that, but if I had high volume email, I'd probably start using it more than myself, to be honest.
SPEAKER_02:But I don't have really high volume.
SPEAKER_01:Yeah, we uh we we struggle with uh a lot of different aspects here of um you know using computers and AI and uh other aspects to make informed decisions. Now it's it's no denying it, you know, similar to what you brought up in 1995, where it's a transition from what we know, what we believe in, to then, oh, this actually thing does work and it's actually very convenient and it's helpful. And oh, I can actually purchase something. I'm actually putting my credit card on this thing and I'm getting something. Okay, there's a bit of trust going on, right? That's where we are right now with AI. It's that it's that trust factor that's um somewhat lacking or lurking. Um, and that's that's okay, it's gonna come with the territory, but I I guarantee people a lot more trustworthy in AI very, very soon, or the this you know, new tool that everyone has. But there's very much uh apprehension or even um what else is what's what's around the corner. So again, I just want to caution you know your listeners here that it's it's a new tool, don't trust it yet. Um you know, use it cautiously, but knowing the rules of engagement that it's not everything, it's not uh it's not anywhere near perfect and no nothingness, and there are challenges that that lurk. But you know, um validate uh and find out, you know, get different sources of information besides just simply chatting to one thing and getting one source and that's that's it. Do more work and uh find out what you know, be truthfinders and the information you're you're seeking.
SPEAKER_03:Well, one thing I would always tell credit unions, but I don't need to tell them because they do it anyway. If in doubt, ask another credit union. If a credit union says, and I get this question sometimes, should we use such and such a vendor? I always say, Oh, why don't you find a credit union that you know and ask and they're using that vendor? Ask them, don't ask me because they will if they will have more valid experience. And I I might think the CEO is a great person, but that I'm not a customer, so right. And credit unions are really good at asking other credit unions about about stuff, and credit unions are really good about sharing uh honest feedback about vendors. Yeah, and that's that's that's word of mouth, right? If Chase calls up Bank of America and says, Hey, you know, are you using such and such vendor or how are they? And let's say B of A thought they were the worst vendor in the world. Oh, love them, man. Love them. Yeah, I'd if I were you, I'd turn over all your functions.
SPEAKER_01:Now that would be mischievous. Yeah. Fully endorse them uh falsely. I like it.
SPEAKER_03:I mean, it's I've known bosses who have bad employees that they're dying, which is they please leave, please leave, and you get a reference call. How oh great! Oh, I'd hate to lose them, but sounds like a good opportunity. So but if a credit union comes up to you and says, okay, Casey, I'm concerned. What can you do for me? What what exact what if you tell me you short form, what kind of program would you set up? Uh specifically for uh just in general or um credit union concerned about no, not just in general.
SPEAKER_01:Yeah, I think there's a important tool that we offer that uh is really underutilized, but it's really our reputation uh SWOT analysis. And what that is, is to uh look at what is your reputation of your of your credit union. So, and that's an unbiased look at the products, the processes, the people of the credit union inside and outside the organization, uh, because the brand of the of the credit union is what the credit union says by itself. The reputation is what other people say. So as they bring us on, we go look underneath the hood to find out what people are saying or not saying about your credit union or your competition. So the SWAT that I speak about, Robert, is about like letting us to see how good is your reputation. And it's it's being vulnerable, but it's looking to see what you don't see and find out other areas that you can shore up or improve upon, or maybe even accentuate. So the findings behind such a SWOT analysis, and this could be surveys, this could be focus groups, this could be online reputation uh assessments, this could be uh interviewing employees, past members, et cetera, et cetera, uh, with the goal in mind is what are we trying to accomplish here? What is it that we we're looking to find? Um, that gives information to the credit union to say, okay, we need to improve our services. We need to improve our, you know, our products, our online or our mobile app, whatever they find. Now, there might be some lone wolves out there who are going to cry out, we'll do we'll discard that, but we're looking for themes here. So that's a good point of entry for a credit union to find out what is it that they don't know. I call it a reputation MRI. A reputation MRI essentially let's find out where you're healthy or where you're not.
SPEAKER_03:Yeah, how does AI figure into this?
SPEAKER_01:Well, AI figures into it as far as some of the research that he goes into. So if I, for instance, I put into your into chat GPT, for example, um, what do we know about the reputation of a uh of a credit union? It's a good starting point, but it's not the end point. Um, AI can help at least the perception of it because that's actually an excellent idea. Every credit union should do that.
SPEAKER_03:Yeah, right. Chat GPT and look up Gemini. You know, what's a broad reputation? Yeah, look at Robert McCarty. Oh, I've done it. It's careless, man. Don't do it. Yeah, don't look, don't look me up, please.
SPEAKER_01:But that's that's kind of what uh if I've if I'm a member, that's one of the first things I'm doing, right?
SPEAKER_03:If I am a uh if I'm an employee, in the old days, people would look up the Better Business Bureau. That's right. And if a credit union has a lot of dings in Better Business Bureau, they got more problems than I can help them with. Yeah, it's uh looking up new products. You're just sitting at your desk, you type it in. You know, what do you think of Navy Federal? And should I go there versus Pentagon Federal? I'm sure it has an answer to that.
SPEAKER_01:So it's it's a starting point, but if you go a little bit deeper, and that's like, you know, if you're an employee or uh prospective employee, uh, I'm gonna say, hey, is this a good place to work? Right. Uh you look at the glass door, right? Or if you are looking at product comparison, you can do your own homework. So part of this is your secret shopping this as a either employee or a member or someone from the public. What is that you know or don't know? So our job is to do all that research for them and provide a unbiased third-party analysis of what we found online, online, uh, offline surveys, et cetera, to culminate. Here's what we found. Here's the theme that you should be aware of. Uh, don't be distracted by those glorious five-star ratings or uh even the net promo promoter scores, those are um fool's gold, I think. Uh what you need to do is really find out across different sectors where are you what's being said or not being said about you and verse competition. You know, competition is important as well. Uh how do you compare and content uh contrast for other uh organizations? So all that's a good starting point to find out really what your true value of your reputation is or where there are deficiencies.
SPEAKER_03:Then I assume you would help to kind of you know create some guardrails. Yes, using using uh AI.
SPEAKER_01:Oh yes. Yeah, that's a that's a good point because uh for AI specifically, uh there are I've seen a little more of an adoption for this. I don't I don't think too many um companies are not adopting that, but I think it's important to say, all right, um, this is this is a new important strong tool that there are guardrails. Even I've seen a lot of companies that are blocking the use of Chat GPT or other AI tools on a regular basis from their computers. People have to go home to use it, and that's that's okay. But um if you do, uh this is one area that's one industry, I should say, that's been working on this is the the legal industry.
SPEAKER_03:They're reliant a little bit too much on gathering facts and information about a case or details from a client using these oh these these court filings that have references to bogus non-existent previous cases. This is this is horrifying.
SPEAKER_01:Yeah, so similarly, um, whatever you're looking at for data and information, if you're a credit union employee, or even, hey, I want to look up uh this is a prospective member, what do I know about them? Uh, it doesn't tell the whole story, right? And so use caution when using such a thing because it doesn't tell the whole story, or there might be erroneous details about them, or you know, other stuff that you need to go a little bit deeper, deeper on. So using caution, having policies and procedures in place, and knowing that if you do use it and you're basing opinions and or your work on it, and we find out about it, there's consequences, and one of them could be to be fired. And you're seeing a lot more of that for employees that they're using it falsely, or they're putting uh information about the member into the that that tool, and it's unfair. So there's a lot of uh do's and don'ts that are taking place right now as guardrails to protect everyone, but if it's not used properly, then there's consequences mostly from the employment side.
SPEAKER_03:Yeah, I I would just say as a rule that no credit union employee should put in personally identifiable information about members or co-workers or themselves into AI under any circumstances. I'm sure there's some circumstances who would be fine, but just to simplify matters and say under any circumstances, don't do it. That to me is the most important thing a credit union employee can follow because if you violate that, you file it, violate something big time, man.
SPEAKER_01:So yeah, trust and reputation, and uh that's where our world comes in play that this is this is serious stuff now. Uh one false you know step, and and that goes a long way. And it was um the famous Warren Buffett quote is it takes 25 years to build a reputation and five minutes to ruin it. If you think about that, you'll do things differently. And the idea here is that uh reputation is so sensitive, and people are sensitive to it, that uh we're forgiving nation, but we're not a free uh forgetting nation. And what I mean by that is that you know, if I'm gonna make a decision between one fine institution and another, and one of them has a even the smallest of black marks, that might tip to scale for me to go somewhere else. Or if there's enough um uh chatter online, or oh, did you see that happen about XYZ? You know, look out. Uh, one of the credit unions up in the Northern California is Patelco. Now they had a fiber attack happen to them last year, and they lost heaps of members, and primarily because of how they mishandled the information that they had or when they knew about the the hack. And that really pissed off a lot of members enough to say, bye-bye, I'm gone, right? So that's another key example of not being prepared, not handling the situation well, and ultimately hurting the bottom line.
SPEAKER_03:Now, what was the uh reaction from readers to your CU inside piece about AI as supervillain?
SPEAKER_01:Yeah, we got a lot of shares and some comments back to us, and uh most folks are saying this is something to watch. Uh I think it's new, Robert, that this is something that we all kind of know that's it's happening, but it's it's uh raising an alarm bell that this is a something to uh pay attention to because we don't know what we don't know. And while we're using it for hopefully all the right reasons and fun reasons, and it's a new toy, uh play with that toy with caution or be vigilant that there's we don't necessarily know all the issues behind it. And so um that's one of those things where uh the cautionary tale is the the the response I got. It's like this is good, thank you for for sharing that.
SPEAKER_03:Well, I I totally agree that you know these tools are extremely powerful, but we don't know the full extent of their powers, to be honest. Therefore, we it's up to us to be cautious about how we use them. And that's difficult. People you know what is caught what caught what what's cautious to me isn't necessarily cautious to you, but I I think we're at a point where it's good to have this kind of discussion. And uh will we agree, not necessarily, but I totally agree with the concept that man, you gotta be thinking about these issues. You just can't plunge head uh head first into this.
SPEAKER_01:It's uh yeah, and I I don't envy a a board of directors or executive leadership team of any company, let alone the credit unions, that they have their own heartburns going on right now, from deposits or lack of deposits to uh moving target in the financial world, uh, all these different disruptors that are happening. Now you throw AI into the mix. Oh wow, there's so much uh uh headspace that's swirling uh that's going on, obviously, from tariffs to the financial uncertainty. There's just so much happening right now, and AI is just one of those important things. Uh, I don't know how they do it.
SPEAKER_03:And it's uh it's tough to and this is the biggest change in financial institutions, not since the smartphone, not since the web, not since uh either of those things. It's actually the biggest change since uh institutions, financial institutions began to computerize when they moved uh data off of pieces of paper onto floppy disks or magnetic tape. I mean, that's that's when this uh that's how big this change is. I mean, this is and that that change happened in the mid-1980s for most institutions. We're at the same place now, and it's it's just an immense change. Now, could this blow up? Yeah, I mean, but then again, the transition into computers could have blown up too, and it didn't.
SPEAKER_01:So yeah, it's just uh we we're all have to adjust, right? And that's the the the tough part here. And I don't necessarily know, you know, uh one of the areas is jobs, Robert. You know, where the jobs can come from when these are this disruptor of AI is taking over jobs, but uh every industry, everything um, or every era I should say, has adjusted to the times. And I don't necessarily know what the new jobs are going to be. Um I hope there's still room for human beings.
SPEAKER_03:Oh, there are, but and you know, if you actually look at historic staffing numbers and credit unions, they're much lower for, let's say, million dollars in assets than they were 20 years ago. There's just fewer people. Yeah, uh significantly fewer. And I think that trend will continue. Can we wipe out all departments? Well, I hope not. We can't all be sitting around letting machines do all our work. I at least I hope not.
SPEAKER_01:Or you know, you know, what's what's our value?
SPEAKER_03:You know, and then machines might decide amongst themselves to get rid of us.
SPEAKER_01:Well, if if it if that's the world uh we had to live in, then um I'll be the first in line to say goodbye.
SPEAKER_03:Hey man, you must have seen the matrix, right?
SPEAKER_01:I know I I still like human beings. If uh human beings are being taken over by AI, then well, that's my time to you know say goodbye.
SPEAKER_03:Uh go go watch the matrix again tonight.
SPEAKER_01:No, I I prefer not to. I won't watch uh maybe uh Annie Griffith rerun or something.
SPEAKER_03: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 unit. What can they do to really, really, really make a difference in the financial team? Can't all be megabanced, can it? It's my hope it won't all be mega banked. It'll always be a place for credit unit. That's what we're discussing here. To figure out how you can help, get in touch with me. This is RJMGGarvey at gmail.com, Robert McGarvey, and that's rjmegarvey at gmail.com. We'll figure out a way that you can help. We need your support, we want your support, we thank you for your support. The CU2.0 podcast.