Banking on Digital Growth
Banking on Digital Growth

Episode · 6 months ago

207) #ExponentialInsights : The Leap From Hunch to Hypothesis Through Automation


A lot of people get anxious when they hear the word ‘automation’ in corporate-speak, sparking a dystopian vision of robots and computers replacing humans at work.

But my guest, Glenn Hopper, believes that machine learning is simply another tool that can take financial brands to the next level.

The CFO at Sandline Global and author of Deep Finance: Corporate Finance in the Information Age argues that proper checks and balances can usher banks and credit unions into digital transformation with AI automation.

Join us as we discuss:

- Potential roadblocks and dangers of using machine learning (8:20)

- How to avoid being held hostage by AI automation (17:36)

- Splitting time on the present versus future focus through data (24:33)

Check out these resources we mentioned during the podcast:

- Glenn Hopper

- Find Glenn’s book, Deep Finance: Corporate Finance in the Information Age, on Amazon

You can find this interview and many more by subscribing to Banking on Digital Growth on Apple Podcasts, on Spotify, or here.

Listening on a desktop & can’t see the links? Just search for Banking on Digital Growth in your favorite podcast player.

If you talk about deploying automation and making this digital transformation and going to a dad a rim and company, I don't think that the right thing to do is think about, Oh, look at the cost savings that we're going to have out of this, because that's shortsighted. You're listening to banking on digital growth with James Robert Leigh, a podcast that empowers financial brand, marketing, sales and leadership teams to maximize their digital growth potential by generating ten times more loans and deposits. Today's episode is part of the exponential insight series, where James Robert Lay interviews the industry's top marketing, sales and FINTECH leaders, sharing practical wisdom to exponentially elevate you and your team. Let's get into the show. Greetings and hello. I am James Robert Lay and welcome to episode two hundred and seven of the banking on digital growth podcast. Today's episode is part of the exponential insight series and I'm excited to welcome Glenn Hopper to the show. Glenn is a former Navy journalist and chief financial officer for Sandline, and he has a master's degree in finance and business analytics from Harvard University, as well as a master's degree in Business Administration from Regis University. Glenn is also a member of American MINSA and volunteers his time for the analytics foundation nonprofits to digitally transform their organization. At to day we're going to be talking through some of the biggest insights found in his book deep finance, which offers an indepth guide on how to digitally optimize and modernize financial brands in the age of analytics. Welcome to the show, Glen. It is so good to share Tommy you today. Buddy James, glad to be here. Finally, I think this is the third time we've tried to get this thing going. It's the third time, but you know that what they say, third time is a charm. And and before we get into your book deep finance, I want to start off our time together on a positive note, as we always do on the show. What is good in your world right now, personally or professionally? It's always your pick to get started, buddy. Sure. Okay. Well, let me see. I'm new. I've been doing the podcast thing for a while, but something that's come out of that is I've started doing the conference to her and I know you said you were just down here to conference last week and it's it's never it's not anything I've ever sought to do, but I've got I did a spoke on a panel at the CFO leadership conference a couple weeks ago. I'm speaking at a Harvard reunion event. I'm doing the keynote speech and I'm doing a hosting a panel with them. That's I'm leaving Thursday for that and then I'm going to be speaking at sweet world this year. I've got another finance conferences fallow and I'm I think I'm talking in Chicago later. So this has been exciting. I don't I'm not at the point where I'm charging people to do this, so it's kind of an out of pocket weird thing for me right now, but it's fine. I'd be just I think at this point I could talk to offense post. Well, you know, I think that's the the the thing is you've got such an important story, number one, and the insights that you're sharing in deep finance are or insights. It's knowledge that is really transformative and that's why I'm excited for this conversation today, particularly you know, when you think about banks, credit unions fintext, what's coming down the pipe lie, particularly from the incompt point of view. And you know, as a fellow author speaker, I understand writing a book is a massive undertaking. It's can take a tremendous amount of time, effort, energy, attention commitment. I want to start there. Why write deep finance in the first place? The secret is I'm a one to be a science fiction writer deep down, that that's like my true passion. I've got a two hundred thousand word space opera sitting on my computer right now. But I was a journalist and before I even got...

...into the business world, my first job out of school was to be a journalist and the way my thinking process works, the way that I've always sought to understand something and was just to start writing about it, sort of to organize the structure and put it down as a story. So it I've always taken copious notes and whatever I'm studying, but because I have this, because there to write. I've also always been a big blogger, whether it's doing forbes finance counsel or medium or just any kind of in you where I could post something and I started writing about a data science and analytics a few years ago, just in how it applies to the field of to corporate finance, and I started putting together all these notes and the more I wrote about it and the more I saw, hey, there's something here, and you know, I kind of turned around in between a few, however many blog posts I had at that point, I said we've got something here. Any Long Story Short, I contributed a chapter to this compilation book on perseverance, a business book, and after I did that the publisher was asking me if I had any any book ideas of my own. I told her about this and kind of one thing led to another and they did maybe save me from myself. I mentioned that two hundred Tho word space opera. I think my first draft of this book was it kind of read like a textbook and it was like eightyzero words. They helped me trim it down and make it more approachable and it's down at least like forty five or fifty thou words, which makes it a real kind of approachable book with a nice length that it's not going to bog you down and ruin your summer trying to weak through it. No, and I think that's the key is a lot of the subject matter can feel very intimidating, it can feel very overwhelming. And you know, when it comes to financial brands, they they they make decisions, they try to make data driven decisions, but a lot of time those quote unquote data driven decisions in reality are just hunches. And I'm curious, from your perspective, what are the opportunities when it comes to banks and credit unions to go from Hutch to hypothesis by defining automated processes to make what I would say smart decisions based on real data? Regardless of what data you have, if you've been doing whatever it is in your profession for a while, you do have business insight. So you pick up with experience and that's your hunch and you can have a good gut feeling on things. But you know the world changes and it's changing faster than ever now with just all this out of it's out there and and how fast technology is moving. So your hunch for what might have worked even a year or two ago may not apply anymore. And I think talking about financial brands, of talking about banks and Kredit Unions, at the two big wounds for me are fraud detection and loan approvals, and you know. So fraud detection, I mean if you think about from a bank standpoint, like I bank with a small bank here in Nashville, a small regional bank, and I know everybody at the branch. I have to tell them when I leave town so that they don't stop my debit card. Yeah, that is that's hunch. That's people driving it. But if you bank with a larger bank in particular, you know and probably are make has some of this as well. But they use data so that they know perfect example, in personal life, someone use my wife's credit card number at a factory, at a farm equipment dealership in Texas a couple weeks ago and it immediately got flagged. Like I bet your wife is not in in rural West Texas buying a tractor right now. So it got flagged. So that's the kind of...

...thing that because machines see so much data and they know, and it's not just they see sort of the global data, and so they know there are more instances of fraud in this state or in this country, but they also know individual spending patterns correct the machine can just track and so if you've never been to Texas and you've never spent you money in Texas before, it'll flag it more. And so there's that. And then there's loan applications. That's a big one and that's a big one to where bias comes out a lot of times. So you have to be very careful in that. You know, you can, just like a human can have bias. You could actually train a machine incorrectly and they and their bias could come through, and that's kind of part of it. So you have to just work on training in that out. Yeah, and I think it's interesting is you is you're talking about the the fraud detection. I was just recently having a conversation around voice and voice banking and you know, what does that look like, particularly from KYC perspective and authentication, and I said, well, you know, you have the possibility of biometric voice identification. You then cross reference that with, you know, location, and there's a lot of different ways to get ml and machine learning to come together to take all of this crunch it down. But I also I'm glad you're minching the possibility for training bias in or training bias out of machine learning when it comes to loan approval of what are maybe some of the road blocks or the dangers? We got a couple of opportunities that we can queue and on, but maybe what are the roadblocks, are the dangers that we need to be aware of when it comes to you know, AI, automation machine learning? That could be a bit of a challenge. Yeah, I think the biggest example. So even even the largest companies in the world who are using aim machine learning or having to deal with bias in the best example I can think of. And we can step out of banking for a moment and sure look at this and then we can talk about come back to the loans. But so Amazon, who's got some of the best AI technology out there, with whether it's their recommendation engine or, you know, kind of everything that they're doing, they're huge machine learning users. Well, a few years ago, you may remember, they wanted to use machine learning in their hiring process. So what they did was take all their current employees and all their resumes and all their backgrounds and took all the data they could about their current employees and said, well, these are the kind of people we want at Amazon, so let us try to find people just like this. Well, turns out if you have a shortage of minorities or you have a shortage of, say, even you know there are a lot of people with a foreign sounding name that's not, you know, joe or Bob or your typical name, and then it's going to say, well, we only like Joe's and Bob's and because that's the data you gave us. So you have to think about it's like it's like study design, when you're organized, you know, when you're doing statistical sampling, you have to think about, okay, yes, let's get a truly random sample. But it machine learning because it it takes the data that you have and it in it grows it so exponentially correct with with what you're doing with it, as small bias will get exaggerated in it. So you have to look at all that and there's yeah, go ahead. This was a big issue, you know, with that example, with Amazon and the hiring. This was an issue maybe three or four years ago in facebook clamp down on and a lot of us came out of the facebook Cambridge Analytica debacle. But it was in regards to targeting ads, particularly around loan products. Right. It was fair lending was becoming a major issue because it was using banks credits were, you know, uploading data and trying to, you know, get look alike audience data of but then it was, like you said, it was growing exponentially a bias that might not have been even a thought at the time, but that, I think that's a key insight right there. It's the exponentiality of data,...'s the exponentiality of automation and it could take something that is relatively small and really make it pretty big pretty fast. Right. Yeah, exactly, and it's and it's tough. Like you know, think about if you are your bank, and so many banks are relying more and more on machine learning. Everybody wants to go fully digital on as much as they can just to cut costs down and to do it more efficiently into to do it better. And if you train the models accurately, that's great, but you know, so think about underwriting at a bank and think about getting through the loan approval process and where you know there's going to be committees and however what, depending on the loan amount and what it's for and all that, there's going to be different hoops that you jump through. But say you're just doing consumer mortgages and you maybe there's a an area of town that is mostly rental properties. Maybe it's like in densely urban population that is you know, maybe there's other components in their work median income might be a little lower than other areas. So you take I mean think about when you train these models, you're just putting in features. So if you're trying to think of a credit worthy person that you're going to loan money to, you look at, you know, what their income is, what their credit history is, what they're you know, all the things that they'd have on the application. But then if you're trying to apply machine learning to it, you're trying to say, okay, well, this applicant put all these things on their application. Let's fight. Let's compare that to our book of loans that are successful and our book of loans that have defaulted. And so if you've had some people in this zip code say that defaulted, if you're in that in that zip code by default, it's going to kick you out and it could you could be discriminating because of using the machine learning algorithms. You got to be very careful and stuff like that. Digital growth is a journey from good to great, but sometimes this journey can feel confusing, frustrating and overwhelming. The good news is you don't have to take this journey alone, because now you can join a community of growth minded marketing and sales leaders from financial brands and fintext who are all learning, collaborating and growing together. Visit Digital growthcom slash insider to learn more about how you can join the digital growth insider community to maximize your future digital growth potential. Now back to the show exactly, and I think that's where it's like. You know, we think about automating processes, it can feel overwhelming and intimidating because of really the abundance of opportunity to automate around. But then there are all of these these little small things that we need to be mindful of. So I'm curious how can financial brands identify and switch from we'll call it mindlessness to mindfulness that directly impacts their bottom line? Like, like you know, it's it's kind of what I always talk about, the lessons from four seasons. Systematize the predictable so that we can humanize the exceptional. Where the opportunities go from mindless to mindfulness? Yeah, great question and I always say you know, you can't automate chaos. So before you start looking at automation and finding this mindful work for people to do, this is my I've kind of been in this start up to scale up zone the past three companies I've been with where I come in, they've been a startup and they've come up with these sort of processes, but mostly they're still in that cowboy phase where everybody's just doing everything they can to serve clients and that's great when you're a startup, but if you want to be able to scale that, you've got to get some processes around it. So same thing goes when you're...

...trying to adopt a data driven approach and you're trying to transform your company. You have to first look at what the processes you're doing and then, for me, it's almost like you do an ISO nine thousand audit where where you go all the way back to the first touch point of any customer or potential customer, leader, prospect or whatever, look at your contact with them, what data you're getting, how it comes through, how you're using it, if you're using it all, find a way to carry that customer persona or the you know, the real customer information all the way through to their one. They get the loan, they go through your banking with them and they go all the way through payments of their loan and if they eventually turn off or whatever. So you have the full customer experience mapped out. Yep, and then it's a matter of you generate the data, you collected dating, you process the data, you store the data, manage it, analyze it, vigialize it, interpret it, and so it's a you know, people say digital transformation like it's a oneandone thing. It's really more of an evolution. It just keeps going and and so, yeah, I'll stop there because I've I threw a lot out right there. No, no, I like what you're going with this and I want to come back to the point that you said. You you don't want to automate chaos, you don't want to scale chaos, and I think that right there is where it's so easy to kind of miss some of this foundational, diagnostic discovery work. And it's we we want to take the past that's informing what we're doing here in the present moment. But that's that necessarily what is needed to get us to move towards the future. Sometimes you gotta simplify before you multiply. Lessons from Dan soul of it over, strategic coach. You know, that's that's his whole perspective in technology is a multiplier, so it's going to multiply whatever it is that you give it, and that's where I think, you know, a lot of times, what's my role in this? You know, at an organization, at a financial brand, because a lot of times when we're going in and we're training marketing teams and sells teams and leadership teams and we're having some of these conversations around Ai and automation, the questions, the concerns, even even with marketing, you know, AI is massively transforming that entire area, that entire discipline, the all the way down to content creation. Ai is now writing articles, AI is now writing headlines, AI is now transcribing podcast and taking an article and then turning that into a video. I mean it is really it's an amazing time, but it can also be a very scary time for quote unquote, humanity who might feel like their job could be replaced by a robot. But I think the good news is, and I know this, you don't believe that's the case, and so I want to come back to data here. You know, what's the opportunities, because I think you can get held hostage by data. How do we prevent not getting held hostage by the data and the insights, by the AI, by the automation, to really create an environment where, you know, nobody is irreplaceable. But what can we do here? You said it earlier. It is the transition from mindless data entry, just entering numbers in, entering in loan applications, whatever you're whatever your job is, entering in invoices to be paid, you know, creating invoices for your clients, that kind of stuff that people used to do. There's there's off the shelf software now that does all this. Like anything in the corporate finance world, there's I could Google and in five minutes probably find a SASS product that I could get for, you know, a hundred dollars in the mith that does what an AP clerk us. To bear over, the case is so I think that if you talk about...

...deploying automation and making this digital transformation and going to a data driven company, I don't think that the right thing to do is think about, Oh, look at the cost savings that we're going to have out of this, because that's shortsighted. Yes, if you can automate things that people are doing, that's great. You can do more with with less, but if you really want to create value, it's okay, I've done away with this mindless work. Now I'm going to ask more of my human counterparts to do what machines can't do and do that mind full work and add that element. So it's it's not just turning over everything to the robots and saying, you know, you run the ship. It's the balance between human input and machine input and in humans using the tool, using the data and everything that comes out from all the machine learning as just another tool, just like you would, you know, if you're the CEO of this company, you're not going to give away all your decisions to your your Seetam, you know, to your management sweet. You're going to use them as advisors. You're going to make the decision. Well, now you have another advisor. It's called data correct. So you take all that and you still apply that human element to it, and I think that's where I'm working on my second book right now, which is titled Banking on Change, because in the subtitle is how to achieve exponential growth in the age of AI, and exponential growth is where you're feeling like you're growing personally and professionally at the same exact time. Because I'm through my work, I'm finding that there's a lot of conflict that's going on, conflict rooted really kind of deep in the mind, almost a little bit of an existential crisis of what's my role in all of this? How do I fit in? Why are we doing this to begin within the first place? And then and then there's a lot of uncertainty. I'm curious what's your recommendation when dealing with risk aversion, and we know the inherent risk a version within the financial services space, but it's almost like in a key lees heel to a degree, to it's almost it's a double edged sword. It could be a great strength, but can also be a tremendous weakness as well. How do you recommend we deal with exponential change in the age of AI? And I would say, more importantly, that it's probably the fear of change, maybe even the fear of the unknown. What's your take on that with all of this happening. I guess first I love that you didn't just have the business part of it, that you had the personal part of it as well. So I think that maybe a way in finance people certainly are risk averse. It's probably the reason that we're in the in the profession, because everything is just, you know, it's black and white. It's either a positive or a negative number. Everything can be explained. It's not, you know, Tu de Fruity Marketing, where right where? You know, you don't know what's going on. What's our lie is on it. We're looking for these concrete things and I guessed my advice would be the same for an individual as it would for a company. And it is to be truly successful in the long term, whether this is you and your career or your company and what they're doing, you have to be able to simultaneously explore and exploit. So exploit is doing what you've ever doing, what you've always done, do it better each time. And if you're if you're not exploiting, if you're not making slow and steady incremental change, then you're going to get left behind immediately. But think about so exploiting is just being the best you can at what you do. So if historically, in my role as a corporate finance guy, you know, we sort of been record keepers. We're looking in the Review Mirror, we're saying, yes, what we did last quarter, this... what we did to budget. You know, you see, you're basically the scorekeeper, and that's great. Businesses are always going to need that. But if you're not getting incrementally better at that and you're inefficient in just doing things that now other finance departments are doing with much lower personnel and much more efficiently, much more quickly than you're getting left behind there. But at the same time, if you're not looking to the future and exploring into what else is out there, then you're also going to get left behind. And you know, I think from a company level it's like look at Kodak or blockbuster or people who forgot what they're you know, blockbuster might as well become like a real estate holding company at the end. They they could not get around the fact that, well, Netflix is never going to beat as we've got all the video stores and now who would ever think about going to a video store? I mean that's so archaic, but they they were exploiting the heck out of what they were doing. They were all the way to the end opening stores really fast and crushing it in that market. But the market change. So you've got I guess it's my advice in my patients. would be then, for someone who is hesitant to change, because I think history is shown time and again that if we ever stop then we're going to get run over. I really liked that perspective of exploring and exploiting at the same exact time. One of the ACRONYMS that I'm recommending is you must act to grow, and acronyms run rampant and digital growth Topia, because it's the only way the add mind that I have can actually remember things with Word Association. But, but, but, the A is all about gaining awareness, and to gain awareness you have to go up the mountain and you have to see where you've been looked down and look ahead, keep looking a head towards the future. Otherwise you risk getting trapped in the cave of complacency and that creates a pseudo sense of confidence. Like you said, blockbuster and Kodak and and all of these other brands. But it's it's exploring and exploiting. I really like that perspective and you know, people always ask me, and I want to get your take on this here, like how much time should we really you know, if we're going to split our time between we'll call it present focus activities and future focus activities, what should that be? Because one of the big concerns I always hear from from bankers and credit new leaders we're so busy executing on the day to day we don't have time to think about the future. And I'm like, well, that's not an excuse. That's going to get you run over, because you know, to quote Peter Diamandas, futures faster than you think, and the next decade and we're already two years into the next decade. Twenty, thirty is going to be here, you know, before you know it. How should we be dividing our time so that we can continuously be exploring and exploiting, not just executing in the present moment? So this does that says tough and I actually have a whole section or two in the book on this one. Is it? You could be at at the top of the world and your company could be crushing it with your performance right now and if you're if that's the case, it's easy to say, you know, all of your time needs to be figuring out how to just get better at exactly what we're doing and get more efficient and do more of that. Yeah, I guess the short answer is, and I'm going to say this, but then I want to come back to it and talk about people who are maybe don't have the SVP title or whatever. They need to really drive decision making. So I would say that you need hopefully the top management in the company is out of the weeds enough that they can step back and look at the big picture, and hopefully the top management at a company or bank is looking at what's out there now and they understand what's going...

...on in the future, because as the captains of this ship, they've got to they've got to drive it. So you hope it comes from the top down. If that's not the message that you're getting from the top down, it can be very difficult. So I talk about if you are frontline, you know manager, senior manager, someone in that realm, then maybe like going across department, finding sort of a skunk court team, unofficial at first, putting together some plans and finding small winds that you can do to show, if you know, what happens if you move forward. So I would say at the top level, hopefully you're sophistic the businesses fists get enough that the senior management is out of the weeds of the day to day trying to do that. They've got, you know, a trusted lieutenants below them that are working on really exploiting what they're doing and that they're charting out the future. So I I would say at the top level I hope you're constantly looking forward and below that it's just about, you know, you've got to follow with the sort of the company mission is but at the same time, if you want to drive change from the lower ranks, it's harder work. But I try to cover it in the book and give some ideas. But it's easy for me to say, I think, in my sea suite position of how to do that. So it's it is more of a challenge for the junior person. Know it definitely is, but I think it's you know, it's a continuous commitment to review what you've done, learn through those experiences, think about like what what are those next best steps forward? Apply that thinking, but then be mindful back to what we were talking about before, to not get stuck in the repeated patterns of Rote and just thinking that, yeah, this is worked, because, like you said, things can transform so quickly. I mean even from a data intelligence standpoint. I I'm very interested in in this has been a practice now for probably the past five to ten years. But we have customer intelligence, yes, but the really kind of the new is the how to use data and ai for competitive intelligence and really gleaning insights from the market place and seeing what others are not either a able to see or what we've not been able to seeing historically, to apply that so that we don't get stuck, because it's, like you said, you know the past. When you say that we're not our past is that define our future, that's a very positive thing. If the past has been historically challenging in a little bit of a struggle, it's a very positive thing. But to say that our past is that divine our future when we've been historically successful, that can be either a a little bit off putting, an insulting or be can just be downright scary, and I think that's where we probably need to lean into a little bit further. So as we start to wrap up here, I want to get real practical. Next best step, something small that the deer listener can do to move forward on their own journey here, because all transformative growth begins with a very small, simple step. For what? What is the best next step for a financial brand to move forward with urge, with confidence in the age of AI, around some of these topics of things like machine learning, an automation? What is one small thing that they can do next? Let me back this up above even the machine learning component, because maybe this helps get you thinking down the right path. And it's this isn't you don't have to know what a random forest is or...

...what you know what a neural network is. This is just thinking about data and how your company is using data today and what's out there. So the first thing I would say is take a look at what data you have, whether it's on your customers, on the competitive environment, on the global economy or whatever. What is? What data are you using or do you have access to today, and then you just you kind of consolidate all that data and you start thinking about what can I do with this? And then I'm going to give you for ways to look at Datas sort of an evolution of how you can adopt this move through this digital transformation and how you can use his data. So the first one is, I've done this autuit. I see all the data. Let me just take this step of describing the data that I have. And here I am, I'm just examine, understand and look at what's happened. And I sign up this many customers a month, I lose this many customers of minds. This is what's going on, this is what our churn rate is, this is what our loan default rate is, whatever the case is. Let me just look at all this data. And then after that, one step deeper is figuring out why. So you've got all this sort of descriptive analytic information that looks at all the data you have, and it's all historical data, and then you say, what can I find in the data that says why customers defaulted, why customers left as why? You know why this month we had more people opening new accounts or whatever it is, and then you can start getting some value in that. You Start Finding correlations, you know, you find things like unemployment went up in this region and loan defaults when you know what if that's that's a super easy one. But you know if you're not, it's hunch is to say that hypothesis is to say it. Look at the data and prove it right. So you go through, you do the descriptive, you do the diagnostic diagnosis and then, once you have a good handle on that, you start doing predictive stuff. So you see, oh, the fet is going to raise interest rates. What's the ripple of that? You know that's a leading indicator for what's going to happen here. So what's that means going to happen for me in the future? And then you can sort of map out whether you do in your FPNA, you're budgeting and planning and you're modeling the future. You say, okay, I'm going to predict, based on these leading indicators, what's going to happen down the road. And then the final place that you get is prescriptive analytics, and that's where you say, okay, that's how and what can I do to control this customer behavior? So, all that said, I didn't say a thing about machine learning, but when you go down this road, you start finding that there are tools out there. They're algorithms that make all this much easier and that you're able to process more data and everything. So I think step back, and I'm always one I want to go one level deeper and look at the why and that sort of underlying thing. So to me, taken that path, the the idea of the power that could come with all that hopefully is enough of a motivator to get you really going down the rabbit hole of researching machine learning more. I really like those four steps because, a, they're super practical. Be Their approachable. See they're not intimidating because you're not talking about like all of this ml and acronyms and things that you just are not common Norman clature for many within the financial services space historically. But I also am hearing you talk through those and see that they follow something that I speak a lot about the common patterns between financial services and healthcare. So, for example, diagnostic, predictive, prescriptive. We can take all of those perspectives turn them in words within our own customer data, within our own membered data, to then make proactive recommendations to our account holders to help them improve their financial wellbeing, which... then closely correlated with their physical wellbeing. So same type of idea, but just a different application. A lot of practicality tied to it all. Glenn, this is good stuff, man. The last thing I would add in that is, if banking is a mostly commoditized industry, if whether you know, if everybody's just competing on interest rate or available credit, you know at what's what's going to make someone bank with you. And as banks getting more digital, maybe it's harder and harder to have that personal touch with it. More online banks and and everything. So if you are doing something prescriptive that adds an exit, it actually valuable to your clients and you're giving them something different. That gives you a way to stand out in the market place, and that's kind of cool. And that's the power of data to I'm telling you, I'm telling one of the the biggest opportunities that I'm really a collaborate with someone on it. I don't you know. I want to see this become reality because it's a common pain point. Is that I hear is either a and I think ai automation machine learning is a possible path. Is when someone opens a new account, right when someone opens a new account out of financial brand. That's step one. It's then getting them to move their money. That step two. But just because you move your deposits over, that doesn't mean that that's a primary relationship, because what what's what all has to come over, are all of the reoccurring transactions, things that you have set up through bill pay, things that you have set up on other credit cards. That's a lot of work and so if there was some type of a digital concierge, that would simplify my life to package all of that up make that a simple, seamless transfer of all of that activity into a new account, new bill pay, new credit card. That would be a tremendous opportunity. But then the second one. It's a symptom of the subscription economy that we live in. How many subscriptions have we signed up for that are seven hundred and ninety five a month? One thousand four hundred and ninety five a month? That, DAD's five bucks, it's fifteen bucks whatever. But then when you have ten of those, well, that's a hundred and fifty bucks. That's you know, that's that's a eighteen. Eighteen hundred dollars a year right there. Now you're starting to like get into some real pain points. But is it enough to go in and then have to cancel all of those those services? That's another opportunity that I can see where, you know, you just come in and it's a plug and play. You run a quarterly or an annual. Well, this is how much you're, you know, losing because of your subscriptions that you've signed up for. We can all of that pain. We're making those prescriptions, those proactive recommendations. We can sit here and talk use case all day long, but I think it's just the practicality of it, of it, of it all together. Glenn, this has been a great conversation. Man, thanks for joining me. What's the best way someone could continue the conversation that we've started today to connect with you? Yeah, probably way. I'm pretty active on Linkedin. I'm on twitter, but I don't really do much there. I think my mom and like some guy went to third grade for Molly followers there. So yeah, Linkedin is the best way. I've got a couple of websites but again they're pretty DADIC. So just hit me up and we can put my linked in profile and the notes of the show or whatever. But it probably if you google me or just search on Linkedin. You'll probably get to me pretty quickly. I love it. Google, Google Glen and what's the best way that that they can get your book? Where can they find that? It's is available on Amazon. It was. It was pretty much anywhere you could find...

...a book. I don't know what the stock levels are right now. You know how it is. It's like it came out last summer. So unless you're, you know, Richard Passion kind of thing, I don't know what's actually in stock, but it's certainly available from from Amazon and Barnes and noble and I think someone just told me they got a copy at target the other day. So it's out there. Get the book. Go to Amazon, get the book. Deep Finance, connect with Glenn, learn from Glen and grow with Glenn Gland. This has been a fantastic conversation. Thank you so much for joining me on another episode of banking on digital growth. Thanks for having me as always, and until next time, be well, do good and make your bed. Thank you for listening to another episode of banking on Digital Growth with James Robert Leigh. To get even more practical and proven insights, along with coaching and guidance. Visit Digital growthcom slash insider to join a community of growth minded marketing and sales leaders from financial brands and Finn Tax until next time, be well and do good.

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