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Glenn Hopper:
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.

James Robert Lay:
Greetings and hello. I am James Robert Lay, and welcome to episode 207 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.

James Robert Lay:
Glenn is also a member of American Mensa and volunteers his time for the Analytics Foundation helping nonprofits to digitally transform their organization. Today we are going to be talking through some of the biggest insights found in his book, Deep Finance, which offers an in-depth guide on how to digitally optimize and modernize financial brands in the age of analytics. Welcome to the show, Glenn. It is so good to share time with you today, buddy.

Glenn Hopper:
James, glad to be here, finally. I think this is the third time we've tried to get this thing going.

James Robert Lay:
It's the third time, but you know what they say, third time is a charm. 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.

Glenn Hopper:
Sure. Okay. Well, let me see. 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 tour, and I know you said you were just down here at a conference last week, and it's not anything I ever sought to do, but I 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 hosting a panel with them.

Glenn Hopper:
I'm leaving Thursday for that. Then I'm going to be speaking at Sweet World this year. I've got another finance conference this fall, and I think I'm talking in Chicago later. This has been exciting. I'm not at the point where I'm charging people to do this, so it's an out of pocket weird thing for me right now, but it's fine. I think at this point I could talk to a fence post.

James Robert Lay:
Well, I think that's the thing, is you've got such an important story, number one, and the insights that you're sharing in Deep Finance are insights. It's knowledge that is really transformative, and that's why I'm excited for this conversation today, particularly when you think about banks, credit unions, FinTechs, what's coming down the pipeline, particularly from the incumbent point of view. As a fellow author, speaker, I understand writing a book is a massive undertaking. It can take a tremendous amount of time, effort, energy, attention, commitment. I want to start there. Why write Deep Finance in the first place?

Glenn Hopper:
The secret is I wanted to be science fiction writer deep down. That's my true passion. I've got a 200,000 word space opera sitting on my computer right now. But I was a journalist, and before I even got into the business role, my first job out of school was to be a journalist. The way my thinking process works, the way that I've always sought to understand something was just to start writing about it, to organize the structure and put it down as a story.

Glenn Hopper:
I've always taken copious notes in whatever I'm studying. But because I have this desire to write, I've also always been a big blogger, whether it's doing Forbes Finance Council or Medium, or just any venue where I could post something, and I started writing about data science and analytics a few years ago and how it applies to the corporate finance. I started putting together all these notes, and the more I wrote about it and the more I saw, hey, there's something here ...

Glenn Hopper:
I turned around in between a few or however many blog posts I had at that point. I said, "We've got something here." Anyway, 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 book ideas on my own and I told her about this and one thing led to another.

Glenn Hopper:
They did maybe save me from myself. I mentioned that 200,000 words space opera. I think my first draft of this book was it read like a textbook and it was 80,000 words. They helped me trim it down and make it more approachable and it's down at least 45 or 50,000 words, which makes it a real approachable book with a nice length that it's not going to bog you down and ruin your summer trying to weed through it.

James Robert Lay:
No, and I think that's the key, is a lot of this subject matter can feel very intimidating, it can feel very overwhelming. When it comes to financial brands, they make decisions. They try to make data-driven decisions. But a lot of time, those "data-driven decisions" in reality are just hunches. I'm curious, from your perspective, what are the opportunities when it comes to banks and credit unions to go from hunch to hypothesis by defining automated processes to make what I would say smart decisions based on real data?

Glenn Hopper:
Regardless of what data you have, if you've been doing whatever it is in your profession for a while, you do have business insights that you pick up with experience, and that's your hunch. You can have a good gut feeling on things. But the world changes and it's changing faster than ever now with just all this data that's out there and how fast technology's moving. Your hunch for what might have worked even a year or two ago may not apply anymore.

Glenn Hopper:
I think we're talking about financial brands, we're talking about banks and credit unions. The two big ones for me are fraud detection and loan approvals. Fraud detection, if you think about from a bank standpoint. 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. That's hunch. That's people driving it.

Glenn Hopper:
But if you bank with a larger bank in particular, and probably our bank has some of this as well, but they use data so that they know ... Perfect example in personal life. Someone used my wife's credit card number at a factory or a farm equipment dealership in Texas a couple weeks ago, and it immediately got flagged like, "I bet your wife is not in rural West Texas buying a tractor right now." It got flagged. That's the kind of thing, that because machines see so much data and they know, and it's not just they see 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-

James Robert Lay:
Correct.

Glenn Hopper:
... the machine can just track. If you've never been to Texas and you've never spent any money in Texas before, it'll flag it more. There's that, and then there's loan applications. That's a big one. That's a big one too where bias comes out a lot of times. You have to be very careful in that. Just like a human can have bias, you could actually train a machine incorrectly and their bias could come through. That's part of it. You have to just work on training that out.

James Robert Lay:
Yeah. I think it's interesting as you're talking about the fraud detection. I was just recently having a conversation around voice and voice banking. What does that look like, particularly from a KYC perspective and authentication? I said, "Well, you have the possibility of biometric voice identification. You then cross reference that with location, and there's a lot of different ways to get ML, machine learning, to come together to take all of this, crunch it down."

James Robert Lay:
But also, I'm glad you're mentioning the possibility for training bias in or training bias out of machine learning when it comes to loan approvals. What are maybe some of the roadblocks or the dangers? We got a couple of opportunities that we can queue in on, but maybe what are the roadblocks or the dangers that we need to be aware of when it comes to AI automation, machine learning that could be a bit of a challenge?

Glenn Hopper:
Yeah. I think the biggest example ... Even the largest companies in the world who are using AI and machine learning are having to deal with bias. The best example I can think of, and we can step out of banking for a moment-

James Robert Lay:
Sure.

Glenn Hopper:
... and look at this, and then we can talk about ... come back to the loans. But Amazon, who's got some of the best AI technology out there with whether it's their recommendation engine or everything that they're do, they're huge machine learning users. Well, a few years ago, you may remember they wanted to use machine learning in their hiring process. 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. Let's try to find people just like this."

Glenn Hopper:
Well, it turns out if you have a shortage of minorities or you have a shortage of say even there aren't a lot of people with a foreign sounding name, that's not Joe or Bob or your typical name, and then it's going to say, "Well, we only like Joes and Bobs, and because that's the data you gave us." You have to think about it. It's like study design when you're doing statistical sampling. You have to think about, okay, yes, let's get a truly random sample. But machine learning, because it takes the data that you have and it grows it so exponentially-

James Robert Lay:
Correct.

Glenn Hopper:
... with what you're doing with it as a small bias, we'll get exaggerated in it. You have to look at all that, and there's ... Yeah, go ahead.

James Robert Lay:
This was a big issue, that example with Amazon and the hiring. This was an issue maybe three or four years ago and Facebook clamped down on it. A lot of this 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 uploading data and trying to get lookalike audience data of ...

James Robert Lay:
But then, like you said, it was growing exponentially a bias that might not have been even a thought at the time, but I think that's a key insight right there. It's the exponentiality of data. It's the exponentiality of automation, and it could take something that is relatively small and really make it pretty big pretty fast. Right?

Glenn Hopper:
Yeah, exactly. It's tough. Think about if you are your bank. 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 and to do it better. If you train the models accurately, that's great. Think about underwriting at a bank and think about getting through the loan approval process and where there's going to be committees, and depending on the loan amount and what it's for and all that, there's going to be different hoops that you jump through.

Glenn Hopper:
But say you're just doing consumer mortgages and maybe there's an area of town that is mostly rental properties, maybe it's densely urban population that is ... maybe there's other components in there where median income might be a little lower than other areas. Think about when you train these models, you're just putting in features. If you're trying to think of a credit worthy person that you're going to loan money to, you look at what their income is, what their credit history is, what their ... all the things that they'd have on the application.

Glenn Hopper:
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 compare that to our book of loans that are successful and our book of loans that have defaulted." If you've had some people in this ZIP code say that have defaulted, if you're in that ZIP code, by default, it's going to kick you out, and you could be discriminating because of using the machine learning algorithm. You got to be very careful on stuff like that.

James Robert Lay:
Exactly. I think that's where ... It's like when you 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 little small things that we need to be mindful of. I'm curious, how can financial brands identify and switch from, we'll call it, mindlessness to mindfulness that directly impacts their bottom line? It's what I always talk about, the lessons from four seasons systematized predictable so that we can humanize the exceptional where the opportunities go from mindless to mindfulness.

Glenn Hopper:
Yeah. Great question. I always say, you can't automate chaos. Before you start looking at automation and finding this mindful work for people to do ... This is my ... I've been in this startup 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 processes, but mostly they're still in that cowboy phase where everybody's just doing everything they can to serve clients.

Glenn Hopper:
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. 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 the processes you're doing, and then for me, it's almost like you do an ISO 9000 audit, where you go all the way back to the first touch point of any customer or potential customer, lead or 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.

Glenn Hopper:
Find a way to carry that customer persona or 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. You have the full customer experience mapped out, and then it's a matter of you generate the data, you collect the data, you process the data, you store the data, manage it, analyze it, visualize it, interpret it. People say digital transformation like it's a one-and-done thing. It's really more of an evolution. It just keeps going. Yeah, I'll stop there because I threw a lot out right there.

James Robert Lay:
No, no. I like where you're going with this, and I want to come back to the point that you said you don't want to automate chaos. You don't want to scale chaos. I think that right there is where it's so easy to miss some of this foundational diagnostic discovery work, and it's we want to take the past that's informing what we're doing here in the present moment, but that's not necessarily what is needed to get us to move towards the future.

James Robert Lay:
Sometimes you got to simplify before you multiply lessons from Dan Sullivan over at Strategic Coach. That's his whole perspective. Technology's a multiplier, so it's going to multiply whatever it is that you give it, and that's where I think a lot of times, "What's my role in this at an organization, at a financial brand?" Because a lot of times when we're going in and we're training marketing teams and sales teams and leadership teams, and we're having some of these conversations around AI and automation, the questions and concerns, even with marketing, AI is massively transforming that entire area, that entire discipline, all the way down to content creation.

James Robert Lay:
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. It's an amazing time, but it can also be a very scary time for "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. I want to come back to data here. 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 nobody is irreplaceable? What can we do here?

Glenn Hopper:
You said it earlier, it is the transition from mindless data entry, just entering numbers and entering in loan applications, whatever your job is. Entering in invoices to be paid, creating invoices for your clients, that kind of stuff that people used to do. There's off-the-shelf software now that does all this. Anything in the corporate finance world, there's ... I could Google it in five minutes and probably find a SaaS product that I could get for $100 a month that does what an AP clerk used to be, or whatever the case is.

Glenn Hopper:
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 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 mindful work and add that element."

Glenn Hopper:
It's not just turning over everything to the robots and saying, "You run the ship." It's the balance between human input and machine input and humans using the data and everything that comes out from all the machine learning as just another tool, just like you would ... If you're the CEO of this company, you're not going to give away all your decisions to your C team, to your management suite. 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.

James Robert Lay:
Correct.

Glenn Hopper:
You take all that and you still apply the human element to it.

James Robert Lay:
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. Exponential growth is where you're feeling like you're growing personally and professionally at the same exact time. Because through my work I'm finding that there's a lot of conflict that's going on. Conflict rooted really deep in the mind, almost a little bit of an existential crisis of what's my role in all of this?

James Robert Lay:
How do I fit in? Why are we doing this to begin with in the first place? 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 aversion within the financial services space. But it's almost like an Achilles' heel to a degree too. It's a double-edged sword. It could be a great strength, but it can also be a tremendous weakness as well. How do you recommend we deal with exponential change in the age of AI and really, 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?

Glenn Hopper:
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. I think that maybe a way ... Finance people certainly are risk-averse. It's probably the reason that we are in the profession because everything is just ... It's black and white. It's either a positive or a negative number. Everything can be explained. It's not 2D fruity marketing, where you don't know what's going on, what the ROI is on it.

Glenn Hopper:
We're looking for these concrete things. I guess 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. Exploit is doing what you've always done, do it better each time. If you're not exploiting, if you're not making slow and steady incremental change, then you're going to get left behind immediately.

Glenn Hopper:
But think about exploiting as just being the best you can at what you do. If historically, and in my role as a corporate finance guy, we've been record keepers. We're looking in the rear view mirror. We're saying, "This-"

James Robert Lay:
Yes.

Glenn Hopper:
"... is what we did last quarter. This is what we did to budget." You're basically the scorekeeper, and that's great. Businesses are always going to need that. But if you are 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 and much more quickly, then 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.

Glenn Hopper:
I think from a company level, it's like ... Look at Kodak or Blockbuster or people who forgot what their ... Blockbuster might as well become a real estate holding company at the end. They could not get around the fact that, well, Netflix is never going to beat us. We've got all the video stores. Now who would ever think about going to a video store? That's so archaic. But 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 changed. I guess, my advice and my patience would be thin for someone who is hesitant to change because I think history has shown time and again that if we ever stop, then we're going to get run over.

James Robert Lay:
I really like 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 in Digital Growthtopia because it's the only way the ADD mind that I have can actually remember things with word association. 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, look down and look ahead, keep looking ahead towards the future, otherwise you risk getting trapped in the cave of complacency.

James Robert Lay:
That creates a pseudo sense of confidence, like you said, Blockbuster and Kodak and all of these other brands. But it's exploring and exploiting. I really like that perspective. People always ask me, and I want to get your take on this here, "How much time should we really ... 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 bankers and credit union leaders, we're still busy executing on the day-to-day, we don't have time to think about the future.

James Robert Lay:
I'm like, "Well, that's not an excuse. That's going to get you run over." Because to quote Peter Diamandis, "The future's faster than you think in the next decade," and we're already two years into the next decade. 2030 is going to be here 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?

Glenn Hopper:
This is tough. I actually have a whole section or two in the book on this. One is you could be at the top of the world and your company could be crushing it with your performance right now. If that's the case, it's easy to say 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. I guess the short answer is ... I'm going to say this, but then I want to come back to it and talk about people who maybe don't have the SVP title or whatever they need to really drive decision making.

Glenn Hopper:
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 drive it. 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.

Glenn Hopper:
I talk about if you are a frontline manager, senior manager, someone in that realm, then maybe going across department finding a skunk work team, unofficial at first, putting together some plans and finding small wins that you can do to show-

James Robert Lay:
Yes.

Glenn Hopper:
... what happens if you move forward. I would say at the top level, hopefully the business is sophisticated enough that the senior management is out of the weeds of the day-to-day trying to do that. They've got trusted lieutenants below them that are working on really exploiting what they're doing and they're charting out the future. I would say at the top level, I hope you're constantly looking forward, and below that, it's just about ...

Glenn Hopper:
You've got to follow what the 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 C-suite position of how to do that. It is more of a challenge for the junior person.

James Robert Lay:
No, it definitely is, but I think it's a continuous commitment to review what you've done, learn through those experiences. Think about what are those next best steps forward, apply 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 has worked, because like you said, things can transform so quickly.

James Robert Lay:
Even from a data intelligence standpoint, I'm very interested in, and this has been a practice now for probably the past five to 10 years, but we have customer intelligence. Yes. But really the new is the how to use data and AI for competitive intelligence, and really gleaning insights from the marketplace and seeing what others are not either, A, able to see what we've not been able to see historically to apply that so that we don't get stuck.

James Robert Lay:
Because it's like you said, the past, when you say that our past does not define our future, that's a very positive thing if the past has been historically challenging and a little bit of a struggle. It's a very positive thing. But to say that our past does not define our future when we've been historically successful, that can be either, A, a little bit off putting and insulting, or B, can just be downright scary. I think that's where we probably need to lean into a little bit further.

James Robert Lay:
As we start to wrap up here, I want to get real practical, next best step. Something small that the dear listener can do to move forward on their own journey here because all transformative growth begins with a very small, simple step forward. What is the best next step for a financial brand to move forward with courage, with confidence in the age of AI, around some of these topics of things like machine learning and automation? What is one small thing that they can do next?

Glenn Hopper:
Let me back this up above even the machine learning component, because maybe this helps get you thinking down the right path. You don't have to know what a random forest is or what a neural network is. This is just thinking about data and how your company is using data today and what's out there. 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 data are you using or do you have access to today?

Glenn Hopper:
Then you consolidate all this data and you start thinking about what can I do with this? I'm going to give you four ways to look at data. It's an evolution of how you adopt this, move through this digital transformation and how you can use data. The first one is, I've done this audit, I see all the data, let me just take this step of describing the data that I have. 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 a month.

Glenn Hopper:
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. Then after that, one step deeper is figuring out why. You've got all this 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 us, why this month we had more people opening new accounts?" Or whatever it is.

Glenn Hopper:
Then you can start getting some value in that. You start finding correlations. You find things like unemployment went up in this region and loan defaults went up. Whatever. That's a super easy one. But if you're not ... But its hunch is to say that hypothesis is to say it, look at the data and prove it. You go through, you do the descriptive, you do the diagnosis, and then once you have a good handle on that, you start doing predictive stuff. You see-

James Robert Lay:
Yes.

Glenn Hopper:
... oh, the Fed is going to raise interest rates. What's the ripple of that? That's a leading indicator for what's going to happen here. What's that means going to happen for me in the future? Then you can map out whether you're doing your FP&A, your 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." then the final place that you get is prescriptive analytics, and that's where you say, "Okay, that's happened. What can I do to control this customer behavior?"

Glenn Hopper:
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, there are algorithms that make all this much easier and that you're able to process more data and everything. I think step back, and I'm always one I want go one level deeper and look at the why and the underlying thing. To me, taking that path, 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.

James Robert Lay:
I really like those four steps because, A, they're super practical, B, they're approachable, C, they're not intimidating because you're not talking about all of this ML and acronyms and things that you just are not common nomenclature 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.

James Robert Lay:
For example, diagnostic, predictive, prescriptive. We can take all of those perspectives, turn them inwards within our own customer data, within our own member data, to then make proactive recommendations to our account holders, to help them improve their financial wellbeing, which is then closely correlated with their physical wellbeing. Same type of idea, but just a different application. A lot of practicality tied to it all. Glenn, this is good stuff, man.

Glenn Hopper:
The last thing I would add in that is if banking is a mostly commoditized industry, if everybody's just competing on interest rate or available credit, what's going to make someone bank with you, and as banks get more digital, maybe it's harder and harder to have that personal touch with more online banks and everything. If you are doing something prescriptive that adds an ... it's actually valuable to your clients and you're giving them something different, that gives you a way to stand out in the marketplace, and that's cool. That's the power of data too.

James Robert Lay:
I'm telling you. I'm telling you one other the biggest opportunities that I'm really ... I'll collaborate with someone on it. I want to see this become reality, because it's a common pain point that I hear, is either, A ... I think AI automation, machine learning is a possible path, is when someone opens a new account, right? When someone opens a new account at a financial brand, that's step one. It's then getting them to move their money that's step two.

James Robert Lay:
But just because you move your deposits over, that doesn't mean that's a primary relationship, because 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. 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.

James Robert Lay:
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 795 a month, 1,495 a month? It's five bucks, it's 15 bucks, whatever. But then when you have 10 of those, well, that's 150 bucks. That's $1,800 a year right there. Now you're starting to get into some real pain points, but is it enough to go in and then have to cancel all of those services?

James Robert Lay:
That's another opportunity that I can see where you just come in and it's a plug and play, you run a quarterly or an annual, [inaudible 00:38:54] this is how much you're losing because of your subscriptions that you've signed up for. Well, we can solve 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 all together. Glenn, this has been a great conversation, man. Thanks for joining me. What's the best way someone can continue the conversation that we've started today to connect with you?

Glenn Hopper:
Yeah, probably the way ... I'm pretty active on LinkedIn. I'm on Twitter, but I don't really do much there. I think my mom and some guy went to third grade with my followers there. [inaudible 00:39:33] Yeah, LinkedIn is the best way. I've got a couple of websites, but again, they're pretty static. Just hit me up and we can put my LinkedIn profile in the notes of the show or whatever. But probably if you Google me or just search on LinkedIn, you'll probably get to me pretty quickly.

James Robert Lay:
I love it. Google Glenn. What's the best way that they can get your book? Where can they find that?

Glenn Hopper:
It is available on Amazon. 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 came out last summer. Unless you're a Richard Paterson kind of thing, I don't know what's actually in stock. But it's certainly available from Amazon and Barnes & Noble, And I think someone just told me they got a copy at Target the other day. It's out there.

James Robert Lay:
Get the book. Go to Amazon, get the book Deep Finance, connect with Glenn, learn from Glenn, grow with Glenn. Glenn, this has been a fantastic conversation. Thank you so much for joining me on another episode of Banking on Digital Growth.

Glenn Hopper:
Thanks for having me.

James Robert Lay:
As always, and until next time, be well, do good and make your bed.

Brief Summary of Episode #207

When you hear the term ‘machine learning,’ what comes to mind? 

Some may think of a bleak future with a world full of humans fighting Terminators. 

But the reality is that machine learning could be the next step in your financial brand’s evolution in digital transformation.

Glenn Hopper, Chief Financial Officer at Sandline Global, certainly believes in it.

The author of Deep Finance: Corporate Finance in the Information Age says that automation with proper oversight can filter the mundane from the exceptional.

In this episode of Banking on Digital Growth, Glenn and I break down the ins and outs of machine learning.

We talk about some potential roadblocks - and dangers - of using AI automation to handle your bank or credit union’s data.

Glenn also gives me his take on the risk aversion generally associated with automation and why it can be a nail in the coffin for financial brands.

Machine learning isn’t a dystopian algorithm designed to replace humans, but a tool we can use to humanize the exceptional.

 

Key Insights and Takeaways

  • 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)

Notable Quotables to Share

How to Connect With Glenn Hopper

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