In part 2 of our look back at the impact AI has had on banking, Alex Habet replays a 2018 panel discussion with Carl Ryden, Jim Marous, and Greg Michaelson.
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[Podcast] A Message That Was Ahead of Its Time
Transcript
Alex Habet
Hi, and welcome to The Purposeful Banker, the leading commercial banking podcast brought to you by Q2 PrecisionLender, where we discuss the big topics on the minds of today's best bankers. I'm your host, Alex Habet.
So two episodes ago, we aired a classic talk about the origins of AI, what the potential can be and how it applies to banking, and we titled that episode "A Message That Was Ahead of Its Time." It really was. If you haven't heard it, stop what you're doing right now, switch to that episode, skip the whole Alex beginning part, and just behold. If I could distill why it's so impactful, it's because it's the "why" behind all of this. I absolutely loved the reaction that we were getting from those who caught the episode. It was brought up multiple times during some of my on-site visits. But the package, of course, is not yet complete, which brings us to this episode.
So you've got the masterful delivery of the why. Now we follow up with a panel discussion on the exact topic, but this time discussing on how to put things in motion, the what and the how, as it were. Carl Ryden, again, hosted Greg Michaelson from DataRobot and Jim Marous from the Financial Brand. It was a great, thought-provoking discussion that helps you think of where to start. Find what's easily measurable, build basic wins off of that, build sophistication incrementally. Then, of course, how to take it next level. There's definitely some next-level stuff there, too. But it also covers things like change management and regulation, but doesn't really get in the weeds, which is great. That's a whole rabbit hole sometimes.
But what I also loved is hearing actual use cases and applications of this technology, which spurned the concept of "what else is possible," at least it was pretty instantaneous for me. But what makes it ahead of its time is that all the struggles and the barriers of meaningful adoption discussed back then absolutely still apply today.
But now we're seeing new tools, new barriers that were recently even brought in, suddenly are coming down. It's spurring a flurry of testing and trials all over the place. I'm hearing it from people that we work with. We're doing it here internally. You're reading about it in the news. It's happening, right? It's going to accelerate. But this discussion gives you a blueprint on how to approach it.
Unfortunately, I couldn't include the Q&A portion from the audience because the audio levels were a little weak, but if you're interested in catching that, head over to bankonpurpose.com. So without further delay, friends, sit back, relax, and enjoy a panel discussion that was ahead of its time.
Carl Ryden
We're going to talk a little bit today about using AI in banking to drive behaviors. I've got with me two guys who know a lot about this. You guys remember Jim from this morning, he's been around the world and has seen what banks are doing in different avenues around the world and what's possible. Also with us is Greg Michaelson from DataRobot. Greg is on the tip of the spear of making things real in this world. So we're going to talk a little bit. We'll have a few questions. I'll ask these guys a few questions, then we'll open up the questions from you guys, if that sounds good. OK?
My first question is, you see a lot of big dreams and you see a lot of kind of hero stories and all the things that Amazon and Google and Facebook and all these guys are doing. But a lot of times at a bank, it's how do you get off zero? How do you get some positive momentum, start building the miracle of compounding, make things like 1% better? What sort of things have you seen as really good projects to get off zero or really good first use cases to kind of start building that muscle within a bank?
Jim Marous
I think first of all, at the Digital Banking Report, we've done two research studies now. We did one last fall on AI, and we did one just in the last month on machine learning, slightly different in concept, but both of them went out to the marketplace and said, "Where are you? What's the digital maturity of your organization in the realm of AI and machine learning?"
What was interesting in both studies, it came up with a couple really key elements. Number one, the only organizations that are really doing very much in the AI and machine learning space are the bigger organizations, a generalist theory, but basically, it's the biggest firms. And I think this kind of reminds me of mobile payments, where five years ago, man, you could not read any kind of publication in the financial service area without it talking about mobile payments. And then, you start looking at the reality of the world and you saw that, "Oh, we're talking about penetration of 8-16%." It never got really off that until recently. But I think the buzz in the marketplace on the concept versus the implementation is vastly different.
I think what we also found was that AI, as I mentioned I think this morning, the application of AI in the marketplace right now is more of an evolution than a revolution. People are doing a little bit better at what they used to do. And almost every organization, whether everybody's aware of it or not, are using AI for some purposes internally, but almost always regarding risk and fraud. So we have a lot of AI learning around what are the things that indicate that a loan's going to go bad, a customer's going to become a bad customer. There's some risk involved, but very little when it comes to personalization and development of products. So what we're seeing is while there's talk about it, the application into new areas: personalization, product development, innovation, things like this, really isn't starting very good. So that's not really an answer to your question, but it kind of sets the fear level that right now it's not all that advanced, I should say.
On the other hand, and I'll defer to you then, is that we're seeing that most organizations that are moving forward in the AI space are not doing it internally, and this goes across all sized organizations. One major reason, and that is there's just not enough talent in the marketplace, there's not enough experience in the marketplace, and people are more likely to feel more comfortable going out and testing ... Some people that have already done it or done it for others than going out there and doing it themselves. Starting from scratch right now is a tough way to go, even on the most basic functions.
Carl Ryden
Well, that gives us ...
Jim Marous
I don't know if you've seen the same thing.
Carl Ryden
Well, that actually gives us a good chance of, speaking of someone who does it for others ...
Jim Marous
Right, right.
Carl Ryden
... for a living, particularly for banks. It's probably worthwhile for you to introduce yourself and then talk about doing it for others and how do you help them get off of zero.
Greg Michaelson
Yeah, so I'm Greg Michaelson. I'm the General Manager for Banking at DataRobot. I'm a Data Scientist. You can tell by my inappropriate choice of footwear.
Jim Marous
You're getting shooed there.
Greg Michaelson
And my inability to maintain eye contact. But yeah, I'm a data scientist. I started out building commercial credit scoring models at Regions Financial back in the day, back before the meltdown and everything. But over the last three years, I've been at DataRobot, really kind of helping organizations figure this thing out, right?
There are really two paths that organizations go down. Well, three. One is, "We don't want it. We don't care." Right? I'm going to set that one aside, because that group of individuals and organizations is getting smaller and smaller, particularly as you see all these fintech companies come up into the lending space and the payment space and gobble up little bits of the market. It's freaked people out, and so nearly everybody is in the market of saying, "Hey, we've got all this data that we're collecting, how can we use it?"
One path that people go down is the vendor path, right? There are core models that everybody needs. You need fraud models, you need credit scoring, you need loan loss forecasting-type models. There are three or four or five sort of core models in a line of business that are absolutely necessary. A lot of organizations buy them. That's an expensive way to go about it, but if you're not interested in developing that capability internally, then that's the route you go down.
The other route is the one that you should go down, and that is becoming AI-driven, that's what we call it, where you're looking at every line of business, every organization, every functional area in your department and saying, "How can I use my data to optimize, to automate, and to learn more about the way my business works?" I've seen one cross-sell use case trying to identify customers for targeting, for deepening of a relationship, generate $10 million a year in revenue for one line of business, over the course of one year for one product in a relatively small business banking unit.
It's amazing where these use cases are, everywhere from even HR analytics to prospecting and sales to fraud. One Canadian bank is using machine learning to reduce its investigation costs on AML, right? So if I can weed out the ones I have to investigate, they actually cut their expenses by 60% by using machine learning to cut out the false alarms that they were getting out of their process.
So to me, the key to getting off zero is to teach the organization to spot opportunities because there are literally hundreds of them in every line of business. And if your executives and your leaders and your line of business people, if you can give them the keys, as it were, the ability to spot opportunities to use AI, then that's going to unlock the potential. I think that's the way you get it, absolutely.
Jim Marous
It is interesting, because you bring up a good point that I kind of referred to a little bit this morning, is that the best way to start is to find direct paybacks, is to say, "OK, this is going to give me this much cost savings or this much revenue, and it's almost a guarantee." It's one of these things that you can fall upon it and it's going to do well for you.
Carl Ryden
Yeah.
Jim Marous
The key, though, when you're looking toward the future, beyond just staying in the current, is to say, "How can you connect that with some kind of customer experience modeling that will take it beyond simply lost reduction or cost reduction?" And what's good is you have money to start with. It's kind of like customer onboarding, is that very few companies are ever going to say that a customer onboarding program doesn't make them money.
The key is, how do you make that then a little bit more than that, and maybe use some of the revenue that's going to be generated from that, the reduction of losses, to apply toward better customer experience? Because at the end of the day, most of the things we're talking about in this room, in the room earlier, and in any of the rooms, are going to be things that make us money.
We have got to find ways that are going to make the customer experience better, that's going to make us more revenue because we know them better. And one of the keys that you're bringing up is that most of these models take in outside information as well to make the models more powerful. If we're going to be taking in outside information, let's not take it in simply for taking it in and building great reports and great cost reduction models. Let's bring this in to apply even more insight into, "How can we make this into a product development or a customer growth tool?"
And it's the other side of the same coin. To be able to pre-approve somebody, to be able to go down the path and say, "You know what? This tells me enough that I can take some risk on the customer because of the fact that I reduce the risk on other people." That's the key to the future. That's what the consumers going to be expecting.
Carl Ryden
Yeah.
Jim Marous
They don't sit by and go, "Oh, thank goodness, they saved some money on costs, or they reduced their risk." That doesn't affect them.
Carl Ryden
Yeah.
Jim Marous
But I think it's important to make it that next step.
Carl Ryden
I think it actually ties back a little bit to your talk ... into the last talk about removing friction and adding to light.
Jim Marous
Yep.
Carl Ryden
Right?
Jim Marous
Exactly.
Carl Ryden
And that kind of leads the relationship to a certain place. OK. So getting off zero, there's some use cases and kind of learn to smell and find the value. And then there's inevitably, I think, one thing that holds folks back is the unknown unknowns. It feels like we've never done this before and it feels like, "Once I get into it, man, the way our IT organization works and the way this works, and I've never ... " Everybody who needs it doesn't know how to make it happen within the organization and all the stuff that's going to get thrown in their path. What are some of those? Can you reveal some of those, demystify some of those, and turn them into at least known unknowns at this point?
Greg Michaelson
Yeah. We run into these blockers all the time. Probably the biggest one that you're going to face is detractors. It turns out all problems are people problems. That is no different in this space, right? So you're going to look across your organization and you're going to see people that own servers that really, their number one priority is making sure that nothing breaks, ever. And if you do something that's outside the box, then that makes them incredibly nervous. And so, they want to delay, stall, and stop. You're going to see compliance people that have forgotten, maybe they never knew what the rules were, but they are sure the answer is no. You can't do that stuff. They think that independence is the opposite of collaboration, right?
And so, whatever you're looking at, it might be data scientists, it might be regulation people, it might be IT, or it might be business people that look and say, "Ah, I don't really need help with credit scoring. I can spread a financial statement and I know how that works. Thanks, but no thanks." Right? So you're going to look out into your organization, you're going to see detractors. And they can bring things to a halt, so it's super important to kind of be prepared for that and to be aware that there are people in your organization that are going to look at these kinds of new technologies and say, "That's not for us." And they're going to have interesting and good-sounding reasons, but those may be the reasons kind of on the way down.
Carl Ryden
I do have one ...
Greg Michaelson
Yeah, go ahead.
Carl Ryden
I'm answering my own question, so I apologize for this. But the one thing we've seen is just there's a lot of places in the organization where, really, folks are operating with a bit of a wet finger in the wind, is where right now they have to make a judgment or make a prediction. There's somebody who needs some number to fill in this application, and they don't know what it is, but they say, "You, Mr. RM, tell me what this is." Right? And so, they're making a guess. They're going, "Ah, here's what it is." And I always find it to be easy to kind of look for wet fingers in the wind. And then, the old story, you don't have to outrun the bear, you've just got to outrun the other guy, right?
Greg Michaelson
Yeah. Right.
Carl Ryden
It sets a threshold of up, and then when compliance says, "Well, I don't think yours is accurate, it's not a 100% accurate," because it never will be. It's a prediction. But let's rewind the tape here. How are we doing this today and having that good benchmark of, "Today, we're making this up. Today, we're just pulling this out of the air. Yeah, it's 80% accurate." And then, they start applying all ... Because you're a thing now. They can apply all of their compliance and stuff to a thing, whereas before, there was no thing, people were just making it up.
Greg Michaelson
Yeah.
Carl Ryden
And you say, "Look, point that at what we're doing now and tell me we can stay there." And I think that's a good opportunity to find places where you can get off zero and overcome some of these things.
Greg Michaelson
Well, change management is a thing.
Carl Ryden
Correct. That's right.
Greg Michaelson
Change management is definitely a thing. In the next five years, what's going to happen is that tools are going to become so much better, whether it's building models or deploying models, or Andi's going to become so smart, or whatever it is, that businesspeople are going to have to become more technical or they're going to become completely irrelevant. And technical people are going to have to become more businesslike or they're going to become irrelevant. Tools are kind of blurring those sort of polarized specialized skills so that people are having to be pushed toward the middle and to be able to understand both the technical details and the business details. That's definitely what we're seeing.
Jim Marous
Yeah. One thing I saw is, and it's interesting because I just saw the picture again, about five years ago, I went to Poland and saw mBank in their development of an online and mobile app for consumers that basically resulted in a one-minute online and 30-second mobile consumer loan. The whole concept was, we want to make money available to them, virtually every one of their customers, to some amount on a clickable button."
The reality of that is the way that got done, the only way it got done, was they put representatives from every interested party in a room and had the leadership of that organization say, "Here's what your goal is. Here's what we have to achieve. You guys work together to find a way to do it," which makes it a whole lot different than sequential turndowns, where everybody said it was OK, but then compliance comes back the second time and says, "I liked it the first time and I said this, this, this. You have made those adjustments, but now I still don't like it." So they have a mission that says, "I'm not going to like it at all, but I'm going to find that one excuse all the way along the way."
You get people in a room and you get leadership that says, "We need to accomplish X," whatever, X may be, a mission. And you have everybody in the room saying, "How do we get to that?" That makes compromise a whole lot more likely.
Greg Michaelson
Don't let them out.
Jim Marous
Well, that's about it. The picture I have is 22 people in a room with white pages all over the boards on what had to be done. And so, everybody had to move a little bit. So it wasn't going to be, "Oh, I'm going to give everybody $10,000." No. Even giving $500 made the consumer feel like they were worth something. So you had different components. You had contingency approvals, whatever it may be.
But this goes with any mission out there, because to your point, we're going to be getting into very uncomfortable space as bankers. We're going to be asked to do things in the future that our consumers are going to want, that they're going to say, "I don't care if you can or can't do it, find a way." We need the leadership to say, "We're going to achieve this. We're going to put all the interested parties in a room and say, 'By the way, you have to get there.' " So eventually, you've got to come out. It's like a jury. You can't leave that room with a hung jury. And at the end, everybody's going to have to give a little bit, but at the end they're going to come up with a solution. It may not be exactly what they went in there to achieve, but it's going to be a whole lot closer than doing it the way we traditionally do it, which is not agile. It's basically a waterfall.
Greg Michaelson
Right.
Jim Marous
A waterfall will not work because you're trying to move people into new areas.
Carl Ryden
Quite a lot of organizations just do waterfall really fast, and they call it agile.
Greg Michaelson
How many folks in the room have ever deposited a check with your phone? Yeah. Who knows the first bank to ever do that? It was USAA, right?
Carl Ryden
Yeah.
Greg Michaelson
And it was like three years between when USAA started doing it and Bank of America and Wells Fargo and some of these others started picking it up, right? You've got to believe that the guy that said, "OK, we're going to do this at USAA," that there had to be people saying, "What's going to stop people from taking pictures of fraudulent checks?"
Carl Ryden
Cash them multiple times.
Greg Michaelson
Or multiple times? Or what if it gets lost and the person no longer has the check? There's all kinds of reasons not to do that, but now it's industry standard.
Jim Marous
Well, industry standard to a way, and this applies to every part of the business model. At Wells Fargo, where I have my personal account and I have quite a bit less money than I have in my business account, they give me the ability to do a remote deposit capture up to $100,000. That scares me, OK? But they allow me to do it. PNC allows me $5,000 a month. And I told them, I said, "You see the flow of money in my account. You see how much I take." And by the way, I have to take a picture of a check to transfer money between PNC and Wells Fargo twice a month. I've never written more checks than I'm writing now, because of the fact that transfers between institutions are just broken. So I go to PNC and I say, "OK. Can you give me the ability to do more?" They go, "I'm sorry, but the limit is the exact same for everybody."
Greg Michaelson
Yeah.
Jim Marous
I'm sorry. It doesn't take just a banker to say, "WTF? What are you doing? What do you mean it's the same for everybody?"
Carl Ryden
Yeah. So you're the machine learning, WTF detector algorithm is fairly easy to build.
Jim Marous
Oh.
Greg Michaelson
Yeah. It's super easy. They're accept only.
Carl Ryden
Yeah. So I've got one more thing, one more question, then we'll open it up to the audience. So getting off zero, what's sort of the unknown unknowns and there's compliance and IY, and that's the normal things, but they are overcomeable.
Greg Michaelson
Sure.
Carl Ryden
And they are. And I think bringing them into a room, getting them on the same page, and giving them a charter where for the compliance officer or the IT person, right now, failure is defined by the narrow scope of their job. Once you put them in that room and, "Your mission is X," now failing at mission X is on their job.
Greg Michaelson
Is a failure, yeah.
Carl Ryden
And they have to balance that. And I think that's really ...
Jim Marous
And they're risk-adverse.
Carl Ryden
And they're risk-adverse and they don't like to fail.
Jim Marous
Yeah. Right.
Carl Ryden
And you don't want that. And so, hero stories, a lot of work to do to go through all that. And you mentioned a little bit about one about the cross-sell. What are the other kind of ones where you go, "Gosh, this is one where we really unlocked a lot of value," and one that you've seen too, too?
Greg Michaelson
Yeah. There there's mountains of them. So if you think of any line of business, you've got a huge opportunity to invest in machine learning and automate some decisioning, right? So one example is the AML use case that I mentioned. So almost every AML use case that's out there today is rules-based, right? If the transaction has characteristics A, B, and C, it raises a flag. And you can't miss one, or it's a big problem. So the tendency is to generate as many as possible and then throw people at them, right? So you have 10, 20, 50 people that do nothing but investigate these AML cases and say, "Alright. Is this severe enough to generate a SAR?" And if it is, you fill out the suspicious activity report, send it off to the feds, and that's the last you ever hear of it, right? Which is awesome. It's a great feedback loop there. Thank you. Thank you, federal government. That system could actually work if there was a feedback loop.
But what these guys found is that if they could build a predicted model to say, "Will this flag, will this transaction, generate a SAR?" So they're just predicting the outcome of their investigation. And it turned out that they could, without missing a single SAR, eliminate 60% of the work in that entire process. So imagine the savings there, just from an expense perspective. So AML is a huge one.
Fraud is the same way. So most fraud detection systems like point-of-sale fraud, transactional fraud systems or deposit fraud, or application fraud. or identity fraud, any of those are rules-based, right? How many have ever had their card declined when you're traveling like on vacation? Yeah, I hate that. It makes me crazy. But now, I get an email from Citibank whenever I travel. It says, "Oh, we noticed that you bought a plane ticket to Austin. Don't bother calling us because we've already made sure that your card is going to work." That's awesome, right?
There's smart AI and there's dumb AI. How many of you have ever called their cell phone company on the phone? They treat you like they don't know you. The first thing they say ...
Jim Marous
Every time they transfer you, they ask the same question, oh, yeah.
Greg Michaelson
"Please enter your phone number, followed by a pound sign." They invented caller ID. OK?
My cell phone company knows everything about me. My bank knows everything about me. They know where I shop, they know when I shop, they know where I vacation. They know everything about me. They know what mail they send me. When I call a call center, they should be able to figure out why I'm calling and route me. And if I'm upset, they should be able to send me to the right person. If you read somebody's file, you can figure out where they are and who they are. And imagine how you could impact customer churn, retention, customer satisfaction, all those things, just by being smarter about how you interact with customers that are reaching out to you. The technology is totally there.
Carl Ryden
Bringing all the data and intelligence that exists anywhere within the organization, to focus it into that moment where you can actually create a better customer experience.
Greg Michaelson
Yeah. Well, exactly.
Jim Marous
And the challenge also is that we have situations where the difference is between a digital-first organization and a legacy organization. So when I started my business, I had to accept credit cards as part of my payment process for subscriptions. Well, to get a credit card process even approved, you have to set up a reserve account at another company that takes a percentage of every transaction and builds this amount up so that if anybody ever wants returns or has a problem that they will pay back the money and that's covered.
Well, this amount kept on getting bigger and bigger, and it was a legacy organization. It got up to the amount of $40,000. Well, we've never had any return, any subscription canceled. Just by the nature of the business that they don't understand, you had to put up a reserve. But that was my only option. Today I was mentioning to both of you, PayPal, which now, by the way, I don't get credit card payments anymore directly. Credit card payments now all go through PayPal for me. No banker has any problem with that. We send it that way. We build up balances. I transfer them once a month to my traditional bank.
But what's interesting is PayPal has built a knowledge of what my business is. They came to me today with a capital funds business loan that said, "We have a business loan waiting for you. Simply go through this short process. We'll approve it." Now it's a fee rather than a rate. As he mentioned, it's probably going to be a user-y, limit-type thing. But most small businesses don't run on logic. They run on simplicity and need. So if I need money, if I need cash, I need it today. And I may not care how much I have to pay for it so I can have it and I'm going to only pay X amount of fee, a short fee. It's no different than a person that goes to an ATM that's not theirs and pays $3 to take out $100. That is not a real good interest rate for cash that you already own.
But I think what the reality is, PayPal is using machine learning and advanced analytics to be able to determine what can they give me based on what they know about me, which is simply transaction volume and amounts. While the other organization is saying, "We're going to go the legacy route. We know you have a credit card. We know you accept this many a year. It's going to be X percent no matter what kind of business you're in." So I think it's important to look at it that way.
Carl Ryden
Yeah. I think a lot of those are moving more to the front of the bank, moving more toward experience and revenue.
Jim Marous
Sure.
Carl Ryden
I think the takeaway is a lot of the places kind of start with an evolution from the place out, that at least there's places where they're comfortable with those. Find the low-hanging fruit that folks or people will maybe be making just wet fingers in the wind.
Jim Marous
Yeah.
Carl Ryden
Target those, because now you've got a good stalking horse to say, "Well, it's better than that."
Jim Marous
Yeah.
Carl Ryden
Get off zero. Begin to improve. You can point to success stories and then kind of grow into some of these, start building that track record of success. And then, ultimately, it's going to take over.
