Transcription:
Penny Crosman (00:03):
Welcome to the American Banker Podcast. I'm Penny Crosman. What does it take to successfully lead AI projects in banks, and what are the defining characteristics of the people and companies that do this well? Our guest today has been a financial industry observer and expert for decades, whom I've known for a very long time. Dan Latimore led the banking group and then research at Celent. He was director of research at Deloitte and he was global research leader at IBM before that, to list a few items from his resume. He recently moved to The Financial Revolutionist, where he's building a research practice. Welcome, Dan.
Dan Latimore (00:40):
Thanks very much, Penny. Great to be here.
Penny Crosman (00:42):
Thanks for coming. So you recently did a report where you looked at how banks are implementing AI and you grouped the leaders into four categories, which I thought were interesting. So the first one was "AI titans." What are those and what have those companies achieved?
Dan Latimore (01:02):
Well, the titans are really firms that you would expect to be in the forefront of AI by virtue of their size and resources. And it's not a foregone conclusion that they'd be there, but because they are in the very largest echelon of financial services, they've just got more resources to put behind AI, not just using vendors to accomplish their goals, but also having staffs of, in most cases, many hundreds of developers to pursue not just run of the mill AI initiatives, but ones that are very tailored to their specific needs. And so you see the likes of Citi and Bank of America and Capital One and RBC and JP Morgan, among others.
Penny Crosman (01:57):
So then you had the category of AI implementers. Who are you thinking of there?
Dan Latimore (02:04):
These are firms that don't have those resources of the biggest banks, but among them are a lot of the superregionals, but also regional banks who are doing the hard work with the resources they've got to make AI happen and more often than the titans, they're reliant on vendors, but they also have pretty good staffs themselves who are devoted to helping integrate vendor technology, but also in certain instances develop initiatives on their own. And you see folks like KeyBank and BBVA and Citizens and Regions and Truist in there, as well as a few smaller banks who are doing pretty interesting things and making more of a bet than some of their similarly sized peers.
Penny Crosman (03:07):
Alright, and then you have AI mavericks. I like that term. What were you referring to there?
Dan Latimore (03:13):
Well, to me, AI mavericks are really the most interesting group. I would characterize them as punching above their weight. So we intentionally didn't do our grouping by asset size, but there is a strong correlation, but despite being smaller and having fewer absolute resources, these folks have generally from a leadership position on down, taken the view that AI is something they've got to be involved in. And they may not have dozens of experiments going on right now, or half a dozen announced pilots or initiatives in production, but nevertheless, they are dipping their toes in the water, seeing where they can realize an economic return and then doubling down when they see success.
Penny Crosman (04:15):
So they have a fearlessness even though they don't have a lot of resources, they have an ability to execute with a smaller team.
Dan Latimore (04:27):
Exactly. They've got an ability to execute. They're willing to go out and try new things despite a relative lack of resources compared to their bigger competitors. But they often are starting out on the gen AI side or even regular AI side with chatbots. There's a lot of fraud activity there, and we're seeing them realize some really interesting returns, and it's good for them to be out there because they recognize that to stay competitive with these larger firms because that's a new set of competitors for them relative to a decade ago, they've got to be out there on the forefront of technology.
Penny Crosman (05:18):
And then your last category is digital first. Who are those companies?
Dan Latimore (05:24):
These are a lot of the digital banks that have begun with new tech stacks. They are generally less than a decade old. They have very few branches and because they are technology oriented, they are built almost exclusively on modern cores. They don't have this legacy technology debt that they've got to deal with. So they're just in a better shape, not just in being able to devote some of their resources — and they are much smaller than all the other banks we've talked about — but not only can they devote some of those limited resources, they are digitally minded from the get go. So by their very nature, they are just that much more inclined to experiment with this new technology.
Penny Crosman (06:29):
Do you think that overall an ability to deploy AI skillfully and successfully is becoming a competitive advantage for these banks? And if so, can small banks really compete with the big banks that have such huge technology budgets?
Dan Latimore (06:53):
It's very interesting to think about where firms should do this. I don't think that any competitive advantage is really going to be lasting over time. It will give a leg up for a little while, but just like a community bank, and there are community banks among our AI mavericks, just like they've got to compete with the likes of a Citi or a Bank of America in terms of satisfying their customers, their customers generally are looking to them for something different than those multinational banks. So they don't have to offer every bit of AI or deploy AI in the same way that the AI titans are, but they've still got to be thinking about how it can be used, not just for offense, if you will, so providing better customer service or letting their bankers, both relationship bankers and at the call center, do a better job servicing customers, but also to play defense if you will. So to guard against fraudsters who are themselves using AI in evermore creative ways.
Penny Crosman (08:18):
That leads to another question I wanted to ask you about, which was about use cases. Which are the use cases that you feel are really bringing about good results today in some of these banks?
Dan Latimore (08:36):
I would, in stepping back, think about big buckets of AI use cases. I talk about five of them, and there are interesting use cases in each of them, but the first is just digesting data. And so you've got, say, a fraud analyst who has got to potentially sort through hundreds if not thousands of pages of potential documents. If the AI assistant can help in digesting that and flagging the most salient points and/or digesting that huge corpus of data into something much more manageable, that's a big help. The second big bucket I'd put in there, and this in some ways applies more to the bigger banks who've got huge legacy overhang and technical debt. It just aids in coding and not just in testing or writing and developing testing new code, but also translating things like COBOL into more modern languages that have a much bigger developer base who can actually deal with them.
(09:59):
The third bucket I'd categorize is just pattern detection. So particularly in fraud, going out and looking for anomalies that should be investigated further by an analyst. And all of these, by the way, humans need to be in the loop at some stage. The fourth one that is pretty interesting that goes into a squishier direction, if you will, is just generating first drafts. So on the marketing side or on the advisor side, who might be composing letters to clients. Or even on the customer service rep side and the call center putting together a first cut at what a customer response should be and then giving that to the human to get over that blank page problem. And then the fifth, I just categorize as natural language processing, and it's just gotten so much better with gen AI than it was five years ago. And so people can get answers, both employees and customers, to basic questions and get pointed in the right direction for a certain piece of information rather than having to dig through lots of menus or compose in very carefully constructed syntax, some kind of query of a database. So those are the five buckets that I'd think about it in, and each of those has some pretty interesting use cases.
Penny Crosman (11:49):
I like that breakdown. That fourth one to me feels the riskiest, letting AI create the first draft of anything. Everyone always says there's a human in the loop, but what if that human only gives a cursory glance and misses something huge? I'm probably not taking an enlightened view in this case, but I just feel like a lot can go wrong with that first draft concept, but I'm happy to be proven wrong over time.
Dan Latimore (12:22):
I think it certainly can, and with all of these, you've got to have the same controls in place, and whether it is for one-to-one communication, random compliance checks or audits or whether it's on a marketing communication, a standard process where things should always be reviewed by at least two people, you've still got to have those checks and balances in there and have people understand that there's a process that they've got to follow.
Penny Crosman (13:01):
What about the people leading these projects? In your research, have you thought about the leadership traits and personality traits of the people who do this well? And do you have any thoughts on what kinds of personal qualities and leadership qualities need to be brought to bear to execute well in this area?
Dan Latimore (13:31):
Well, the first thing I'd observe is that you've got to have buy-in from the top of the house to really make this meaningful because you want to have resources devoted to the initiatives. You want to make sure that learnings from across the bank, whether it's a relatively small community bank where it's much easier or a behemoth or a titan, in our case, you want to make sure there's a mechanism where experiments and learnings can be propagated across the institution, so that mistakes, which will be made, but they're made once and only once, and then rectified, rather than having the same mistake happen in four different areas and just have four different learnings. It's kind of like the whole self-driving car where once it encounters an anomaly, every other self-driving car picks up on that. But without that executive support mandating that cross institution collaboration, you're going to have a very tough time. I think the next part is there's a huge element of change management here. And so change management principles apply to AI initiatives as well as they do to any other kind of initiative. So keeping people informed, having checkpoints, publishing progress reports, having metrics and goals, and talking about how you're meeting them, all those are crucial. And just because gen AI in particular is a new technology, doesn't mean that governance goes out the window,
Penny Crosman (15:33):
It probably becomes more important.
Dan Latimore (15:35):
Absolutely.
Penny Crosman (15:37):
Because the capabilities are so advanced. Well, Dan Latimore, thank you so much for joining us today, and to all of you, thank you for listening to the American Banker Podcast. I produced this episode with audio production by Adnan Khan. Special thanks this week to Dan Latimore at The Financial Revolutionist. Rate us, review us and subscribe to our content at www.americanbanker.com/subscribe. For American Banker, I'm Penny Crosman and thanks for listening.