Representatives from Wex, Home Trust and Wells Fargo sat down at Imagination In Action’s ‘Forging the Future of Business with AI’ Summit to talk about how AI has impacted the the financial industry on the business and retail side.
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TechTranscript
00:00 Hi, everybody.
00:03 We're super excited to be here today.
00:05 So this is a panel on the impact that AI is having on the financial industry.
00:11 And one of the things that's interesting with finance is that it's an industry that has
00:15 really embraced artificial neural nets for quite a long time.
00:19 I think Jan LeCun's seminal work on optical character recognition was in 1989.
00:26 By the end of the 1990s, 10 to 20% of checks were processed using artificial neural nets.
00:34 In the '90s, the industry embraced using neural nets to identify credit card fraud.
00:43 And so this is a technology that the industry is very familiar with.
00:48 That said, obviously, what we've seen in the past couple of years is completely new.
00:52 This has really changed the game.
00:55 We're fortunate here to have a panel that has a lot of experience, particularly in the
01:00 consumer-facing side of finance, which has really embraced this technology perhaps more
01:08 than the other sides of it.
01:10 So I will let the team here introduce themselves.
01:14 Can I go first?
01:16 Karun.
01:17 I'm Kiran Vipu.
01:18 I'm the chief product officer for commercial banking in Wells Fargo.
01:22 So I guess I'm the odd person out.
01:24 The rest of them are retail banking.
01:28 I'm Karen Stroop, chief digital officer for WEX, which is a B2B fintech focused and serves
01:35 800,000 customers globally.
01:37 Hi everyone.
01:38 Mike Henry, I'm responsible for product and channel and digital and data at a Canadian
01:43 financial institution called Home Trust.
01:46 Well, great.
01:47 So when we think about this industry, we have this huge amount of experience using the technology,
01:57 but things really have changed.
01:58 And so I guess my first question for you is for your teams in particular, what stayed
02:04 the same and what's different now?
02:08 I can start.
02:09 Yeah, I think, you know, one thing about banking industry is we are a very heavily regulated,
02:15 for all the right reasons, industry.
02:17 And that factors into everything that we do, particularly when we have to embrace a new
02:23 technology or new models and new quantitative techniques, et cetera.
02:30 So, you know, what has not changed is that governance, the accountability to be able
02:41 to explain how we make decisions, to be able to prove that the decisions we make are repeatable
02:49 and providing transparency into how the whole decision making structure within a given model
02:56 works.
02:57 Right.
02:58 So AI is no different.
03:00 We treat everything AI as a model.
03:04 And you know, so we'll have to put everything that we do, leveraging this technology through
03:10 the same governance and same rigor as well.
03:14 Right.
03:15 So that hasn't changed.
03:17 What also has not changed is we are maniacally focused on improving our customers' experience,
03:24 particularly at Wells Fargo.
03:26 So we are looking at AI and leveraging AI very aggressively, again, within the bounds
03:32 of that old governance and risk management to be able to improve sort of the customer
03:39 experience.
03:40 Right.
03:41 So that continues to stay.
03:42 And we're always looking at efficiency opportunities.
03:44 We are a bank, right?
03:45 It's all about efficiency ratios.
03:47 That's how we are judged.
03:49 So we are looking aggressively at leveraging the technology to continue to drive efficiencies
03:55 as well.
03:56 Karen.
03:57 Totally agree.
03:58 I'll build on that.
04:00 So two things that have not changed, we have used AI in our industry for years, particularly
04:05 in risk management when you think about credit adjudication or fraud detection and prevention,
04:11 personalization and marketing.
04:13 The other thing that hasn't changed is the customer problems that we need to solve.
04:18 And so whether they be businesses or the end users, for us, customers in the financial
04:23 services industry want to save time.
04:25 They want to make more money, more money in their pockets.
04:28 They need confidence in their decisions.
04:30 So those problems stay the same.
04:33 The two things for me that have changed are one, the tools available to solve the problems,
04:37 like exponentially more powerful.
04:40 And it opens up these new avenues to solve problems.
04:43 I look at parallels and analogs and other industries and think that our customers, what
04:49 they define as great is set by others.
04:51 So think about Amazon and the return process, like incredibly easy.
04:56 It needs to be that easy in the financial services industry.
04:59 I think the second thing that has changed is from an executive and a board perspective,
05:05 AI is not an option anymore.
05:08 It's an imperative.
05:09 And so I no longer have to advocate for it.
05:12 I'm actually asked to be bolder, which I think is awesome.
05:16 And Mike?
05:17 Thanks.
05:18 You know what, I'm going to start.
05:19 We negotiated as a panel before and said it didn't matter what order we go in, but I want
05:23 to revisit that, what they said.
05:26 No, segueing off what Karen said, I mean, look, boards are all over this.
05:29 I'm here today because one of our board members is an associate professor here at MIT and
05:33 a contributor, Hussain Romana.
05:34 So I'm super happy he invited me.
05:36 I'll try and say something a little bit different.
05:38 So what hasn't changed is the importance of trust.
05:42 So we can do all this cool stuff now, but if any of it, any of it gets in the way of
05:46 customer trust, you're dead on arrival.
05:49 So that's something we always have to be mindful of.
05:53 The big thing that's different, so forget about the technology itself, whether it's
05:57 gen AI or something like that.
05:58 AI has existed in banks for decades now, but tended to be incredibly specialized, incredibly
06:05 niche.
06:06 And I don't like the word democratized here, but now it really is accessible to everyone.
06:09 It's usable by everyone.
06:11 And that's a big change.
06:12 So maybe to follow up on that, Mike, where do you see opportunities for gaining a sustainable,
06:19 or at least a temporarily sustainable competitive advantage?
06:23 And where do you see things that will just erode to be the table stakes that you really
06:28 need to have without falling behind your peers?
06:31 Excellent.
06:32 Well, thanks for sending it back to me, Brian.
06:35 So look, I guess I would, I'd put it into two categories a little bit differently.
06:39 I think about everyday AI and game changing AI.
06:43 And I'd say, you know, to be completely truthful, I think the majority of what I see from almost
06:47 everyone goes in the everyday bucket right now.
06:50 It's the productivity stuff, the optimization stuff.
06:53 So we have lots of that going on.
06:55 It's just kind of marginal improvements.
06:57 It's super cool, but it doesn't sort of change the nature of the business.
07:01 I think, so I haven't actually really seen a lot of good examples of what I'd characterize
07:07 as game changing AI for financial services yet.
07:10 I'm hopeful we get there.
07:12 And look, unlike other industries, no one banks for the sake of banking, right?
07:16 You know, you go shopping, you get some gratification.
07:18 No one gets a dopamine hit off doing a deposit.
07:21 Like oh, yay, great.
07:23 So there's friction by definition in banking.
07:25 I think when we can take this stuff and we can start to make experiences more contextual,
07:29 more compelling, get the friction out, that's where I think we'll see the business model
07:33 evolve.
07:34 Just, you know, we're still looking for what that is.
07:37 That's a great answer.
07:39 Love it.
07:40 So building on that as well, you know, I start from a perspective that I'm, I would say,
07:45 constructively paranoid.
07:46 I assume that people are out to disrupt us.
07:50 And I think that AI will provide interim competitive advantage, but not durable because people
07:56 will catch up and those who don't get on board, I think will be disrupted.
08:00 And so for me, it's not about specific opportunities, but it's about three things.
08:05 One is creating a repeatable process.
08:07 And so that idea of how do you understand the customer problems, how do you innovate
08:11 and how do you have the enabling capabilities, whether it be data or the platforms, you know,
08:16 you have to have that process to continue to sustain yourself and differentiate.
08:21 Two is actually for me about trust.
08:23 And so you have to prove that you can, you earn and keep customers' trust.
08:27 If you provide advice, it's good advice.
08:30 And then the third for me is really about a willingness to disrupt yourself because
08:34 if you don't, someone else will be looking too.
08:37 And so I think those three things perpetuate durable advantage.
08:42 You guys have not left anything for me.
08:44 But I think I'll take a slightly different slant on the answers, right?
08:48 So I agree with everything that Mike and Karen said.
08:52 But if you think about banking industry, beyond sort of going to a digital sort of a dot com
09:03 of a given bank and doing your basic transactions, particularly in the sort of the institutional
09:09 side of the banking, a majority of the interactions are still sort of relationship based.
09:17 And it is somebody picking up the phone and calling a relationship manager.
09:20 So there's been a lot of friction in terms of pushing more digital interactions.
09:27 And we obviously want to push more digital interactions.
09:29 You know, for the reason that Mike mentioned, nobody gets a dopamine hit out of going to
09:34 a bank branch or calling a relationship manager, whatever.
09:39 So I think what generative AI is going to do, especially with sort of capabilities like
09:45 conversational interfaces, it's going to really sort of disrupt that relationship based banking.
09:54 And it's also going to help us take banking to the customer as opposed to the customer
10:00 coming to a bank.
10:02 We call it, I mean, the technical term we use in the banking is embedded finance, right?
10:07 I mean, institutional banking, and for that matter, any banking should be just like Apple
10:12 Cash.
10:13 I love that.
10:14 Right?
10:15 I mean, you don't even think about a bank at the end.
10:16 You don't think about ACH.
10:17 You don't think about wire.
10:19 You're just having a conversation and you want to be able to send money.
10:23 And it should be that easy, right?
10:25 I think that is where it's going to be sort of game changing in my view.
10:30 It's going to take a few years.
10:31 Again, we have to work through all of the regulations and make sure that, you know,
10:35 when we expose conversational interfaces, you know, one of the speakers said right now
10:40 generative AI is like a wild animal.
10:43 It is truly a wild animal.
10:44 So we need to sort of tame it to banking industry to follow the regulations.
10:48 But I think that's what is going to be game changing.
10:52 There's so much excitement about this technology and different things that we'll be able to
10:58 do, but there are also concerns.
11:01 And so this is if you think of large language models, these are things that hallucinate.
11:06 There are this is a heavily regulated industry.
11:11 There's the potential for new legal requirements.
11:15 Always burdens of internal restrictions on things.
11:20 Access to a talent base that have these skills is challenging.
11:24 And even getting access, obviously, to the computing hardware is very challenging.
11:30 So I'd love to know what are the concerns that keep you up at night?
11:35 Karen?
11:36 Very close.
11:37 Karen.
11:38 So maybe I'm an optimist, but I assume the enablers will work themselves out, whether
11:46 it be computing capacity, the regulatory environment.
11:49 You think about mainframe computers that took up huge rooms decades ago.
11:53 Like I assume that that's going to get fixed.
11:56 My concerns are where I spend time thinking are on two fronts.
12:00 The first is around the idea of with great power comes great responsibility.
12:05 And so we are using people's data, both our data, third party data, and I think there's
12:10 a lot of responsibility and we take that responsibility really seriously.
12:14 And the second for me is really on the human side.
12:17 And so as we look at the evolution, I think that AI and all forms of it will augment jobs,
12:23 but that's not a foregone conclusion.
12:25 And we also have a responsibility to help train employees, retrain them, teach them
12:30 to use this, show them how Gen AI can augment and not disrupt their job.
12:36 And I think that there is a societal obligation to that that I think a lot about.
12:40 Kiran?
12:41 Well, I think the good news is we have been a regulated industry for a long time and operated
12:49 in these constraints before.
12:51 And the industry also sort of adopted, you know, I mean, I remember, you know, back when
12:58 I started in this industry, it was very difficult to get a financial engineering grad.
13:04 So we figured out a way to get physicists and train them in financial engineering, right?
13:09 And solve for the sort of the talent crunch.
13:13 So we have got the muzzle in terms of figuring out how to solve for the talent problem.
13:19 That's the good news, right?
13:20 But we'll have to execute on this new wave of it.
13:25 Similarly working under regulation and in fact, contributing to and shaping the regulation
13:30 to some extent, right?
13:32 We have done that and we continue to do that as well.
13:34 You know, for example, at Wells Fargo, we are one of the first banks that signed the
13:38 Bill of Rights that Biden administration put out there in terms of pushing for that sort
13:44 of responsible usage of AI.
13:48 You know, and data privacy is also something that we have dealt with.
13:52 So I don't feel like these are newer problems, right?
13:56 There are definitely challenges that we'll have to, there may be some new challenges
14:00 too, right?
14:01 As the technology evolves and we start more aggressively leveraging it.
14:04 But we have done this before.
14:06 We can do it again.
14:07 >> It's interesting hearing you talk about privacy.
14:09 I was talking to one of the professors here out in the foyer.
14:14 And he mentions that the people need to stop thinking of owning data and buying data, but
14:21 instead being entrusted with data.
14:24 And I thought that that's a great way to think of it.
14:28 We only have a few minutes left.
14:31 Love to get your thoughts on what you think this panel will be talking about at next year's
14:37 conference.
14:38 >> Well, I can tell you what, it's hockey playoff season.
14:41 So I'm going to say, I hope we're talking about the Mabelese winning for the first time
14:44 in half a century.
14:46 But you know what?
14:47 I think I'm going to bridge off your last question too, Brian, and say, you know, I
14:52 hope we're talking about success in getting this stuff to kind of embed in our organizations.
14:58 Because I think for me, it's not about a talent crunch.
15:01 We can at the margin go hire new people that come in and they're conversant in new technologies.
15:06 But what about the other 99% of the population that are already there that aren't necessarily
15:11 starting off conversant and fluent in this stuff?
15:13 And we've got to do something, Karen touched on this a little bit, we've got to do something
15:17 to get those people there.
15:19 And so I hope that I hear us talking more about how we're doing that.
15:24 And even more optimistically, how we're being successful doing that.
15:27 >> Karen.
15:28 >> Yeah, I was going to say something similar.
15:33 So I think right now, if you think about this population who's here today, I don't know
15:37 if it's the 1% or the 5% of people who are AI believers.
15:41 We're all in here.
15:43 But the majority of the population I think is in a wait and see.
15:46 And so I think like last year was a year of experimentation.
15:49 This is a year of, okay, let's figure it out and make it real.
15:53 I think next year, there's going to be an urgency because you see people starting to
15:56 separate themselves from the rest of the pack.
15:59 And so those who are in the wait and see are going to really be trying to figure out how
16:03 we make it successful, how we scale it in the organizations.
16:07 >> I think we'll talk about AI like we talked about digital a few years ago.
16:12 This is the year where everybody's sort of dipping their toes in, especially large banks.
16:18 But next year and the year after is going to be about sort of industrial usage of AI.
16:25 And like I said a minute ago, things like conversational interface, things like embedded
16:30 finance, expanded credit offerings to customers that we don't usually do business with today
16:36 because we don't understand the risk that a customer brings, which is such an important
16:42 thing for us to make a decision to lend to somebody.
16:46 Those are the types of things we'll be talking about.
16:47 How can we expand it more and more?
16:50 >> So it's interesting that one of the themes is senior leadership has become much more
16:56 interested in this.
16:57 I'm curious, how do you balance the excitement for innovation and the drive to move forward
17:04 well being in a heavily regulated industry?
17:08 I think I'm going to have to pick on someone for that one.
17:13 >> Carefully.
17:14 >> Mike, let's start with you.
17:16 >> I think it's classic change management.
17:18 I think you've got to find small, quick wins.
17:21 And so I did a lightning talk earlier today, and one of the things I talked about there
17:25 was you need this portfolio of experiments, some where you have very high confidence that
17:30 you're going to get some impact, even if that impact is small, and some that might flame
17:34 out, and that's okay.
17:35 But with that first group that you're showing some business value and some traction, that's
17:41 how you just sort of get the flywheel turning and convert people over time.
17:46 >> Yeah, I have a board member who's an HBS professor who really encouraged us to set
17:52 bold targets, which I personally love.
17:55 What was fantastic about that is it changed the mindset not only of me and my team, but
17:59 of the organization holistically.
18:01 So we think and work differently, but we have a governance committee, we have a whole team
18:06 behind us that's figuring out how can we, not should we.
18:10 >> Yeah, I would say, you know, governance, but I think we'll have a problem, a different
18:17 problem, which is there will be more push to go more aggressive than less aggressive.
18:23 Because every time I sit in a meeting and there is a problem to be solved, now the question
18:26 I get is, why can't we AI that?
18:29 Right?
18:30 You know, kind of like there is an app for everything, right?
18:33 So I think that's really the thing, and it will be prioritizing and governance is how
18:39 we are going to get through this.
18:41 >> Got it.
18:42 Well, as we wait for John to pull us off the stage, I'd love to know, what are you most
18:46 excited about?
18:48 >> I'm -- can I start?
18:51 Yeah.
18:52 I'm really excited about the embedded finance and expanding credit.
18:57 Because there's a lot of folks that we don't bank with today for various reasons, because
19:01 there's not enough data associated with them, or the more traditional means are not enough
19:07 for us to get comfortable as a bank to lend to those folks.
19:11 Like, you know, credit rating is not enough, or for some people credit rating is not good
19:17 or whatnot, but that does not mean they are not credit worthy.
19:21 I'm really excited about that opportunity where we are able to reach more of the community
19:26 with AI and leveraging other means, other data points about those folks to understand
19:34 their credit worthiness, right?
19:36 And as I said, embedded finance, I'm super excited about it.
19:38 I think it's going to really transform.
19:42 I'm sure we'll get to a point where we'll give a dopamine hit for people to use banking,
19:46 but I'm really excited about that as well.
19:48 Go ahead.
19:49 Well, I get it.
19:52 So you know what?
19:53 I'll sound very similar to Kiran.
19:55 So I'm a banker by background.
19:57 I've done it for three decades.
19:58 I love it, and I've done it for as long as I have because I think it's really important
20:01 to society.
20:04 If I geek out on banking for a second, banks exist to be a transmitter of monetary policy
20:08 in the economies we serve.
20:10 So if you can do what Kiran just said and expand the credit box and expand the supply
20:14 of credit, you end up having a positive multiplier effect for the economies of those whole societies.
20:21 So that to me is why I do what I do.
20:24 Super exciting.
20:25 Yeah, so I'll answer it in two ways.
20:28 The first is, I work with a lot of small businesses over my career, and there is so much fear
20:33 and uncertainty and paycheck to paycheck.
20:36 And so I think there is a tremendous power to help people make better decisions and actually
20:41 improve their livelihoods and prosper.
20:43 From a personal perspective, I am thrilled to have a partner that helps me.
20:48 Thank you everyone, and thanks for having us.
20:50 All right.
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