Welcome to Executive Edge, a video series featuring expert insights on top technology trends. In this episode, we explore how AI is transforming the financial services industry—enhancing innovation, streamlining operations, and fortifying security measures.
Lauren Langer 0:03
Hello and welcome to Executive Edge.
I’m Lauren Langer, Director of Marketing and Hartman Executive Advisors, and I’ll be facilitating today’s discussion.
Today we’re delving into AI and financial services, driving innovation, efficiency and security.
Joining us are two distinguished experts, Shea, Vice President of Relationship Development for Financial Services at Hartman Executive Advisors, Shea collaborates with financial institutions to align IT strategies with business objectives. His experience includes aiding banks in navigating technological advancements to enhance efficiency and security.
Also joining us is Anne Balduzzi, Executive Advisor for AI and Innovation at Hartman Executive Advisors, is recognized among the region’s Top 40 Women in Tech. Her career spans roles at Apple and AOL, and she now guides organizations in leveraging AI to foster innovation while ensuring security.
Let’s explore how AI is transforming financial services.
Welcome, Ann and Shea to get us started.
My first question to you guys, how can a bank or an insurance institution identify the appropriate owner of AI strategy within the organization?
And how does it align with their overall IT strategy?
Shea Gabrielleschi 1:30
Thanks, Lauren.
and Anne, Good to see you.
I’m, you know, excited to be here in such great company and I’m really excited for where this conversation goes.
Anne Balduzzi 1:33
Yep.
Shea Gabrielleschi 1:39
There’s obviously a lot to talk about with this topic of AI for banks, insurance companies, other financial institutions.
I’m on the road a lot and every conference I go to, especially in the last year, AI has been at the top of the list in terms of popular topics.
So you know it’s, you know, it’s on top of executives minds and I certainly hear that in conversations that I’m having with financial services executives.
So I’m really excited to get into this discussion, hear your thoughts and from your perspective on how AI is changing, particularly this industry.
And you know, I know for one thing, you know, getting back to Lauren’s question, teeing this up.
My my two cents is that when it comes to AI and who is owning that at the bank, obviously it’s got to be cross functional, but it really needs to be with a person or a group of people that understand the business first.
It can’t just sit in it, because if it does, it’s just going to get it’s it’s not going to drive positive business outcomes, so.
You know, I, I’ll, I’ll leave that there and you know let you add to that Anne and continue this topic.
Anne Balduzzi 2:59
Yeah, I’m actually a big fan of companies establishing governance models and as part of the governance model it, there’s a committee that ’cause you really do need a committee that is made-up of people from leaders, from all different parts of the organization because there are going to.
Be numerous AI initiatives.
There’s like unlikely to be just one.
There’ll be several of them. As this technology grows and flourishes.
And so having that team of individuals that are are charged with aligning those AI, you know, kind of pilots that might be taking off or other, you know, initiatives aligning those with the strategic goals of the business and making sure that a real good assessment is actually taking.
Place in advance of that so that there you know more than just aligning with the strategic objectives they’re aligning with the future goals of the organization and how that business is going to move forward, especially in these very, very competitive times.
And so that committee sort of acts as like the kind of the base where, you know, if there’s an issue that’s going on, it’s a place where employees can report back.
It’s a place where decisions can be made.
It’s a place that can monitor the success or failure of a pilot.
So that’s I think one of the core pieces that needs to be established right off the bat.
Shea Gabrielleschi 4:17
So I know, I know. Companies love committees.
What also happens with committees is there’s really no one person to sometimes put put the onus on when when a project fails or doesn’t, you know.
Isn’t completed by the deadline.
What do you recommend to companies, particularly with with AI committees?
How can they maintain accountability within the organization to make sure that that these these policies and initiatives are getting mented in a timely manner?
Because we all know AI is very fast moving and so, you know, I’m just imagining a bank or insurance company that that comes in and sets up this committee, and things just aren’t getting done fast enough.
So what do you recommend for that?
Anne Balduzzi 5:05
Yeah, you have to have a what we call a strategic AI road map and that’s going to actually help you stair step the different AI objectives. And by the way, feel free to change what those objectives are along the way because the technology’s constantly changing. So just.
Because you’re tied to a specific AI road map that you might have created at the beginning of this year, or even at, you know, at some point in the year, the technologies are changing.
So you’ve got to go back and sort of reassess that road map on a regular basis.
It helps to have somebody sort of leading the, you know, all of these initiatives, I have a preference for somebody sort of in your IT department because they’re, you know, have a history of having lead different initiatives throughout an organization and likely already have connections into all the.
Different, you know, areas of the organization and all the different departments. It’s really important that everything is done above board very ethically.
I think that’s true of every AI initiative everyone wants to see that happen and make sure it’s transparent all the way down to the employees and your staff so that people understand how the AI is working as best as possible and how that is going to impact them.
Over the long haul.
Lauren Langer 6:19
And as we’re discussing committees, what do you guys think the biggest challenges that the committee and the financial institution are going to be facing when they’re adopting this AI driven solutions?
Anne Balduzzi 6:32
Yeah, definitely. Legacy systems that are already in place is gonna be a big challenge.
That doesn’t mean to say you can’t work around that.
I mean some of the newer AI systems are actually working quite well with legacy systems that might already be in place.
But you’ve got it. You know, kind of.
You know, look at it from a really holistic process and if there is, you know, if you’re trying to get one initiative off the ground and maybe it’s not going so well or it’s sitting in pilot purgatory, you know, feel free to shift gears and work on somet.
Else that might be giving a more immediate impact and it’s those small initial wins, especially wins that are going to bring back dollars into your, you know kind of AI future AI initiatives that are going to allow you to have that spend. So that you can go off.
And do those other initiatives down the road?
Shea Gabrielleschi 7:21
And I I also think data is a huge roadblock early on for a lot of banks when they’re when they, you know, they get excited they they found an AI solution and and a perfect use case. But then they realize as soon as they open up the the.
Box. Oh wait, our data has to be in order.
And most banks don’t have their data neat and tidy, right?
And most financial service companies do not have.
Crystal clear data.
No, no one’s going to have perfect data.
But is your data as accurate as it practically can be?
Is it accessible?
You know, a lot of these legacy systems make it very hard to extract your data.
But there other there are a lot of other companies out there. I’m not going to name any, but there there are a lot of other solutions out there in the banking world and in the insurance world that maybe aren’t AI themselves, but obviously help the company.
Create, you know have accessibility over their data and ownership over their data so that they can easily use it when implementing AI products and services and so.
It most most of these companies are already down that that, that path, right and they’re taking ownership over their data.
But there, there’s also a lot that are not, and so it’s important to get that in order to.
Lauren Langer 8:45
That makes sense.
I mean, what do you guys think?
The real world examples should could be for these financial institutions that are trying, you know, people that are really successfully leveraging AI today.
What are those that kind of? What are some of those examples? If they do it right, they get past those challenges.
Where can they land?
Shea Gabrielleschi 9:03
Well, I I’ll let Ann dive into this, but I I’ll just say from the conversations that I’m having with CEOs, what I’m hearing all the time is that the number one focus from an AI perspective use case that CEOs are having right now is fraud, fraud, fraud and.
You know that is, you know, they see AI as a, as an opportunity to get more sophisticated about preventing fraud. And they also see it as a as a cause of fraud in a threat.
So you know, Anna, I know you have a lot you want to speak on that, but you know, in addition to that, we’ve got, you know, there’s AI usage use cases in lending, customer experience. You know, internal efficiency, but fraud is is definitely the number one topic that.
Keeps coming up when it comes to use cases.
Anne Balduzzi 9:53
Yeah, I unfortunately AI has taken your average hacker and turned them into a super hacker.
There’s just no way around that. You know people, you know, even banking customers used to be able to sometimes spot fraud just because it was a really poorly worded, you know, e-mail or something or text message.
But now A is writing those, and they’re perfectly written.
And they’re harder to, you know, uncover.
AI can even mimic somebody’s voice, so it could sound just like your voice calling in or somebody you know an employee’s voice that’s changing a routing number or something to that effect.
So these are a lot of serious concerns that banks should actually, I would say, up the game on their cyber training, because employees need to be aware of all the different ways in which you know these new phishing scams and other activities can occur within their settings. But.
On the flip side, there’s a lot of opportunity with AI ’cause. You can have sort of like, you know, in the sense of AI, you can get thousands of tiny security guards that are watching the money and that can catch those fraudulent transactions before they ever happen. So.
That’s sort of your predictive analytics of being able to spot it, and AI is capable of spotting things that are, you know, human brains don’t often spot naturally, so it gives.
It gives you that sort of a little bit of edge of tracking that suspicious money movement or something that a human might miss.
It also can help with other complications when it comes to, you know, government regulations or, you know any other things that might be occurring over time to make sure you’re in line with, you know, all those different changes that will definitely be surfacing over the next year or.
2.
And then monitor and it’s going to monitor these risks, by the way, in real time.
So it’s not just during the occasional check up, this is going to be a constant ongoing. You know monitoring scenario.
So you really will have that advantage to catching the bad guys, so to speak, when you’re implementing these, you know solutions in the right way.
Shea Gabrielleschi 11:54
And before we move on to the next topic, can you can you talk briefly?
Can we talk for a minute about copilot?
Because I know a lot of these institutions have are are in the Microsoft 365 environment and copilot is is, you know, comes up a lot. I’m sure in meetings you know do we need to use it? Is it worth it?
The price keeps going up.
Can you?
Can you speak on that?
I know we’ve internally been testing out copilot, and there’s there’s some great features.
There’s some features that aren’t quite, you know, up to snuff yet.
Can you speak on that?
Anne Balduzzi 12:25
Yeah, I think the key thing with all of these AI solutions is you want to make sure that you read the fine print and that it is based on the type of business you have, that it is giving you the security factors.
You need a copilot.
Luckily, does have very secure features already in place.
Probably one of the better ones, but if you look at other enterprise editions.
They also have some of those same security protocols in place, but they’re changing.
So you’ve got to constantly be looking at it, but even more so.
So it’s important to have employee policies in place so that your employees know which systems they’re allowed to use, because you very easily could right now have employees using any number of the other large language models that are out there and might be most often using the free.
Versions of those other models and those free versions do not always come with those protections, and so therefore they could be putting personally identifiable information up or spreadsheets and trying to do their job more efficiently.
But in the but in the long run that language models actually learning off that data.
And it is actually releasing that data in a very unsecure environment.
So it’s really super important to have those employee policies in place so employees know what what they can and can’t use.
Lauren Langer 13:39
How do they go about getting one of those if they, you know, do they go on the Internet and search like AI policy for financial institutions?
Like, how do they start putting that together?
What is the best way for them to get get moving?
Shea Gabrielleschi 13:52
Ask AI to write one for you and.
Anne Balduzzi 13:58
That that might give you like a baseline from which to start off of, but it really helps to work with people that have expertise in this area, people who have written several of these types of policies because there’s the technology’s changing. Frankly every day. I mean there.
Are large language models now that allow you to share your screen and walk you through things on your screen in real time and and that creates a whole new set of you know?
Risks, you know, so you’ve got to keep these policies continually up to date. And if you’re and even these, you know, large language models don’t always know about the latest, greatest risks.
So it’s important to work with experts in this area to make sure that your policies, your governance models and things like that are really reflecting the current technologies and evolving with those technologies over time.
Lauren Langer 14:47
Makes sense. So say we have a financial institution out there, which I’m sure there are tons that are like, OK, we’re bought in.
We know we got to do this.
We’re going to get left behind if we don’t for them. What are the first steps that they need to take in order to get started?
Like what? Education do Ce OS need in order to lead AI innovation within their institution?
Shea Gabrielleschi 15:11
Go ahead and.
Anne Balduzzi 15:12
Yeah. So it’s really helpful to have have all the leadership within an organization to have at least the same baseline understandings of AI people who are being educated about AI from, you know, some people might have heard about it through social media. Some people might have attended a.
Conference. But when everyone’s not playing, you know with the same, you know, set of understanding.
You know it can change how you view different, you know, potential applications of AI.
So starting out with sort of AA101 workshop of understanding so the people know what the tools are, how those tools can be implemented, and how best to brainstorm new AI use cases for their organization and use cases that are going to really help them.
Achieve the goals they’re looking to achieve, and then beyond that it’s, you know, sort of, you know, helping people as they stair step through you know all these different initiatives really helping them to review each initiative on its own and over time developing a matrix and that mat.
Is going to help them put all of those applications into focus so they know where to start first.
Shea Gabrielleschi 16:19
That’s that’s. That’s a great. That’s great advice.
Lauren Langer 16:20
OK.
Shea Gabrielleschi 16:22
And you know, a lot of CE OS, they’re they’re in this fork in the road right now because they they see that AI is here and that it’s moving so quickly and they’re they’re almost paralyzed because they they know that they have to embrace AI and lean into.
AI.
But they’re they’re that’s a hard thing to do.
And so it it takes it takes.
Some patience, but also it it takes a lot of, you know, smart thinking and bring in outside help to help develop a road map.
You know, start small.
I mentioned earlier the data hurdle that can be a daunting task for a lot of institutions, but.
Our recommendation to banks usually is.
You know, start small.
Put together a cross functional committee.
Get your data in order and you know start with a A use case. A practical use case that’s easy to accomplish.
And then, you know, develop a road map so.
You know, we we didn’t get into compliance much.
That’s another issue, but.
There’s, you know, it’s it’s not.
It’s not as complicated as a lot of Ce OS probably think it is, you know, wouldn’t you say that, Anne?
Anne Balduzzi 17:39
Yeah. And some of the use cases to start out with are relatively simple to implement and are not that costly.
The people are implementing chat bots, for example using all kinds of interesting applications there, and that actually those chat bots, as they get more and more sophisticated, are becoming like people’s top financial advisors.
So it’s it’s funny that you know here, you know, you don’t have to be a rich person to have a top financial advisor anymore. Or when it comes to AI, so you can get that advice, you can get Apple, you know, insurance applications and things like that. I.
Mean. There are companies that are having these turned around in minutes and seconds, so it’s not taking days and weeks to analyze.
You’re actually able to give tools to you know, when you’re assessing a loan application, you can pull in data from a wide range of areas to make.
A better judgement on that loan application.
So the list of opportunities goes on.
And on.
Lauren Langer 18:40
Wonderful. Alright, well, guys. Well, that’s gonna wrap up this episode of executive Edge. A huge thanks to both you Shay and Anne, for sharing your insights on how AI is driving innovation, efficiency and security and financial services.
You know, as we discussed, AI is not just a buzz word, it’s completely reshaping the industry and enhancing operations, strengthening security, enhancing customer experiences.
So thank you everyone that tuned in for us today. If you found this discussion valuable, be sure to like, share and subscribe for more executive insights into topics, top technology trends and until next time, I’m Lauren Langer and this has been Executive Edge.
Thank you guys.
