WEBVTT
00:00:03.600 --> 00:00:05.360
Welcome everybody.
00:00:05.599 --> 00:00:17.440
You're with today's Workflow Payment, a podcast, where we help payment professionals content with the challenges of modernizing their payment operations and identifying ways to build for the future.
00:00:17.600 --> 00:00:23.039
If you're interested in payment innovation and modernization, you're in the right place.
00:00:23.920 --> 00:00:27.039
Now, let's get into it.
00:00:33.039 --> 00:00:35.439
Hey everyone, welcome to Wrestling Payments.
00:00:35.520 --> 00:00:36.640
Today we got a great episode.
00:00:36.799 --> 00:00:41.280
We're going to talk about a new product that solves a really, really important problem.
00:00:41.520 --> 00:00:46.479
The reason I sound a little uh odd, Sean Carter, who's joined me today, and Toli Chia.
00:00:46.799 --> 00:00:52.880
Sean Carter, president of Nietzsche and NPG, he's talked forever about the importance of data.
00:00:52.960 --> 00:00:55.520
You have to have data, analytics, you have to use the data.
00:00:55.679 --> 00:01:00.079
Today we have someone that that proves him right, which is very uncomfortable for the rest of us.
00:01:01.039 --> 00:01:03.039
Sean, do you want to just introduce yourself, say hi?
00:01:03.359 --> 00:01:03.759
Yeah, yeah.
00:01:03.920 --> 00:01:07.200
No, so uh thanks, Joe, for having me on Wrestle and Payments.
00:01:07.359 --> 00:01:08.879
Um, very excited today.
00:01:08.959 --> 00:01:18.400
Uh, president CEO of Nietzsche and and Nietzsche Payments Group, and one of the things that Nietzsche needs to be healthy is that our financial institutions are healthy.
00:01:18.480 --> 00:01:24.560
And the way I've been saying for years that financial institutions can remain healthy is to make strong decisions.
00:01:24.799 --> 00:01:31.920
The best way to do that is to be able to have the data, which we know from talking with a lot of you, it's it's kind of all over the place, right?
00:01:32.000 --> 00:01:33.680
Which is good because you have a lot of it.
00:01:33.840 --> 00:01:38.799
The bad is that it's it's not always as easy to get as you would think, even though it's your data, right?
00:01:38.959 --> 00:01:40.959
And this is just an FI issue.
00:01:41.120 --> 00:01:48.640
I talk about this all the time at the Nacha level where Nacho will say we can't get certain information from the operators, and I'm like, it's your network.
00:01:48.719 --> 00:01:49.519
What are we talking about?
00:01:49.599 --> 00:01:51.680
You can't get information from the operators.
00:01:51.760 --> 00:01:59.519
So I'm very consistent on this issue of it's your data, you should be able to have it and get it, and then allows you to make informed decisions.
00:01:59.599 --> 00:02:01.599
So excited to have this conversation.
00:02:01.760 --> 00:02:05.680
Just quickly, how I how I met Tolley is uh is interesting as well.
00:02:05.840 --> 00:02:07.200
Um, a lot of you don't know.
00:02:07.280 --> 00:02:22.000
I was kind of a train wreck when I first took over as CEO of Nietzsche, and I was uh lucky enough to be pushed to join Vistage, which is a CEO group, and Toli happens to be on my group, uh, and we've had a chance to get to know each other a little.
00:02:22.080 --> 00:02:27.919
And if you guys are uh interested or impressed by intelligence, this this is what we're talking about.
00:02:28.080 --> 00:02:30.319
Is Tolly's mind just works differently.
00:02:30.400 --> 00:02:33.439
So I hope that comes across in in this podcast.
00:02:33.520 --> 00:02:40.000
We'll try to team up with questions so that he can just shine because uh really bright individual.
00:02:40.319 --> 00:02:41.840
And Toli, please.
00:02:42.080 --> 00:02:44.639
Thank you, thank you, thank you, uh Sean, for those kind words.
00:02:44.719 --> 00:02:51.759
And you know, on way of background, yeah, I started off my career a couple years as an assistant bank auditor while working with FDIC.
00:02:52.000 --> 00:02:55.120
Did a lot of the regional banks here uh in terms of bank examination.
00:02:55.280 --> 00:03:00.000
You know, I decided that, you know, being a bank auditor wasn't m my calling in life.
00:03:00.080 --> 00:03:07.840
So I spent uh about 20 years in the investment and wealth management industry, focusing mostly on the analytics side of the business, right?
00:03:08.000 --> 00:03:10.479
Focusing on uh the operation trading aspect.
00:03:10.639 --> 00:03:16.560
I built out the infrastructure, the core infrastructure for the investment banking firm that I worked for.
00:03:16.800 --> 00:03:21.199
After leaving the investment industry, I decided, you know, why don't just start something different, right?
00:03:21.280 --> 00:03:24.479
Why just do something totally off uh out of the field?
00:03:24.639 --> 00:03:30.240
So I I bought into a water company, a water purification company, and knew nothing about the industry.
00:03:30.400 --> 00:03:34.000
But it really gave me a lot of insight onto how to run a company, right?
00:03:34.159 --> 00:03:41.919
From uh sales and marketing to running a book of business to managing, you know, 20 or 30 people that are looking at you for key decisions.
00:03:42.159 --> 00:03:45.039
And that led me down to creating several other companies.
00:03:45.199 --> 00:03:55.599
And one of them is the company we're talking about right now, which is uh Predion.ai, which is a decision intelligence platform that really solves some of the data issue that Sean is talking about, right?
00:03:55.840 --> 00:04:04.400
It allows executive, it's an active engine that allows executives to really test out decision before they even risk one dollar capital uh into it at all.
00:04:04.800 --> 00:04:08.560
Sorry, I have that moment where I I become an audience member and I'm like, huh?
00:04:09.520 --> 00:04:16.800
You know, the genesis of our product really stems from uh another company that uh my partner and I started, Anjid, who's not on the call.
00:04:16.959 --> 00:04:24.879
But we had a uh we have a uh consulting company that focused on helping, you know, medium and uh small size businesses grow.
00:04:25.040 --> 00:04:33.920
And one of the challenges that we had was accessing the data, getting the data and quantifying in a manner that makes uh allows us to make uh insightful business decisions.
00:04:34.079 --> 00:04:41.519
And the problem was that is the crux of the problem, whether it's small business or banks that we work with, you know, two problems that persist, right?
00:04:41.680 --> 00:04:43.839
One is data quality is an issue.
00:04:44.000 --> 00:04:51.040
And then the second is there are a bunch of solid data living in disjointed systems that don't talk to each other, right?
00:04:51.199 --> 00:04:55.920
Now one of the things that people don't really underestimate is the root cause of data quality.
00:04:56.079 --> 00:05:05.839
You know, if you think about this, you have the perfect technology uh in place, but if you have messy data going in, it's garbage in and garbage out, right?
00:05:06.000 --> 00:05:06.560
On the send.
00:05:06.800 --> 00:05:16.879
One of our um advisory board members, uh, who you who is a former uh CFO of a regional bank, he actually did an audit of an institution uh around here.
00:05:17.040 --> 00:05:25.120
And what he he didn't audit their core uh banking system, what he uncovered during that time was that that bank had four deposit products in there.
00:05:25.519 --> 00:05:27.439
I'm sorry, 400 deposit products on there.
00:05:27.600 --> 00:05:41.519
So what happened there was basically over the years of the banking operation, someone was just manually putting in these uh uh deposit products in there, whether it's a promotional product that they did over summer, but they keep on compounding over the years and adding up, right?
00:05:41.600 --> 00:05:46.160
And if you think about it, if banks are doing merging acquisition, they're just bringing more products in there.
00:05:46.319 --> 00:05:54.000
And so now the data is just ballooning into this messy pile of data where it's impossible to do any analytics around the deposit portfolio.
00:05:54.160 --> 00:06:01.600
The other challenge that I see outside of just the data quality, I imagine, is the fragment data living across uh several different systems.
00:06:01.759 --> 00:06:03.199
So think about this, right?
00:06:03.279 --> 00:06:09.120
Uh simple question, uh, an executive will walk into a board meeting and say, hey, who are most profitable clients, right?
00:06:09.279 --> 00:06:09.759
On this end.
00:06:10.079 --> 00:06:17.519
A very simple answer, but it's a very complex process for banks right now because all the data live in separate systems, it's messy.
00:06:17.680 --> 00:06:34.079
So if you have data in your uh core system, you have your data in your loan origination, you have your data in your CRM, the simple answer of who's my uh most profitable client becomes a week-long process versus something that they could uh you know um push out in a few minutes, right?
00:06:34.399 --> 00:06:40.160
Um so by the time they get the answer and the board needs to make the decision on it, the opportunity might have passed already, right?
00:06:40.240 --> 00:06:48.240
Or even worse, a competitor that's actually have a better process around data normalization, data duplication has already got in and took that opportunity.
00:06:48.480 --> 00:06:54.879
And Joe, this is where where I got interested with Tolley and Sanjeev when they were kind of explaining what they were doing.
00:06:55.040 --> 00:06:57.040
You think about this in your payments business, right?
00:06:57.120 --> 00:06:57.199
Right?
00:06:57.360 --> 00:07:05.439
Nietzsche's payments and and we love payments and trying to, you know, there's a couple of key inflection points in your payment strategy, right?
00:07:05.519 --> 00:07:07.439
It's number one is what do we offer, right?
00:07:07.600 --> 00:07:12.879
But then, you know, beyond that is kind of what we're should be focusing our time.
00:07:13.040 --> 00:07:15.519
What is the most profitable products we have?
00:07:15.680 --> 00:07:34.160
And the example that always comes to mind to me, and you know, I love the the whole ISO world in card processing, is a financial institution looking at businesses such as credit card or or processing fees like that, that is coming from a totally different stream than maybe everything else they're offering.
00:07:34.319 --> 00:07:39.199
And so when when somebody does ask a very straightforward question like, how are we doing?
00:07:39.360 --> 00:07:41.360
There's obviously an accounting answer to that.
00:07:41.519 --> 00:07:45.439
Anybody can look at a P ⁇ L and say, Oh yeah, or we're losing money, right?
00:07:45.600 --> 00:07:46.560
Or we're making money.
00:07:46.639 --> 00:07:51.120
But to Tolley's point, there's a deep there's deeper questions under that, right?
00:07:51.199 --> 00:08:00.879
And so even I think what happens on board discussions is sometimes they get stalled because everybody knows not to go any more deeper, because you probably can't get the answer, right?
00:08:00.959 --> 00:08:04.800
And so the question becomes very basic, like, are we making money or not?
00:08:04.959 --> 00:08:08.720
When really what you're probably trying to get at is what's our most profitable segment?
00:08:08.879 --> 00:08:10.959
Like, where should we be focused next?
00:08:11.199 --> 00:08:13.360
You know, where we're where are our threats, right?
00:08:13.439 --> 00:08:27.439
Like what industry, like if 80% of our revenue is from one industry and you see that big bank ABC that has specialization in that industry is coming to your area, that's a bigger threat than if it was a different bank coming into your network.
00:08:27.519 --> 00:08:45.840
And I what Tol showed me, and we've we've actually gone out to some member institutions and talked about this, and he's done some demos, and and what it allows you to do is really get at that inflection point to say, you know, we need to know from this system and this system in order to change pricing.
00:08:46.080 --> 00:08:58.240
But the the beauty, Joe, this is what you would love, because anyone that ever, if you ever watch Nietzsche operate, I am like so quick to make decisions, and Joe's like, whoa, whoa, whoa, we just have to take a little more time to decide.
00:08:58.480 --> 00:09:01.279
Joe's that secondary, well, what if this happens?
00:09:01.600 --> 00:09:03.919
Which totally brain works the same way.
00:09:04.080 --> 00:09:05.679
What if we did change pricing?
00:09:05.840 --> 00:09:12.960
Before making that decision, this tool allows you to say, what does that make my income look like over the next couple years?
00:09:13.120 --> 00:09:14.879
What if the interest rates change?
00:09:15.039 --> 00:09:18.559
How does that impact this decision I'm about to make over here?
00:09:18.720 --> 00:09:26.080
And if you don't have all your data, let's say you have 67% of your data, you're getting 67% of the decision, right?
00:09:26.240 --> 00:09:28.799
It's it's it's it's an amazing.
00:09:29.039 --> 00:09:38.000
I didn't understand what they were doing at first, but when he put it into banking terms, because he has the bank around background, I'm like, this is this is perfect.
00:09:38.159 --> 00:09:40.000
I know other systems exist.
00:09:40.240 --> 00:09:45.360
However, some of those are provided by the same provider that doesn't allow you to have all your data.
00:09:45.600 --> 00:09:48.960
So somebody listening to this may say, oh yeah, we already have that tool.
00:09:49.200 --> 00:10:00.879
Pause before you say that 100%, because if if it's the person that's limiting your access now to what you can see, if it's their tool, it's also not seeing those other things that may live outside.
00:10:00.960 --> 00:10:11.840
And when we talk about payments in fintech and how much revenue you want to drive, it's really important to see all of that because you know, you know, Joe, you and I get to see this all the time.
00:10:12.000 --> 00:10:17.759
Some people are skittish about fintech relationships, even though they can be very lucrative, right?
00:10:18.080 --> 00:10:23.360
Because, you know, the regulators get, you know, they want to see a bigger program, that there's all kinds of risks.
00:10:23.440 --> 00:10:39.200
So if if you're not giving everybody the full information on the board about what these relationships are really looking like, and all they're saying is the increased headaches that the relationships are bringing, or employees are saying, hey, this is too much to deal with, you know, we've seen this in the third-party center space.
00:10:39.360 --> 00:10:46.080
People exit the business and they're giving up a lot of opportunity sometimes because they're probably not seeing the full picture.
00:10:46.240 --> 00:10:54.080
Um, and you know, this this process that Tully talks about with the data just allows you to do that at a board at executive level.
00:10:54.159 --> 00:11:01.679
And I even talk to them about this could be within a department for your treasury people to kind of look at pricing and things that they want to do.
00:11:01.840 --> 00:11:08.080
So it's a it's it's a fascinating journey, but it all starts from that simple premise of it's your data.
00:11:08.559 --> 00:11:10.080
You should be able to see it all.
00:11:10.159 --> 00:11:11.600
And it doesn't always work like that.
00:11:11.840 --> 00:11:13.519
I actually think you brought up a good point.
00:11:13.600 --> 00:11:19.120
Um, you know, a lot of this could be solved if you have the capital resources to just throw money at it, right?
00:11:19.200 --> 00:11:20.720
I think Bink Bank has that, right?
00:11:20.799 --> 00:11:23.440
They are at a hugely competitive advantage.
00:11:23.759 --> 00:11:30.399
Not only do they have the money to throw an implementation like a snowflake or build out a data department, right?
00:11:30.480 --> 00:11:32.000
Or even hire the best talent.
00:11:32.159 --> 00:11:34.480
You know, community banks don't have that, right?
00:11:34.559 --> 00:11:42.480
Uh they're very uh constrained by you know a b uh the budget, and any talent that they might have, they might have difficulty retaining them, right?
00:11:42.639 --> 00:11:47.200
Because you know, a fancy fintech companies come in, a bigger bank could double their salaries, and you're gone.
00:11:47.360 --> 00:11:51.120
Where I think Predion comes in, it levels the playing field for them, right?
00:11:51.279 --> 00:11:58.480
In this case, is that it allows community banks the same type of solution where they'll have access to uh clean data.
00:11:58.639 --> 00:12:14.799
And also one thing that I think that gives us uh a really competitive advantage is that not only do we provide the best, what I think is one of the premier technology, is that we also provide a financial analytical expertise that will come in and help you use the technology.
00:12:14.960 --> 00:12:15.759
So think about this.
00:12:15.840 --> 00:12:23.279
It's like not only you're buying a fancy sport car, you're getting an excellent driver to drive uh drive that fancy sport car.
00:12:23.360 --> 00:12:26.320
It's not we're asking you to drive the spancy sport car.
00:12:26.480 --> 00:12:28.159
We're we're doing it to drive it for you.
00:12:28.480 --> 00:12:32.320
I have an objection, an observation, and a question.
00:12:32.639 --> 00:12:34.799
So my objection, it's not really an objection.
00:12:34.960 --> 00:12:36.559
The ACH is 50 years old.
00:12:36.720 --> 00:12:41.679
Lots of times they've built systems on top of systems, on top of systems, on top of systems.
00:12:41.759 --> 00:12:45.679
So finding walking into a a bank and saying, Oh, can you pull this report?
00:12:45.840 --> 00:12:48.159
Uh we know we can't do that.
00:12:48.320 --> 00:12:54.159
So I just want to defend those my my my community bankers saying, you know, sometimes it's it's hard.
00:12:54.320 --> 00:12:59.200
The the observation that that core, you know, the root cause of the problem.
00:12:59.440 --> 00:13:06.720
How if I'm listening to this podcast right now, how do I know that this solution solves my problem?
00:13:07.039 --> 00:13:10.480
Maybe because I haven't actually scoped up my problem enough.
00:13:10.639 --> 00:13:15.759
What what problem how do I know that I can put the connection together that I should be looking at Pretion?
00:13:16.240 --> 00:13:18.960
Um this this would help me solve that problem.
00:13:19.200 --> 00:13:21.919
Do you have examples of how you came in and solved a problem?
00:13:22.159 --> 00:13:28.639
Yeah, well, I'll give you an example, but I think let's take a step back what uh what makes Predion uh different from other platforms.
00:13:28.720 --> 00:13:32.799
But I I think Pretion itself, that software itself is top-notch.
00:13:32.879 --> 00:13:37.919
It's you know, but I don't think the technology is the one that makes us that sets us apart.
00:13:38.159 --> 00:13:44.159
Where I really think that sets us apart is the financial analytical analyst that comes in along with it.
00:13:44.320 --> 00:13:46.720
You know, our analysts understand banking, right?
00:13:46.799 --> 00:13:56.240
They understand what's the difference between loan attributes and deposit attributes and how to look at raw banking data and say, you know what, that data is good, that data's bad.
00:13:56.480 --> 00:13:58.960
They could understand it uh and they speak the language of that.
00:13:59.120 --> 00:14:01.200
And I think that's a huge competitive advantage.
00:14:01.360 --> 00:14:10.639
And uh competitive advantage that most other companies don't have is that they only provide you the tool, but the tool is only good and useful as what decision is being made, right?
00:14:10.799 --> 00:14:18.720
And those decisions are being made by the executive, which are being uh, you know, with the assistance of our analysts, we're providing that resource.
00:14:18.879 --> 00:14:24.000
Again, remember, banking, uh community banking are limited on resources based on pricing pressures.
00:14:24.159 --> 00:14:27.679
Having something like this is a key competitive advantage that only bigger banks have.
00:14:27.840 --> 00:14:33.679
So, one example I could provide is it is less working directly with a bank, but working with a consultant.
00:14:33.840 --> 00:14:37.360
So we were talking to a consultant that did a lot of merge and acquisition.
00:14:37.440 --> 00:14:44.080
And because of the environment we're in, you know, banking got to look for different ways to uh improve the economy of scales.
00:14:44.240 --> 00:14:47.600
And a lot of the option right now on the table is merging acquisition.
00:14:47.759 --> 00:14:54.000
The problem that this consultant was having is they had a pool of potential partners uh to merge with.
00:14:54.159 --> 00:14:59.759
Um, you know, they could simply you know combine the balance sheet and know what the pre- and post-balance sheet looks like.
00:14:59.919 --> 00:15:04.799
What they had trouble was what does that balance sheet look like after a few scenarios, right?
00:15:05.039 --> 00:15:06.159
Different multiple scenarios.
00:15:06.240 --> 00:15:16.240
So we were able to provide her with a analysis and say, hey, if you were to merge with XYZ, a bank right here, here's what your post-balance sheet income state looked like.
00:15:16.399 --> 00:15:23.919
But by the way, here's what it looked like under this scenario where uh there's a deposit run, or here's how it affects your capital ratio and liquidity ratio.
00:15:24.080 --> 00:15:35.519
And then really it helps her really come down to looking at the partners that provide not only uh just the most profitable, but the profit at the best adjusted uh risk adjusted return level as well.
00:15:35.759 --> 00:15:39.200
And that analysis, what she mentioned, took her months, right?
00:15:39.279 --> 00:15:43.200
We were able to do that in a much faster time frame than what she can do.
00:15:43.519 --> 00:15:49.679
And a lot of that is because we're able to pull in the data, normalize it, our data's clean, we can access the data pretty quickly.
00:15:49.919 --> 00:15:50.480
That's amazing.
00:15:50.559 --> 00:15:52.960
I I didn't see where, you know, that zipped at the end.
00:15:53.279 --> 00:15:57.279
Not only were you examining her options, you said, oh, and here's two other options.
00:15:57.440 --> 00:15:58.559
Very, very interesting.
00:15:58.799 --> 00:16:01.519
So I am an AI fanboy.
00:16:01.759 --> 00:16:07.519
How does, you know, Key, every day I go in and and they talk about this new SaaS service.
00:16:07.600 --> 00:16:09.120
Look, you can build it in 10 hours.
00:16:09.279 --> 00:16:10.080
How how do you go?
00:16:10.240 --> 00:16:11.120
How are you different than that?
00:16:11.279 --> 00:16:16.159
Can I can I build go over on the weekend and me and Sean go in my garage and and build this solution?
00:16:16.399 --> 00:16:19.759
I you know, the the answer to that is yes and no, right?
00:16:19.919 --> 00:16:22.159
Um I said yes.
00:16:22.799 --> 00:16:24.720
Yeah, I think we had this question before, right?
00:16:24.799 --> 00:16:28.559
They say, hey, let's, you know, we could dump this data in cloud and cloud will do it, right?
00:16:28.639 --> 00:16:31.279
Uh but the problem there is cloud is not a database, right?
00:16:31.360 --> 00:16:36.000
And nor, you know, like I said, cloud will only kick out data that they think it's right.
00:16:36.159 --> 00:16:37.519
So you will need the expert.
00:16:37.679 --> 00:16:38.960
And I think this really is it.
00:16:39.039 --> 00:16:42.720
Like our competitive advantage is that we have that data analyst as an expert, right?
00:16:42.960 --> 00:16:44.720
To understand what the output means.
00:16:44.879 --> 00:16:47.360
And does that helping uh uh make sense at all?
00:16:47.600 --> 00:16:53.120
But you know, for someone that don't have the level of expertise, or even if you have the level of expertise to it, right?
00:16:53.200 --> 00:16:54.399
You know, how do you gonna manage data?
00:16:54.480 --> 00:16:55.759
How are you gonna normalize it, right?
00:16:55.840 --> 00:16:59.679
That's where the tool comes in, the software tools and also the data analyst comes in as well.
00:16:59.840 --> 00:17:06.960
So, like I said, you can do it, but would you you'll run the same problem as you're trying to do it manual right now through Excel spreadsheet.
00:17:07.039 --> 00:17:07.680
Yeah, on the set.
00:17:08.000 --> 00:17:13.359
And Joe, so yeah, so I I don't know if I fully explain to Tully the whole tie-in to wrestling payments.
00:17:13.599 --> 00:17:16.319
I'm gonna we do wrestling like WWE.
00:17:16.400 --> 00:17:18.400
I'm gonna I'm gonna blindside you, Toli.
00:17:18.559 --> 00:17:31.200
Like when the guy comes out of the back and the announcers go crazy and say, oh, he's turning, he's turning heel or uh, you know, one of the things that our our industry, our members are dealing with right now is this issue of fraud.
00:17:31.359 --> 00:17:43.039
I mean, there's a lot of I was just reading something, somebody posted from the Connecticut Bankers event that was going on that there's been$20 billion lost and just from some and that's only reported, right, where people are comfortable reporting.
00:17:43.279 --> 00:17:59.599
You know, when when you and I were at that bank in Connecticut chatting, right, one of the scenarios we kind of I joked about was that, you know, when we look at the profitability of a customer, you know, some people have like a tracking system for fraud that's outside of everything else, right?
00:17:59.759 --> 00:18:08.240
Where with the R application, we could take all that fraud tracking into consideration when we actually look at the profitability of a customer.
00:18:08.400 --> 00:18:37.279
Because one of my big complaints, and I know I get in trouble for saying this, is that sometimes exiting the relationship, because over here it looks like it's a very profitable customer, and but some of the other systems are showing you that you've had to take two insurance claims, it's driven up your cost of insurance because having this customer, they keep having computer issues, and right, it's really hard to go to management without a real full data set to say, I think we should offboard this customer.
00:18:37.359 --> 00:18:38.480
And that's typically what happens.
00:18:38.559 --> 00:19:05.359
I think everybody in treasury management knows who the bad customer is, everybody in ops knows who those customers are, but it's hard when you don't have a story using the data to be able to say offboarding this customer actually is a better result for us as an institution than we'll go get them trained, we'll wipe their machine, uh, and you know, because because basically the behaviors aren't changing uh at the end user side.
00:19:05.519 --> 00:19:11.279
Can you just, you know, you you confirm what me on like any data set can be sucked into.
00:19:11.599 --> 00:19:14.480
I mean, you could have a smart sheet with stuff that you're tracking.
00:19:14.559 --> 00:19:18.960
You know, this isn't just about the weakness of cores not giving you all your data.
00:19:19.039 --> 00:19:25.119
This is anywhere in your environment that you're keeping information that should be used to make decisions.
00:19:25.359 --> 00:19:32.720
I think one of the things that surprised me the most about uh learning about the bank industry is the lack of granularity of the data.
00:19:32.960 --> 00:19:36.079
So most banks look at the NIM at the bankwide level, right?
00:19:36.160 --> 00:19:41.920
And looking at the bank wide level, it just really allows you to compare am I doing well or I'm doing poorly against my competitors or my peers.
00:19:42.240 --> 00:19:46.480
To your point, right, our system allows us to go the next step down, or even more, right?
00:19:46.559 --> 00:19:50.400
It allows you to take a look at what are the levers that are moving the profitability.
00:19:50.480 --> 00:19:52.079
In this case, look at clients' ability, right?
00:19:52.160 --> 00:19:55.200
You're looking at your set of clients, and you uncover, hey, you know what?
00:19:55.359 --> 00:20:00.000
These set of clients make up 80% of our profitability, and we should really focus on them.
00:20:00.160 --> 00:20:05.759
Well, these set of clients where they have a lot of frauds, a lot of uh disruption, and they're not really profitable.
00:20:06.000 --> 00:20:13.680
By getting rid of the uh uh by getting rid of this client, we'll actually could improve our overall efficiency and eventually profitability in the firm, right?
00:20:13.759 --> 00:20:21.680
Uh on this and from administrative costs, having to take care of them, you know, having IT go back and forth on uh checking product load, those costs go away.
00:20:21.920 --> 00:20:33.279
And our system allows us to do that uh to really look at not only just client profitability, you could you know, you know, segment down to product level, which product are most competitive uh or most profitable in this case.
00:20:33.440 --> 00:20:41.359
You know, you might be thinking that your CR CRE portfolio is doing well, but in reality, these subsets of CRE proposals are not, right?
00:20:41.519 --> 00:20:45.839
So it might make more business sense to not even offer them at all as a product.
00:20:46.000 --> 00:20:50.319
Um other thing, like I said, uh other segment we could do is branch profitability, right?
00:20:50.400 --> 00:20:57.200
If you're trying to think about uh community outreach and say, hey, I'm getting the return that I want for my community outreach program here.
00:20:57.359 --> 00:21:02.240
If I want to grow this branch by$50 million deposit, is it really worthwhile, right?
00:21:02.400 --> 00:21:06.880
And not only does our data show that, but we can also do simulation and modeling around that.
00:21:06.960 --> 00:21:12.960
Not, hey, if we grow this by$50 million to this uh this branch, what does that do to all our capital ratio, lacurate ratio?
00:21:13.440 --> 00:21:17.680
You know, what happened if there's a run on the uh uh uh change in interest, what does that do?
00:21:17.839 --> 00:21:20.799
So our product allows us to do that type of analysis.
00:21:21.039 --> 00:21:26.880
Um I I have to admit I was I'm I was confused, um, I think, and I I like examples.
00:21:26.960 --> 00:21:29.519
So if you give me another another example, that would be great.
00:21:29.759 --> 00:21:33.759
At the beginning, we kind of focused on, oh, and if we make a decision whether to do a merge or not.
00:21:33.920 --> 00:21:36.960
So I I kind of thought this was a spot solution.
00:21:37.119 --> 00:21:40.880
But when Sean talked about fraud, I can use this ongoing.
00:21:41.039 --> 00:21:47.839
Can you give me an example that I I was thinking, oh, got a big decision, we're gonna do a merger, we're gonna do change this system, or we're gonna find out this.
00:21:47.920 --> 00:21:49.680
But but this is ongoing data.
00:21:50.799 --> 00:21:55.039
Is a live machine that gives you insights into business real time, right?
00:21:55.200 --> 00:21:59.599
So we have part of our AI too is as we're ingesting data in the AI.
00:22:00.160 --> 00:22:02.160
Is looking for anomaly trends, right?
00:22:02.480 --> 00:22:10.559
In this case, and it looks in this case, go back to my data quality issue, it immediately will start uh uh highlighting any bad data quality that you have up again.
00:22:10.720 --> 00:22:17.279
So if you're missing a month, uh let's say you have customer data uh for 12 months and you're missing one month, it'll flag it right away.
00:22:17.359 --> 00:22:21.200
And then it'll look at trends, it'll look at trends and uh and any metrics that you're putting in.
00:22:21.279 --> 00:22:27.839
If you look at profitability or client profitability, you say, hey, your client profitability is trending upward because of this, this is the reason why.
00:22:28.000 --> 00:22:35.839
So it is not just a static analysis, narrow analysis, it's a live machine that's continuously looking at data and giving you insights before you could even look at it.
00:22:36.079 --> 00:22:36.480
Excellent.
00:22:36.640 --> 00:22:40.480
You're walking along and like, oh, okay, yeah, they're gonna pull this software in it to do a solution.
00:22:40.559 --> 00:22:42.799
And like, wait a minute, did he just say ongoing?
00:22:42.960 --> 00:22:43.599
Really interesting.
00:22:44.000 --> 00:22:45.440
Is there anything that we haven't asked?
00:22:45.759 --> 00:22:47.759
Any other um features?
00:22:48.160 --> 00:22:53.119
I think, you know, I think a lot of us are living real time of the AI revolution, right?
00:22:53.200 --> 00:22:54.400
Uh I I think, yeah.
00:22:54.559 --> 00:22:56.160
I think we're all fascinated with it.
00:22:56.240 --> 00:22:59.200
Uh we we benefit from it because we're offering an AI solution.
00:22:59.279 --> 00:23:02.559
Uh, but I think banking in a whole could definitely benefit from it.
00:23:02.720 --> 00:23:06.160
I think we're really just at the tip of where this is right now.
00:23:06.400 --> 00:23:12.559
I think where we are right now is what we're offering, which is this is intelligence, allowing you can start seeing that right now, right?
00:23:12.720 --> 00:23:21.680
Bigger banks or even green banks are looking for products like ours where they're normalizing the data, gathering data, making decisions faster, trying to make decisions faster and smarter, right?
00:23:21.839 --> 00:23:23.759
Um and I think that's where we are now.
00:23:23.839 --> 00:23:32.720
But I think going forward, the second phase of this AI revolution to be uh around augmenting what banks already know about the uh the local economy, right?
00:23:32.799 --> 00:23:35.359
So AI could provide you quantitative data.
00:23:35.519 --> 00:23:38.000
What it can't provide you is the qualitative data, right?
00:23:38.240 --> 00:23:41.920
An executive or a banking relationship manager in a local economy.
00:23:42.000 --> 00:23:43.359
I know what the local economy is, right?
00:23:43.440 --> 00:23:46.240
I know the people, I know the ins and out of the economy, right?
00:23:46.319 --> 00:23:48.240
I have my own biases, I have my own vision of it.
00:23:48.559 --> 00:23:52.000
What AI could do uh anything in this state is ultimate, right?
00:23:52.079 --> 00:23:59.200
It could either verify your opinion or uh kind of uh give you uh flags or insight on something that you might be blind to.
00:24:00.480 --> 00:24:10.720
But really, where I think AI will be in five years from now is you know, we're doing scenario modeling in terms of like, hey, when an executive wants to make a strategic decision, right?
00:24:10.799 --> 00:24:12.799
When they're trying to make a big decision around something.
00:24:12.960 --> 00:24:19.279
I think in five years, it's gonna be like what uh we were talking about continuous optimization, continuous scenario modeling.
00:24:19.440 --> 00:24:28.640
So real time deposit comes in, there's a machine running background that's looking at the data and analyzing and putting out possible scenarios of what the potential outcomes is.
00:24:28.720 --> 00:24:34.480
So it's constant feeding and optimizing the data set uh of the bank and giving information real time.
00:24:34.640 --> 00:24:36.000
I think that's where we're gonna be.
00:24:36.160 --> 00:24:42.000
Right now, we're just at the at the beginning, the tip of it, where hey, we have AI, we have a tool that allows you to scenario model.
00:24:42.319 --> 00:24:50.559
Ultimately, it's gonna be part of the platform where it's an AI assistant as data being fed into the system, it's being optimized real time uh on it.
00:24:50.880 --> 00:24:53.519
That I I love that's you know, that's a vision.
00:24:53.680 --> 00:24:54.559
You're ahead of the game.
00:24:54.720 --> 00:24:55.039
Love it.
00:24:55.200 --> 00:24:56.480
Sean, anything else from you?
00:24:56.720 --> 00:25:00.319
Yeah, no, just just again, from from our perspective at Nietzsche, right?
00:25:00.400 --> 00:25:03.839
Like the healthier this banking segment is, right?
00:25:03.920 --> 00:25:05.440
It it I think it benefits.
00:25:05.599 --> 00:25:06.880
Like I'm a huge fan.
00:25:07.359 --> 00:25:09.039
We wouldn't be at Nietzsche, Joe.
00:25:09.119 --> 00:25:12.319
You've been over here 35 years, I've been here almost 29 years.
00:25:12.559 --> 00:25:16.880
We just love our community banks, our credit unions, the smaller institutions.
00:25:17.119 --> 00:25:25.920
I see it on a personal level with our, you know, we've had some merges where I live, and getting money for softball this year was three times harder than it used to be.
00:25:26.079 --> 00:25:31.039
We would walk into the local branch and say, hey, we need 500 bucks for softball, no problem.
00:25:31.200 --> 00:25:32.559
Bang, they they would do it.
00:25:32.640 --> 00:25:45.839
Now we're calling out of state to some district headquarters, and so the impact on community banks leaving and and not doing well is really, you know, it's a it's a challenge for everybody, personally and professionally for Nietzsche.
00:25:45.920 --> 00:25:56.720
And so one of the things that we're trying to drive through with all of the work we do at Nietzsche, whether it's you know, your fintech innovation series that's out right now, this is all about making FIs better.
00:25:56.799 --> 00:26:02.559
And obviously, you know, we we're we're not saying told there's no other products out there that can help you make decisions.
00:26:02.640 --> 00:26:22.319
But what I was struck by with what what he was talking about is this data system kind of because one of one of the challenges we talked about on a group which has a bank CEO that's a members on our uh vistage group as well is you know, sometimes there's a bench problem um at institutions as people get poached.
00:26:22.400 --> 00:26:25.440
And you know, as you're developing a leader, that's awesome, right?
00:26:25.519 --> 00:26:27.599
You're developing new leaders all the time.
00:26:27.759 --> 00:26:40.480
During development, decisions still need to be made that affect the future of the institution and and having something to support that as you're growing that person into the CFO role, and then they're going to be a key decision maker.
00:26:40.640 --> 00:26:52.480
But, you know, in that eight months it may take them to get ready, there may need to be a decision, a buy, sell, or a new product, or offboarding a customer, and just having something there that can help in that.
00:26:52.640 --> 00:27:01.519
There's decisions that are made every day uh at institutions on pricing, or where do we close a branch, where do we open a branch, should we take on this relationship?
00:27:01.599 --> 00:27:03.200
Is it worth this customer?
00:27:03.359 --> 00:27:06.720
And that doesn't stop when you're building your bench up, right?
00:27:06.799 --> 00:27:08.960
You don't get a timeout, unfortunately.
00:27:09.200 --> 00:27:19.440
So that this is what just struck me about my initial conversations with them is you know, uh they they kind of understand the the perils that are members of Facian.
00:27:19.599 --> 00:27:22.799
Um, but they also are are more positive, right?
00:27:22.880 --> 00:27:29.839
You read a lot of stuff about the bleakness of this size institution, the sweet spot, the 500 million to 5 billion.
00:27:30.160 --> 00:27:33.440
What I was struck by is they're like, no, these guys shouldn't be worried.
00:27:33.519 --> 00:27:39.759
They can absolutely compete and win, and you know, but it just takes thinking differently and having and having the data.
00:27:39.920 --> 00:27:43.920
And, you know, it kind of energized me a little bit because you're like, you know what, you're right.
00:27:44.000 --> 00:27:48.960
There is there is no reason that someone, especially with all the technology that exists, right?
00:27:49.039 --> 00:27:52.480
We the products are like a bill pay product's a bill pay product, right?
00:27:52.640 --> 00:27:55.680
A ACH origination's ACH origination.
00:27:55.839 --> 00:27:57.599
RTP is offering RTP.
00:27:57.839 --> 00:28:13.599
You know, we're where things get different is probably at that decision-making level and having more insight allows the chases of the world to maybe put better positioning on a product or a better target for marketing, right?
00:28:13.759 --> 00:28:15.440
That to me, I think, is the difference.
00:28:15.839 --> 00:28:30.640
It's not because inherently those people are smarter or they you know, it's it's they have the tools, and you know, whenever you can whenever we can help our members level that playing field, I think it's important to get the message out.
00:28:30.720 --> 00:28:38.400
And you know, one of the things you talk about, Joe, all the time as we think about AI, and you know, there's no bigger fan.
00:28:38.720 --> 00:28:40.400
Joe likes it for several reasons.
00:28:40.559 --> 00:28:42.319
One, it's really nice to him all the time.
00:28:43.680 --> 00:28:53.119
He needs that, he loves that, but you're always talking, Joe, that like you there's little things you can do along the way as AI is developing, right?
00:28:53.200 --> 00:29:07.359
Like this stuff we've done at Nietzsche brilliantly, that you know, we're gonna do some stuff with our AP program, which is brilliant, but it's all going towards that next chapter always of what the next like Toli was just talking about what AI could be looking like next.
00:29:07.519 --> 00:29:10.720
If in three years, right, we're at that point he's talking about.
00:29:10.880 --> 00:29:17.599
If you haven't been doing the little steps along the way, that's gonna feel like going in my 19, what did I have?
00:29:17.680 --> 00:29:21.359
A 91 Toyota Corolla trying to go up Mount Washington.
00:29:21.440 --> 00:29:25.839
Like it, I never earned the sticker that says this car climbed Mount Washington.
00:29:26.079 --> 00:29:27.680
Yeah, I was going the other way.
00:29:27.920 --> 00:29:32.319
I had the fastest decline on Mount Washington in the history of motor vehicle.
00:29:32.480 --> 00:29:36.880
So I I think there's like there's an on-ramping that needs to happen.
00:29:37.119 --> 00:29:40.799
And so it's not and this is not to do AI, just to do AI.
00:29:40.880 --> 00:29:45.759
This stuff is extremely valuable, but it also allows you to be better positioned.
00:29:45.839 --> 00:29:50.480
And so I think strategically that's what we've been trying to get across to people.
00:29:50.559 --> 00:30:00.799
And Joe, you did that session at PMC recently, great event where members got to hear, like, you know, this it's not this big overwhelming thing that you can't take advantage of it.
00:30:00.960 --> 00:30:04.000
Some of the AI conversations, I was joking totally.
00:30:04.079 --> 00:30:07.279
I took a course that I went through and it was a great course.
00:30:07.359 --> 00:30:13.519
I got this AI certificate, but like when I was signing up, I specifically asked a lady, like, is there coding in this course?
00:30:13.599 --> 00:30:14.799
And she's like, oh no.
00:30:15.039 --> 00:30:27.359
And I'm swearing in my office week five, they got me doing a project about revitalizing the prison system, and I'm coding AI, and I'm like, no, I just wanted to learn a little bit about AI and how you could use it strategically.
00:30:27.440 --> 00:30:31.920
So we all know how people feel overwhelmed, but there's simple steps along.
00:30:32.720 --> 00:30:33.920
People are not using AI.
00:30:34.000 --> 00:30:37.759
If you're coding AI, you're at the 0.04% of users right now.
00:30:38.000 --> 00:30:38.160
Yeah.
00:30:38.319 --> 00:30:39.039
Yeah, I understand.
00:30:39.359 --> 00:30:40.720
You don't need to be a coder.
00:30:40.799 --> 00:30:44.240
You you just need to understand what's out there and and make it fit.
00:30:44.640 --> 00:31:07.920
Sean, in uh in regard to your CEO at the bank saying we need bench strength and you were gonna grow that along the way, something I've realized lately, and I I realized that just yesterday, I listened to a YouTuber and talking about AI, and literally she said, Well, I was just gonna outperform by doing it more often than not.
00:31:08.079 --> 00:31:20.640
In my career, 20 years ago, 30 years ago, Anthony Robbins was saying, Well, I was just gonna be a better presenter because I was gonna present every day, where everyone else was presenting once a week.
00:31:20.799 --> 00:31:22.079
From an insight perspective.
00:31:22.160 --> 00:31:25.279
So you're building that bench strength if the person's coming up.
00:31:25.599 --> 00:31:42.799
This seems, this solution seems very much like a here's the shortcut to to all that time, because you could make decisions all along your two-year, one-year um orientation, coming to building that bench.
00:31:42.960 --> 00:31:49.680
You can make decisions along the way, realizing at the end, oh, we should have made this decision back here.
00:31:49.920 --> 00:31:50.960
Do you see that?
00:31:51.119 --> 00:31:53.839
I mean, is that a real thing or am I making that up?
00:31:54.079 --> 00:32:00.880
No, I mean, remember when we used to, Joe, we used to bring on people at niche and we'd be like, we had that same mindset.
00:32:01.039 --> 00:32:03.519
Hey, if they just present more, right?
00:32:03.680 --> 00:32:08.799
All that happens when you present more is you get just you get more comfortable presenting.
00:32:08.960 --> 00:32:11.920
It doesn't mean you know the information any better.
00:32:12.079 --> 00:32:16.799
Uh, it just means you get that those fears become, you know, even now we still do it.
00:32:16.880 --> 00:32:19.599
I probably get 10 seconds of nervousness before I speak.
00:32:19.759 --> 00:32:23.440
When I first started, it was like a minute and a half, I'd be freaking out, right?
00:32:23.920 --> 00:32:25.839
And everybody goes through that, right?
00:32:26.000 --> 00:32:37.440
The key with what you're talking about, I think, at these decisioning points is that you can use and leverage the tool so that it's not just a decision at this point.
00:32:37.599 --> 00:32:41.759
You put the factors in and say, if I do this now, what is it three years from now?
00:32:41.920 --> 00:32:43.039
How does this affect us?
00:32:43.279 --> 00:32:56.319
Because that, you know, we see that even at a small company like Nietzsche, every little decision I make here, or you make eight months or 12 months from now, we have to go back and you know, and say, oh wow, because we did this, we're now at this point.
00:32:56.480 --> 00:33:00.160
And sometimes that's good, and sometimes we get it wrong, and that and that happens.
00:33:00.319 --> 00:33:14.240
Um, but just being able to, it's not just doing it more to get better, it's actually every time you're doing it, it's like Dwight Evans, former Red Sox player, used to say, practicing perfect makes perfect, not just practicing.
00:33:14.319 --> 00:33:20.720
And I think that's what this allows you to do is you're not just making decisions to get in the habit of making decisions.
00:33:20.799 --> 00:33:25.279
You're actually getting in the habit of making good decisions based upon data.
00:33:25.440 --> 00:33:33.039
And then the more and more that you do that, your decisions are going to become more and more defendable and bulletproof and all and all of that things.
00:33:33.119 --> 00:33:39.839
And so I think just doing things that used to be that old, you know, we've had trainers here that haven't worked out.
00:33:39.920 --> 00:33:43.359
We had people go stone cold white on stage.
00:33:43.519 --> 00:33:48.960
We we've we've had you know, we've had it all in our time, and it, you know, that we were just wrong about that.
00:33:49.119 --> 00:33:51.519
Like, oh no, just do more webinars, you're gonna get better.
00:33:51.599 --> 00:33:57.279
And it was really what makes you better, like you and I as presenters, is we know the stuff cold in and out.
00:33:57.440 --> 00:34:12.960
There's not a question someone in the audience is gonna say that's gonna throw you off on a webinar, or and so I think there's that different thing in in this podcast, uh this YouTuber you're talking about, like just using AI and I'm gonna just gonna use it more than everybody.
00:34:13.119 --> 00:34:15.119
I think they're gonna run into the same challenge.
00:34:15.280 --> 00:34:23.440
If you're not using it for a purpose, or if you're not it's just gonna be something you do and you're gonna tell people you're doing it, but you're not really you're not really effective.
00:34:23.599 --> 00:34:26.400
And you know, totally you're you're more of a tech person.
00:34:26.480 --> 00:34:31.840
You like to beat stuff up and you know, what's your thoughts on this whole just use AI to use it and I'm gonna get better at it.
00:34:32.000 --> 00:34:33.360
I think you you hit it on the point, right?
00:34:33.440 --> 00:34:36.159
I think that's kind of uh pretty in the tagline, right?
00:34:36.400 --> 00:34:44.320
Not only do we allow you to increase your decision velocity, but we allow you to make fast decisions at a smarter rate, right?
00:34:44.480 --> 00:34:49.199
And that the smarter, the ability to make smarter decisions comes back from our scenario modeling, right?
00:34:49.360 --> 00:34:58.239
It allows you to say, you know, test out certain scenarios without having to risk any type of capital, any type of uh investment into it, right?
00:34:58.400 --> 00:35:02.960
And that allows you to appear up, be more confident when you walk into an outgoing meeting, right?
00:35:03.119 --> 00:35:09.039
Knowing that the decision you make knows exactly how that's gonna impact your capital ratios, your local ratios.
00:35:09.199 --> 00:35:10.960
And that's where I think Predian does.
00:35:11.039 --> 00:35:12.079
And I agree with you, right?
00:35:12.239 --> 00:35:15.039
Just making fast decisions is not gonna help you, right?
00:35:15.199 --> 00:35:17.599
Making fast, smart decisions is the key thing.
00:35:17.760 --> 00:35:23.440
And with Predian, because of the scenario modeling tool that we have, it gives people a lot of confidence that saying, hey, you know what?
00:35:23.599 --> 00:35:27.199
I feel really good about decision because I model this thing out a hundred different ways, right?
00:35:27.280 --> 00:35:28.400
Based on my expertise.
00:35:28.480 --> 00:35:32.079
And so I feel confident that the decision I'm gonna make, I know what the potential outcome will be.
00:35:32.320 --> 00:35:32.559
Excellent.
00:35:32.719 --> 00:35:35.199
Well, give you the last word, anything in partying?
00:35:35.440 --> 00:35:40.960
Yeah, I think we are a tool that, you know, I think we kind of uh talk about it.
00:35:41.119 --> 00:35:42.880
It's a tool that really levels the playing field.
00:35:42.960 --> 00:35:49.840
I think, you know, community banks are really struggling and under pressure from low profitability, uh losing key talent.
00:35:50.000 --> 00:35:52.079
You know, trying to level that playing field.
00:35:52.159 --> 00:35:55.039
It put us in the same ballpark as the big boys, right?
00:35:55.119 --> 00:36:04.400
On this end, meaning that we not only provide you the most advanced technology, but the analytical infrastructure to allow you to assess the data that's coming in.
00:36:04.639 --> 00:36:17.599
And you know, it immediately, I think, solve a huge gap in in the banking world right now, which is both like you know, how do you analyze clean data, get clean data, and how do you analyze it on this end without spending a significant amount of capital.
00:36:17.920 --> 00:36:18.159
Excellent.
00:36:18.239 --> 00:36:19.119
Well, thank you for joining.
00:36:19.280 --> 00:36:21.519
Thanks to the WrestleMania.
00:36:22.159 --> 00:36:22.880
It's been a lot of fun.
00:36:23.039 --> 00:36:23.519
Yeah, yeah.
00:36:23.760 --> 00:36:24.159
Excellent.
00:36:24.239 --> 00:36:24.719
Thank you very much.
00:36:24.960 --> 00:36:25.360
All right, thank you.
00:36:26.000 --> 00:36:26.559
Thanks, everyone.
00:36:28.719 --> 00:36:35.920
On behalf of NEET, your trusted resource in payments and our guests, I'd like to thank everyone for tuning in to the podcast.
00:36:36.079 --> 00:36:49.199
If you've enjoyed this episode and want to hear more about payments innovation and modernization, you can find the Wrestling Payments Podcast on the podcast page of Neach.org or wherever you subscribe the podcast.
00:36:49.360 --> 00:36:52.719
Just search for Wrestling Payments in your podcast app.
00:36:52.960 --> 00:36:54.159
Thanks for listening.
00:00:03.600 --> 00:00:05.360
Welcome everybody.
00:00:05.599 --> 00:00:17.440
You're with today's Workflow Payment, a podcast, where we help payment professionals content with the challenges of modernizing their payment operations and identifying ways to build for the future.
00:00:17.600 --> 00:00:23.039
If you're interested in payment innovation and modernization, you're in the right place.
00:00:23.920 --> 00:00:27.039
Now, let's get into it.
00:00:33.039 --> 00:00:35.439
Hey everyone, welcome to Wrestling Payments.
00:00:35.520 --> 00:00:36.640
Today we got a great episode.
00:00:36.799 --> 00:00:41.280
We're going to talk about a new product that solves a really, really important problem.
00:00:41.520 --> 00:00:46.479
The reason I sound a little uh odd, Sean Carter, who's joined me today, and Toli Chia.
00:00:46.799 --> 00:00:52.880
Sean Carter, president of Nietzsche and NPG, he's talked forever about the importance of data.
00:00:52.960 --> 00:00:55.520
You have to have data, analytics, you have to use the data.
00:00:55.679 --> 00:01:00.079
Today we have someone that that proves him right, which is very uncomfortable for the rest of us.
00:01:01.039 --> 00:01:03.039
Sean, do you want to just introduce yourself, say hi?
00:01:03.359 --> 00:01:03.759
Yeah, yeah.
00:01:03.920 --> 00:01:07.200
No, so uh thanks, Joe, for having me on Wrestle and Payments.
00:01:07.359 --> 00:01:08.879
Um, very excited today.
00:01:08.959 --> 00:01:18.400
Uh, president CEO of Nietzsche and and Nietzsche Payments Group, and one of the things that Nietzsche needs to be healthy is that our financial institutions are healthy.
00:01:18.480 --> 00:01:24.560
And the way I've been saying for years that financial institutions can remain healthy is to make strong decisions.
00:01:24.799 --> 00:01:31.920
The best way to do that is to be able to have the data, which we know from talking with a lot of you, it's it's kind of all over the place, right?
00:01:32.000 --> 00:01:33.680
Which is good because you have a lot of it.
00:01:33.840 --> 00:01:38.799
The bad is that it's it's not always as easy to get as you would think, even though it's your data, right?
00:01:38.959 --> 00:01:40.959
And this is just an FI issue.
00:01:41.120 --> 00:01:48.640
I talk about this all the time at the Nacha level where Nacho will say we can't get certain information from the operators, and I'm like, it's your network.
00:01:48.719 --> 00:01:49.519
What are we talking about?
00:01:49.599 --> 00:01:51.680
You can't get information from the operators.
00:01:51.760 --> 00:01:59.519
So I'm very consistent on this issue of it's your data, you should be able to have it and get it, and then allows you to make informed decisions.
00:01:59.599 --> 00:02:01.599
So excited to have this conversation.
00:02:01.760 --> 00:02:05.680
Just quickly, how I how I met Tolley is uh is interesting as well.
00:02:05.840 --> 00:02:07.200
Um, a lot of you don't know.
00:02:07.280 --> 00:02:22.000
I was kind of a train wreck when I first took over as CEO of Nietzsche, and I was uh lucky enough to be pushed to join Vistage, which is a CEO group, and Toli happens to be on my group, uh, and we've had a chance to get to know each other a little.
00:02:22.080 --> 00:02:27.919
And if you guys are uh interested or impressed by intelligence, this this is what we're talking about.
00:02:28.080 --> 00:02:30.319
Is Tolly's mind just works differently.
00:02:30.400 --> 00:02:33.439
So I hope that comes across in in this podcast.
00:02:33.520 --> 00:02:40.000
We'll try to team up with questions so that he can just shine because uh really bright individual.
00:02:40.319 --> 00:02:41.840
And Toli, please.
00:02:42.080 --> 00:02:44.639
Thank you, thank you, thank you, uh Sean, for those kind words.
00:02:44.719 --> 00:02:51.759
And you know, on way of background, yeah, I started off my career a couple years as an assistant bank auditor while working with FDIC.
00:02:52.000 --> 00:02:55.120
Did a lot of the regional banks here uh in terms of bank examination.
00:02:55.280 --> 00:03:00.000
You know, I decided that, you know, being a bank auditor wasn't m my calling in life.
00:03:00.080 --> 00:03:07.840
So I spent uh about 20 years in the investment and wealth management industry, focusing mostly on the analytics side of the business, right?
00:03:08.000 --> 00:03:10.479
Focusing on uh the operation trading aspect.
00:03:10.639 --> 00:03:16.560
I built out the infrastructure, the core infrastructure for the investment banking firm that I worked for.
00:03:16.800 --> 00:03:21.199
After leaving the investment industry, I decided, you know, why don't just start something different, right?
00:03:21.280 --> 00:03:24.479
Why just do something totally off uh out of the field?
00:03:24.639 --> 00:03:30.240
So I I bought into a water company, a water purification company, and knew nothing about the industry.
00:03:30.400 --> 00:03:34.000
But it really gave me a lot of insight onto how to run a company, right?
00:03:34.159 --> 00:03:41.919
From uh sales and marketing to running a book of business to managing, you know, 20 or 30 people that are looking at you for key decisions.
00:03:42.159 --> 00:03:45.039
And that led me down to creating several other companies.
00:03:45.199 --> 00:03:55.599
And one of them is the company we're talking about right now, which is uh Predion.ai, which is a decision intelligence platform that really solves some of the data issue that Sean is talking about, right?
00:03:55.840 --> 00:04:04.400
It allows executive, it's an active engine that allows executives to really test out decision before they even risk one dollar capital uh into it at all.
00:04:04.800 --> 00:04:08.560
Sorry, I have that moment where I I become an audience member and I'm like, huh?
00:04:09.520 --> 00:04:16.800
You know, the genesis of our product really stems from uh another company that uh my partner and I started, Anjid, who's not on the call.
00:04:16.959 --> 00:04:24.879
But we had a uh we have a uh consulting company that focused on helping, you know, medium and uh small size businesses grow.
00:04:25.040 --> 00:04:33.920
And one of the challenges that we had was accessing the data, getting the data and quantifying in a manner that makes uh allows us to make uh insightful business decisions.
00:04:34.079 --> 00:04:41.519
And the problem was that is the crux of the problem, whether it's small business or banks that we work with, you know, two problems that persist, right?
00:04:41.680 --> 00:04:43.839
One is data quality is an issue.
00:04:44.000 --> 00:04:51.040
And then the second is there are a bunch of solid data living in disjointed systems that don't talk to each other, right?
00:04:51.199 --> 00:04:55.920
Now one of the things that people don't really underestimate is the root cause of data quality.
00:04:56.079 --> 00:05:05.839
You know, if you think about this, you have the perfect technology uh in place, but if you have messy data going in, it's garbage in and garbage out, right?
00:05:06.000 --> 00:05:06.560
On the send.
00:05:06.800 --> 00:05:16.879
One of our um advisory board members, uh, who you who is a former uh CFO of a regional bank, he actually did an audit of an institution uh around here.
00:05:17.040 --> 00:05:25.120
And what he he didn't audit their core uh banking system, what he uncovered during that time was that that bank had four deposit products in there.
00:05:25.519 --> 00:05:27.439
I'm sorry, 400 deposit products on there.
00:05:27.600 --> 00:05:41.519
So what happened there was basically over the years of the banking operation, someone was just manually putting in these uh uh deposit products in there, whether it's a promotional product that they did over summer, but they keep on compounding over the years and adding up, right?
00:05:41.600 --> 00:05:46.160
And if you think about it, if banks are doing merging acquisition, they're just bringing more products in there.
00:05:46.319 --> 00:05:54.000
And so now the data is just ballooning into this messy pile of data where it's impossible to do any analytics around the deposit portfolio.
00:05:54.160 --> 00:06:01.600
The other challenge that I see outside of just the data quality, I imagine, is the fragment data living across uh several different systems.
00:06:01.759 --> 00:06:03.199
So think about this, right?
00:06:03.279 --> 00:06:09.120
Uh simple question, uh, an executive will walk into a board meeting and say, hey, who are most profitable clients, right?
00:06:09.279 --> 00:06:09.759
On this end.
00:06:10.079 --> 00:06:17.519
A very simple answer, but it's a very complex process for banks right now because all the data live in separate systems, it's messy.
00:06:17.680 --> 00:06:34.079
So if you have data in your uh core system, you have your data in your loan origination, you have your data in your CRM, the simple answer of who's my uh most profitable client becomes a week-long process versus something that they could uh you know um push out in a few minutes, right?
00:06:34.399 --> 00:06:40.160
Um so by the time they get the answer and the board needs to make the decision on it, the opportunity might have passed already, right?
00:06:40.240 --> 00:06:48.240
Or even worse, a competitor that's actually have a better process around data normalization, data duplication has already got in and took that opportunity.
00:06:48.480 --> 00:06:54.879
And Joe, this is where where I got interested with Tolley and Sanjeev when they were kind of explaining what they were doing.
00:06:55.040 --> 00:06:57.040
You think about this in your payments business, right?
00:06:57.120 --> 00:06:57.199
Right?
00:06:57.360 --> 00:07:05.439
Nietzsche's payments and and we love payments and trying to, you know, there's a couple of key inflection points in your payment strategy, right?
00:07:05.519 --> 00:07:07.439
It's number one is what do we offer, right?
00:07:07.600 --> 00:07:12.879
But then, you know, beyond that is kind of what we're should be focusing our time.
00:07:13.040 --> 00:07:15.519
What is the most profitable products we have?
00:07:15.680 --> 00:07:34.160
And the example that always comes to mind to me, and you know, I love the the whole ISO world in card processing, is a financial institution looking at businesses such as credit card or or processing fees like that, that is coming from a totally different stream than maybe everything else they're offering.
00:07:34.319 --> 00:07:39.199
And so when when somebody does ask a very straightforward question like, how are we doing?
00:07:39.360 --> 00:07:41.360
There's obviously an accounting answer to that.
00:07:41.519 --> 00:07:45.439
Anybody can look at a P ⁇ L and say, Oh yeah, or we're losing money, right?
00:07:45.600 --> 00:07:46.560
Or we're making money.
00:07:46.639 --> 00:07:51.120
But to Tolley's point, there's a deep there's deeper questions under that, right?
00:07:51.199 --> 00:08:00.879
And so even I think what happens on board discussions is sometimes they get stalled because everybody knows not to go any more deeper, because you probably can't get the answer, right?
00:08:00.959 --> 00:08:04.800
And so the question becomes very basic, like, are we making money or not?
00:08:04.959 --> 00:08:08.720
When really what you're probably trying to get at is what's our most profitable segment?
00:08:08.879 --> 00:08:10.959
Like, where should we be focused next?
00:08:11.199 --> 00:08:13.360
You know, where we're where are our threats, right?
00:08:13.439 --> 00:08:27.439
Like what industry, like if 80% of our revenue is from one industry and you see that big bank ABC that has specialization in that industry is coming to your area, that's a bigger threat than if it was a different bank coming into your network.
00:08:27.519 --> 00:08:45.840
And I what Tol showed me, and we've we've actually gone out to some member institutions and talked about this, and he's done some demos, and and what it allows you to do is really get at that inflection point to say, you know, we need to know from this system and this system in order to change pricing.
00:08:46.080 --> 00:08:58.240
But the the beauty, Joe, this is what you would love, because anyone that ever, if you ever watch Nietzsche operate, I am like so quick to make decisions, and Joe's like, whoa, whoa, whoa, we just have to take a little more time to decide.
00:08:58.480 --> 00:09:01.279
Joe's that secondary, well, what if this happens?
00:09:01.600 --> 00:09:03.919
Which totally brain works the same way.
00:09:04.080 --> 00:09:05.679
What if we did change pricing?
00:09:05.840 --> 00:09:12.960
Before making that decision, this tool allows you to say, what does that make my income look like over the next couple years?
00:09:13.120 --> 00:09:14.879
What if the interest rates change?
00:09:15.039 --> 00:09:18.559
How does that impact this decision I'm about to make over here?
00:09:18.720 --> 00:09:26.080
And if you don't have all your data, let's say you have 67% of your data, you're getting 67% of the decision, right?
00:09:26.240 --> 00:09:28.799
It's it's it's it's an amazing.
00:09:29.039 --> 00:09:38.000
I didn't understand what they were doing at first, but when he put it into banking terms, because he has the bank around background, I'm like, this is this is perfect.
00:09:38.159 --> 00:09:40.000
I know other systems exist.
00:09:40.240 --> 00:09:45.360
However, some of those are provided by the same provider that doesn't allow you to have all your data.
00:09:45.600 --> 00:09:48.960
So somebody listening to this may say, oh yeah, we already have that tool.
00:09:49.200 --> 00:10:00.879
Pause before you say that 100%, because if if it's the person that's limiting your access now to what you can see, if it's their tool, it's also not seeing those other things that may live outside.
00:10:00.960 --> 00:10:11.840
And when we talk about payments in fintech and how much revenue you want to drive, it's really important to see all of that because you know, you know, Joe, you and I get to see this all the time.
00:10:12.000 --> 00:10:17.759
Some people are skittish about fintech relationships, even though they can be very lucrative, right?
00:10:18.080 --> 00:10:23.360
Because, you know, the regulators get, you know, they want to see a bigger program, that there's all kinds of risks.
00:10:23.440 --> 00:10:39.200
So if if you're not giving everybody the full information on the board about what these relationships are really looking like, and all they're saying is the increased headaches that the relationships are bringing, or employees are saying, hey, this is too much to deal with, you know, we've seen this in the third-party center space.
00:10:39.360 --> 00:10:46.080
People exit the business and they're giving up a lot of opportunity sometimes because they're probably not seeing the full picture.
00:10:46.240 --> 00:10:54.080
Um, and you know, this this process that Tully talks about with the data just allows you to do that at a board at executive level.
00:10:54.159 --> 00:11:01.679
And I even talk to them about this could be within a department for your treasury people to kind of look at pricing and things that they want to do.
00:11:01.840 --> 00:11:08.080
So it's a it's it's a fascinating journey, but it all starts from that simple premise of it's your data.
00:11:08.559 --> 00:11:10.080
You should be able to see it all.
00:11:10.159 --> 00:11:11.600
And it doesn't always work like that.
00:11:11.840 --> 00:11:13.519
I actually think you brought up a good point.
00:11:13.600 --> 00:11:19.120
Um, you know, a lot of this could be solved if you have the capital resources to just throw money at it, right?
00:11:19.200 --> 00:11:20.720
I think Bink Bank has that, right?
00:11:20.799 --> 00:11:23.440
They are at a hugely competitive advantage.
00:11:23.759 --> 00:11:30.399
Not only do they have the money to throw an implementation like a snowflake or build out a data department, right?
00:11:30.480 --> 00:11:32.000
Or even hire the best talent.
00:11:32.159 --> 00:11:34.480
You know, community banks don't have that, right?
00:11:34.559 --> 00:11:42.480
Uh they're very uh constrained by you know a b uh the budget, and any talent that they might have, they might have difficulty retaining them, right?
00:11:42.639 --> 00:11:47.200
Because you know, a fancy fintech companies come in, a bigger bank could double their salaries, and you're gone.
00:11:47.360 --> 00:11:51.120
Where I think Predion comes in, it levels the playing field for them, right?
00:11:51.279 --> 00:11:58.480
In this case, is that it allows community banks the same type of solution where they'll have access to uh clean data.
00:11:58.639 --> 00:12:14.799
And also one thing that I think that gives us uh a really competitive advantage is that not only do we provide the best, what I think is one of the premier technology, is that we also provide a financial analytical expertise that will come in and help you use the technology.
00:12:14.960 --> 00:12:15.759
So think about this.
00:12:15.840 --> 00:12:23.279
It's like not only you're buying a fancy sport car, you're getting an excellent driver to drive uh drive that fancy sport car.
00:12:23.360 --> 00:12:26.320
It's not we're asking you to drive the spancy sport car.
00:12:26.480 --> 00:12:28.159
We're we're doing it to drive it for you.
00:12:28.480 --> 00:12:32.320
I have an objection, an observation, and a question.
00:12:32.639 --> 00:12:34.799
So my objection, it's not really an objection.
00:12:34.960 --> 00:12:36.559
The ACH is 50 years old.
00:12:36.720 --> 00:12:41.679
Lots of times they've built systems on top of systems, on top of systems, on top of systems.
00:12:41.759 --> 00:12:45.679
So finding walking into a a bank and saying, Oh, can you pull this report?
00:12:45.840 --> 00:12:48.159
Uh we know we can't do that.
00:12:48.320 --> 00:12:54.159
So I just want to defend those my my my community bankers saying, you know, sometimes it's it's hard.
00:12:54.320 --> 00:12:59.200
The the observation that that core, you know, the root cause of the problem.
00:12:59.440 --> 00:13:06.720
How if I'm listening to this podcast right now, how do I know that this solution solves my problem?
00:13:07.039 --> 00:13:10.480
Maybe because I haven't actually scoped up my problem enough.
00:13:10.639 --> 00:13:15.759
What what problem how do I know that I can put the connection together that I should be looking at Pretion?
00:13:16.240 --> 00:13:18.960
Um this this would help me solve that problem.
00:13:19.200 --> 00:13:21.919
Do you have examples of how you came in and solved a problem?
00:13:22.159 --> 00:13:28.639
Yeah, well, I'll give you an example, but I think let's take a step back what uh what makes Predion uh different from other platforms.
00:13:28.720 --> 00:13:32.799
But I I think Pretion itself, that software itself is top-notch.
00:13:32.879 --> 00:13:37.919
It's you know, but I don't think the technology is the one that makes us that sets us apart.
00:13:38.159 --> 00:13:44.159
Where I really think that sets us apart is the financial analytical analyst that comes in along with it.
00:13:44.320 --> 00:13:46.720
You know, our analysts understand banking, right?
00:13:46.799 --> 00:13:56.240
They understand what's the difference between loan attributes and deposit attributes and how to look at raw banking data and say, you know what, that data is good, that data's bad.
00:13:56.480 --> 00:13:58.960
They could understand it uh and they speak the language of that.
00:13:59.120 --> 00:14:01.200
And I think that's a huge competitive advantage.
00:14:01.360 --> 00:14:10.639
And uh competitive advantage that most other companies don't have is that they only provide you the tool, but the tool is only good and useful as what decision is being made, right?
00:14:10.799 --> 00:14:18.720
And those decisions are being made by the executive, which are being uh, you know, with the assistance of our analysts, we're providing that resource.
00:14:18.879 --> 00:14:24.000
Again, remember, banking, uh community banking are limited on resources based on pricing pressures.
00:14:24.159 --> 00:14:27.679
Having something like this is a key competitive advantage that only bigger banks have.
00:14:27.840 --> 00:14:33.679
So, one example I could provide is it is less working directly with a bank, but working with a consultant.
00:14:33.840 --> 00:14:37.360
So we were talking to a consultant that did a lot of merge and acquisition.
00:14:37.440 --> 00:14:44.080
And because of the environment we're in, you know, banking got to look for different ways to uh improve the economy of scales.
00:14:44.240 --> 00:14:47.600
And a lot of the option right now on the table is merging acquisition.
00:14:47.759 --> 00:14:54.000
The problem that this consultant was having is they had a pool of potential partners uh to merge with.
00:14:54.159 --> 00:14:59.759
Um, you know, they could simply you know combine the balance sheet and know what the pre- and post-balance sheet looks like.
00:14:59.919 --> 00:15:04.799
What they had trouble was what does that balance sheet look like after a few scenarios, right?
00:15:05.039 --> 00:15:06.159
Different multiple scenarios.
00:15:06.240 --> 00:15:16.240
So we were able to provide her with a analysis and say, hey, if you were to merge with XYZ, a bank right here, here's what your post-balance sheet income state looked like.
00:15:16.399 --> 00:15:23.919
But by the way, here's what it looked like under this scenario where uh there's a deposit run, or here's how it affects your capital ratio and liquidity ratio.
00:15:24.080 --> 00:15:35.519
And then really it helps her really come down to looking at the partners that provide not only uh just the most profitable, but the profit at the best adjusted uh risk adjusted return level as well.
00:15:35.759 --> 00:15:39.200
And that analysis, what she mentioned, took her months, right?
00:15:39.279 --> 00:15:43.200
We were able to do that in a much faster time frame than what she can do.
00:15:43.519 --> 00:15:49.679
And a lot of that is because we're able to pull in the data, normalize it, our data's clean, we can access the data pretty quickly.
00:15:49.919 --> 00:15:50.480
That's amazing.
00:15:50.559 --> 00:15:52.960
I I didn't see where, you know, that zipped at the end.
00:15:53.279 --> 00:15:57.279
Not only were you examining her options, you said, oh, and here's two other options.
00:15:57.440 --> 00:15:58.559
Very, very interesting.
00:15:58.799 --> 00:16:01.519
So I am an AI fanboy.
00:16:01.759 --> 00:16:07.519
How does, you know, Key, every day I go in and and they talk about this new SaaS service.
00:16:07.600 --> 00:16:09.120
Look, you can build it in 10 hours.
00:16:09.279 --> 00:16:10.080
How how do you go?
00:16:10.240 --> 00:16:11.120
How are you different than that?
00:16:11.279 --> 00:16:16.159
Can I can I build go over on the weekend and me and Sean go in my garage and and build this solution?
00:16:16.399 --> 00:16:19.759
I you know, the the answer to that is yes and no, right?
00:16:19.919 --> 00:16:22.159
Um I said yes.
00:16:22.799 --> 00:16:24.720
Yeah, I think we had this question before, right?
00:16:24.799 --> 00:16:28.559
They say, hey, let's, you know, we could dump this data in cloud and cloud will do it, right?
00:16:28.639 --> 00:16:31.279
Uh but the problem there is cloud is not a database, right?
00:16:31.360 --> 00:16:36.000
And nor, you know, like I said, cloud will only kick out data that they think it's right.
00:16:36.159 --> 00:16:37.519
So you will need the expert.
00:16:37.679 --> 00:16:38.960
And I think this really is it.
00:16:39.039 --> 00:16:42.720
Like our competitive advantage is that we have that data analyst as an expert, right?
00:16:42.960 --> 00:16:44.720
To understand what the output means.
00:16:44.879 --> 00:16:47.360
And does that helping uh uh make sense at all?
00:16:47.600 --> 00:16:53.120
But you know, for someone that don't have the level of expertise, or even if you have the level of expertise to it, right?
00:16:53.200 --> 00:16:54.399
You know, how do you gonna manage data?
00:16:54.480 --> 00:16:55.759
How are you gonna normalize it, right?
00:16:55.840 --> 00:16:59.679
That's where the tool comes in, the software tools and also the data analyst comes in as well.
00:16:59.840 --> 00:17:06.960
So, like I said, you can do it, but would you you'll run the same problem as you're trying to do it manual right now through Excel spreadsheet.
00:17:07.039 --> 00:17:07.680
Yeah, on the set.
00:17:08.000 --> 00:17:13.359
And Joe, so yeah, so I I don't know if I fully explain to Tully the whole tie-in to wrestling payments.
00:17:13.599 --> 00:17:16.319
I'm gonna we do wrestling like WWE.
00:17:16.400 --> 00:17:18.400
I'm gonna I'm gonna blindside you, Toli.
00:17:18.559 --> 00:17:31.200
Like when the guy comes out of the back and the announcers go crazy and say, oh, he's turning, he's turning heel or uh, you know, one of the things that our our industry, our members are dealing with right now is this issue of fraud.
00:17:31.359 --> 00:17:43.039
I mean, there's a lot of I was just reading something, somebody posted from the Connecticut Bankers event that was going on that there's been$20 billion lost and just from some and that's only reported, right, where people are comfortable reporting.
00:17:43.279 --> 00:17:59.599
You know, when when you and I were at that bank in Connecticut chatting, right, one of the scenarios we kind of I joked about was that, you know, when we look at the profitability of a customer, you know, some people have like a tracking system for fraud that's outside of everything else, right?
00:17:59.759 --> 00:18:08.240
Where with the R application, we could take all that fraud tracking into consideration when we actually look at the profitability of a customer.
00:18:08.400 --> 00:18:37.279
Because one of my big complaints, and I know I get in trouble for saying this, is that sometimes exiting the relationship, because over here it looks like it's a very profitable customer, and but some of the other systems are showing you that you've had to take two insurance claims, it's driven up your cost of insurance because having this customer, they keep having computer issues, and right, it's really hard to go to management without a real full data set to say, I think we should offboard this customer.
00:18:37.359 --> 00:18:38.480
And that's typically what happens.
00:18:38.559 --> 00:19:05.359
I think everybody in treasury management knows who the bad customer is, everybody in ops knows who those customers are, but it's hard when you don't have a story using the data to be able to say offboarding this customer actually is a better result for us as an institution than we'll go get them trained, we'll wipe their machine, uh, and you know, because because basically the behaviors aren't changing uh at the end user side.
00:19:05.519 --> 00:19:11.279
Can you just, you know, you you confirm what me on like any data set can be sucked into.
00:19:11.599 --> 00:19:14.480
I mean, you could have a smart sheet with stuff that you're tracking.
00:19:14.559 --> 00:19:18.960
You know, this isn't just about the weakness of cores not giving you all your data.
00:19:19.039 --> 00:19:25.119
This is anywhere in your environment that you're keeping information that should be used to make decisions.
00:19:25.359 --> 00:19:32.720
I think one of the things that surprised me the most about uh learning about the bank industry is the lack of granularity of the data.
00:19:32.960 --> 00:19:36.079
So most banks look at the NIM at the bankwide level, right?
00:19:36.160 --> 00:19:41.920
And looking at the bank wide level, it just really allows you to compare am I doing well or I'm doing poorly against my competitors or my peers.
00:19:42.240 --> 00:19:46.480
To your point, right, our system allows us to go the next step down, or even more, right?
00:19:46.559 --> 00:19:50.400
It allows you to take a look at what are the levers that are moving the profitability.
00:19:50.480 --> 00:19:52.079
In this case, look at clients' ability, right?
00:19:52.160 --> 00:19:55.200
You're looking at your set of clients, and you uncover, hey, you know what?
00:19:55.359 --> 00:20:00.000
These set of clients make up 80% of our profitability, and we should really focus on them.
00:20:00.160 --> 00:20:05.759
Well, these set of clients where they have a lot of frauds, a lot of uh disruption, and they're not really profitable.
00:20:06.000 --> 00:20:13.680
By getting rid of the uh uh by getting rid of this client, we'll actually could improve our overall efficiency and eventually profitability in the firm, right?
00:20:13.759 --> 00:20:21.680
Uh on this and from administrative costs, having to take care of them, you know, having IT go back and forth on uh checking product load, those costs go away.
00:20:21.920 --> 00:20:33.279
And our system allows us to do that uh to really look at not only just client profitability, you could you know, you know, segment down to product level, which product are most competitive uh or most profitable in this case.
00:20:33.440 --> 00:20:41.359
You know, you might be thinking that your CR CRE portfolio is doing well, but in reality, these subsets of CRE proposals are not, right?
00:20:41.519 --> 00:20:45.839
So it might make more business sense to not even offer them at all as a product.
00:20:46.000 --> 00:20:50.319
Um other thing, like I said, uh other segment we could do is branch profitability, right?
00:20:50.400 --> 00:20:57.200
If you're trying to think about uh community outreach and say, hey, I'm getting the return that I want for my community outreach program here.
00:20:57.359 --> 00:21:02.240
If I want to grow this branch by$50 million deposit, is it really worthwhile, right?
00:21:02.400 --> 00:21:06.880
And not only does our data show that, but we can also do simulation and modeling around that.
00:21:06.960 --> 00:21:12.960
Not, hey, if we grow this by$50 million to this uh this branch, what does that do to all our capital ratio, lacurate ratio?
00:21:13.440 --> 00:21:17.680
You know, what happened if there's a run on the uh uh uh change in interest, what does that do?
00:21:17.839 --> 00:21:20.799
So our product allows us to do that type of analysis.
00:21:21.039 --> 00:21:26.880
Um I I have to admit I was I'm I was confused, um, I think, and I I like examples.
00:21:26.960 --> 00:21:29.519
So if you give me another another example, that would be great.
00:21:29.759 --> 00:21:33.759
At the beginning, we kind of focused on, oh, and if we make a decision whether to do a merge or not.
00:21:33.920 --> 00:21:36.960
So I I kind of thought this was a spot solution.
00:21:37.119 --> 00:21:40.880
But when Sean talked about fraud, I can use this ongoing.
00:21:41.039 --> 00:21:47.839
Can you give me an example that I I was thinking, oh, got a big decision, we're gonna do a merger, we're gonna do change this system, or we're gonna find out this.
00:21:47.920 --> 00:21:49.680
But but this is ongoing data.
00:21:50.799 --> 00:21:55.039
Is a live machine that gives you insights into business real time, right?
00:21:55.200 --> 00:21:59.599
So we have part of our AI too is as we're ingesting data in the AI.
00:22:00.160 --> 00:22:02.160
Is looking for anomaly trends, right?
00:22:02.480 --> 00:22:10.559
In this case, and it looks in this case, go back to my data quality issue, it immediately will start uh uh highlighting any bad data quality that you have up again.
00:22:10.720 --> 00:22:17.279
So if you're missing a month, uh let's say you have customer data uh for 12 months and you're missing one month, it'll flag it right away.
00:22:17.359 --> 00:22:21.200
And then it'll look at trends, it'll look at trends and uh and any metrics that you're putting in.
00:22:21.279 --> 00:22:27.839
If you look at profitability or client profitability, you say, hey, your client profitability is trending upward because of this, this is the reason why.
00:22:28.000 --> 00:22:35.839
So it is not just a static analysis, narrow analysis, it's a live machine that's continuously looking at data and giving you insights before you could even look at it.
00:22:36.079 --> 00:22:36.480
Excellent.
00:22:36.640 --> 00:22:40.480
You're walking along and like, oh, okay, yeah, they're gonna pull this software in it to do a solution.
00:22:40.559 --> 00:22:42.799
And like, wait a minute, did he just say ongoing?
00:22:42.960 --> 00:22:43.599
Really interesting.
00:22:44.000 --> 00:22:45.440
Is there anything that we haven't asked?
00:22:45.759 --> 00:22:47.759
Any other um features?
00:22:48.160 --> 00:22:53.119
I think, you know, I think a lot of us are living real time of the AI revolution, right?
00:22:53.200 --> 00:22:54.400
Uh I I think, yeah.
00:22:54.559 --> 00:22:56.160
I think we're all fascinated with it.
00:22:56.240 --> 00:22:59.200
Uh we we benefit from it because we're offering an AI solution.
00:22:59.279 --> 00:23:02.559
Uh, but I think banking in a whole could definitely benefit from it.
00:23:02.720 --> 00:23:06.160
I think we're really just at the tip of where this is right now.
00:23:06.400 --> 00:23:12.559
I think where we are right now is what we're offering, which is this is intelligence, allowing you can start seeing that right now, right?
00:23:12.720 --> 00:23:21.680
Bigger banks or even green banks are looking for products like ours where they're normalizing the data, gathering data, making decisions faster, trying to make decisions faster and smarter, right?
00:23:21.839 --> 00:23:23.759
Um and I think that's where we are now.
00:23:23.839 --> 00:23:32.720
But I think going forward, the second phase of this AI revolution to be uh around augmenting what banks already know about the uh the local economy, right?
00:23:32.799 --> 00:23:35.359
So AI could provide you quantitative data.
00:23:35.519 --> 00:23:38.000
What it can't provide you is the qualitative data, right?
00:23:38.240 --> 00:23:41.920
An executive or a banking relationship manager in a local economy.
00:23:42.000 --> 00:23:43.359
I know what the local economy is, right?
00:23:43.440 --> 00:23:46.240
I know the people, I know the ins and out of the economy, right?
00:23:46.319 --> 00:23:48.240
I have my own biases, I have my own vision of it.
00:23:48.559 --> 00:23:52.000
What AI could do uh anything in this state is ultimate, right?
00:23:52.079 --> 00:23:59.200
It could either verify your opinion or uh kind of uh give you uh flags or insight on something that you might be blind to.
00:24:00.480 --> 00:24:10.720
But really, where I think AI will be in five years from now is you know, we're doing scenario modeling in terms of like, hey, when an executive wants to make a strategic decision, right?
00:24:10.799 --> 00:24:12.799
When they're trying to make a big decision around something.
00:24:12.960 --> 00:24:19.279
I think in five years, it's gonna be like what uh we were talking about continuous optimization, continuous scenario modeling.
00:24:19.440 --> 00:24:28.640
So real time deposit comes in, there's a machine running background that's looking at the data and analyzing and putting out possible scenarios of what the potential outcomes is.
00:24:28.720 --> 00:24:34.480
So it's constant feeding and optimizing the data set uh of the bank and giving information real time.
00:24:34.640 --> 00:24:36.000
I think that's where we're gonna be.
00:24:36.160 --> 00:24:42.000
Right now, we're just at the at the beginning, the tip of it, where hey, we have AI, we have a tool that allows you to scenario model.
00:24:42.319 --> 00:24:50.559
Ultimately, it's gonna be part of the platform where it's an AI assistant as data being fed into the system, it's being optimized real time uh on it.
00:24:50.880 --> 00:24:53.519
That I I love that's you know, that's a vision.
00:24:53.680 --> 00:24:54.559
You're ahead of the game.
00:24:54.720 --> 00:24:55.039
Love it.
00:24:55.200 --> 00:24:56.480
Sean, anything else from you?
00:24:56.720 --> 00:25:00.319
Yeah, no, just just again, from from our perspective at Nietzsche, right?
00:25:00.400 --> 00:25:03.839
Like the healthier this banking segment is, right?
00:25:03.920 --> 00:25:05.440
It it I think it benefits.
00:25:05.599 --> 00:25:06.880
Like I'm a huge fan.
00:25:07.359 --> 00:25:09.039
We wouldn't be at Nietzsche, Joe.
00:25:09.119 --> 00:25:12.319
You've been over here 35 years, I've been here almost 29 years.
00:25:12.559 --> 00:25:16.880
We just love our community banks, our credit unions, the smaller institutions.
00:25:17.119 --> 00:25:25.920
I see it on a personal level with our, you know, we've had some merges where I live, and getting money for softball this year was three times harder than it used to be.
00:25:26.079 --> 00:25:31.039
We would walk into the local branch and say, hey, we need 500 bucks for softball, no problem.
00:25:31.200 --> 00:25:32.559
Bang, they they would do it.
00:25:32.640 --> 00:25:45.839
Now we're calling out of state to some district headquarters, and so the impact on community banks leaving and and not doing well is really, you know, it's a it's a challenge for everybody, personally and professionally for Nietzsche.
00:25:45.920 --> 00:25:56.720
And so one of the things that we're trying to drive through with all of the work we do at Nietzsche, whether it's you know, your fintech innovation series that's out right now, this is all about making FIs better.
00:25:56.799 --> 00:26:02.559
And obviously, you know, we we're we're not saying told there's no other products out there that can help you make decisions.
00:26:02.640 --> 00:26:22.319
But what I was struck by with what what he was talking about is this data system kind of because one of one of the challenges we talked about on a group which has a bank CEO that's a members on our uh vistage group as well is you know, sometimes there's a bench problem um at institutions as people get poached.
00:26:22.400 --> 00:26:25.440
And you know, as you're developing a leader, that's awesome, right?
00:26:25.519 --> 00:26:27.599
You're developing new leaders all the time.
00:26:27.759 --> 00:26:40.480
During development, decisions still need to be made that affect the future of the institution and and having something to support that as you're growing that person into the CFO role, and then they're going to be a key decision maker.
00:26:40.640 --> 00:26:52.480
But, you know, in that eight months it may take them to get ready, there may need to be a decision, a buy, sell, or a new product, or offboarding a customer, and just having something there that can help in that.
00:26:52.640 --> 00:27:01.519
There's decisions that are made every day uh at institutions on pricing, or where do we close a branch, where do we open a branch, should we take on this relationship?
00:27:01.599 --> 00:27:03.200
Is it worth this customer?
00:27:03.359 --> 00:27:06.720
And that doesn't stop when you're building your bench up, right?
00:27:06.799 --> 00:27:08.960
You don't get a timeout, unfortunately.
00:27:09.200 --> 00:27:19.440
So that this is what just struck me about my initial conversations with them is you know, uh they they kind of understand the the perils that are members of Facian.
00:27:19.599 --> 00:27:22.799
Um, but they also are are more positive, right?
00:27:22.880 --> 00:27:29.839
You read a lot of stuff about the bleakness of this size institution, the sweet spot, the 500 million to 5 billion.
00:27:30.160 --> 00:27:33.440
What I was struck by is they're like, no, these guys shouldn't be worried.
00:27:33.519 --> 00:27:39.759
They can absolutely compete and win, and you know, but it just takes thinking differently and having and having the data.
00:27:39.920 --> 00:27:43.920
And, you know, it kind of energized me a little bit because you're like, you know what, you're right.
00:27:44.000 --> 00:27:48.960
There is there is no reason that someone, especially with all the technology that exists, right?
00:27:49.039 --> 00:27:52.480
We the products are like a bill pay product's a bill pay product, right?
00:27:52.640 --> 00:27:55.680
A ACH origination's ACH origination.
00:27:55.839 --> 00:27:57.599
RTP is offering RTP.
00:27:57.839 --> 00:28:13.599
You know, we're where things get different is probably at that decision-making level and having more insight allows the chases of the world to maybe put better positioning on a product or a better target for marketing, right?
00:28:13.759 --> 00:28:15.440
That to me, I think, is the difference.
00:28:15.839 --> 00:28:30.640
It's not because inherently those people are smarter or they you know, it's it's they have the tools, and you know, whenever you can whenever we can help our members level that playing field, I think it's important to get the message out.
00:28:30.720 --> 00:28:38.400
And you know, one of the things you talk about, Joe, all the time as we think about AI, and you know, there's no bigger fan.
00:28:38.720 --> 00:28:40.400
Joe likes it for several reasons.
00:28:40.559 --> 00:28:42.319
One, it's really nice to him all the time.
00:28:43.680 --> 00:28:53.119
He needs that, he loves that, but you're always talking, Joe, that like you there's little things you can do along the way as AI is developing, right?
00:28:53.200 --> 00:29:07.359
Like this stuff we've done at Nietzsche brilliantly, that you know, we're gonna do some stuff with our AP program, which is brilliant, but it's all going towards that next chapter always of what the next like Toli was just talking about what AI could be looking like next.
00:29:07.519 --> 00:29:10.720
If in three years, right, we're at that point he's talking about.
00:29:10.880 --> 00:29:17.599
If you haven't been doing the little steps along the way, that's gonna feel like going in my 19, what did I have?
00:29:17.680 --> 00:29:21.359
A 91 Toyota Corolla trying to go up Mount Washington.
00:29:21.440 --> 00:29:25.839
Like it, I never earned the sticker that says this car climbed Mount Washington.
00:29:26.079 --> 00:29:27.680
Yeah, I was going the other way.
00:29:27.920 --> 00:29:32.319
I had the fastest decline on Mount Washington in the history of motor vehicle.
00:29:32.480 --> 00:29:36.880
So I I think there's like there's an on-ramping that needs to happen.
00:29:37.119 --> 00:29:40.799
And so it's not and this is not to do AI, just to do AI.
00:29:40.880 --> 00:29:45.759
This stuff is extremely valuable, but it also allows you to be better positioned.
00:29:45.839 --> 00:29:50.480
And so I think strategically that's what we've been trying to get across to people.
00:29:50.559 --> 00:30:00.799
And Joe, you did that session at PMC recently, great event where members got to hear, like, you know, this it's not this big overwhelming thing that you can't take advantage of it.
00:30:00.960 --> 00:30:04.000
Some of the AI conversations, I was joking totally.
00:30:04.079 --> 00:30:07.279
I took a course that I went through and it was a great course.
00:30:07.359 --> 00:30:13.519
I got this AI certificate, but like when I was signing up, I specifically asked a lady, like, is there coding in this course?
00:30:13.599 --> 00:30:14.799
And she's like, oh no.
00:30:15.039 --> 00:30:27.359
And I'm swearing in my office week five, they got me doing a project about revitalizing the prison system, and I'm coding AI, and I'm like, no, I just wanted to learn a little bit about AI and how you could use it strategically.
00:30:27.440 --> 00:30:31.920
So we all know how people feel overwhelmed, but there's simple steps along.
00:30:32.720 --> 00:30:33.920
People are not using AI.
00:30:34.000 --> 00:30:37.759
If you're coding AI, you're at the 0.04% of users right now.
00:30:38.000 --> 00:30:38.160
Yeah.
00:30:38.319 --> 00:30:39.039
Yeah, I understand.
00:30:39.359 --> 00:30:40.720
You don't need to be a coder.
00:30:40.799 --> 00:30:44.240
You you just need to understand what's out there and and make it fit.
00:30:44.640 --> 00:31:07.920
Sean, in uh in regard to your CEO at the bank saying we need bench strength and you were gonna grow that along the way, something I've realized lately, and I I realized that just yesterday, I listened to a YouTuber and talking about AI, and literally she said, Well, I was just gonna outperform by doing it more often than not.
00:31:08.079 --> 00:31:20.640
In my career, 20 years ago, 30 years ago, Anthony Robbins was saying, Well, I was just gonna be a better presenter because I was gonna present every day, where everyone else was presenting once a week.
00:31:20.799 --> 00:31:22.079
From an insight perspective.
00:31:22.160 --> 00:31:25.279
So you're building that bench strength if the person's coming up.
00:31:25.599 --> 00:31:42.799
This seems, this solution seems very much like a here's the shortcut to to all that time, because you could make decisions all along your two-year, one-year um orientation, coming to building that bench.
00:31:42.960 --> 00:31:49.680
You can make decisions along the way, realizing at the end, oh, we should have made this decision back here.
00:31:49.920 --> 00:31:50.960
Do you see that?
00:31:51.119 --> 00:31:53.839
I mean, is that a real thing or am I making that up?
00:31:54.079 --> 00:32:00.880
No, I mean, remember when we used to, Joe, we used to bring on people at niche and we'd be like, we had that same mindset.
00:32:01.039 --> 00:32:03.519
Hey, if they just present more, right?
00:32:03.680 --> 00:32:08.799
All that happens when you present more is you get just you get more comfortable presenting.
00:32:08.960 --> 00:32:11.920
It doesn't mean you know the information any better.
00:32:12.079 --> 00:32:16.799
Uh, it just means you get that those fears become, you know, even now we still do it.
00:32:16.880 --> 00:32:19.599
I probably get 10 seconds of nervousness before I speak.
00:32:19.759 --> 00:32:23.440
When I first started, it was like a minute and a half, I'd be freaking out, right?
00:32:23.920 --> 00:32:25.839
And everybody goes through that, right?
00:32:26.000 --> 00:32:37.440
The key with what you're talking about, I think, at these decisioning points is that you can use and leverage the tool so that it's not just a decision at this point.
00:32:37.599 --> 00:32:41.759
You put the factors in and say, if I do this now, what is it three years from now?
00:32:41.920 --> 00:32:43.039
How does this affect us?
00:32:43.279 --> 00:32:56.319
Because that, you know, we see that even at a small company like Nietzsche, every little decision I make here, or you make eight months or 12 months from now, we have to go back and you know, and say, oh wow, because we did this, we're now at this point.
00:32:56.480 --> 00:33:00.160
And sometimes that's good, and sometimes we get it wrong, and that and that happens.
00:33:00.319 --> 00:33:14.240
Um, but just being able to, it's not just doing it more to get better, it's actually every time you're doing it, it's like Dwight Evans, former Red Sox player, used to say, practicing perfect makes perfect, not just practicing.
00:33:14.319 --> 00:33:20.720
And I think that's what this allows you to do is you're not just making decisions to get in the habit of making decisions.
00:33:20.799 --> 00:33:25.279
You're actually getting in the habit of making good decisions based upon data.
00:33:25.440 --> 00:33:33.039
And then the more and more that you do that, your decisions are going to become more and more defendable and bulletproof and all and all of that things.
00:33:33.119 --> 00:33:39.839
And so I think just doing things that used to be that old, you know, we've had trainers here that haven't worked out.
00:33:39.920 --> 00:33:43.359
We had people go stone cold white on stage.
00:33:43.519 --> 00:33:48.960
We we've we've had you know, we've had it all in our time, and it, you know, that we were just wrong about that.
00:33:49.119 --> 00:33:51.519
Like, oh no, just do more webinars, you're gonna get better.
00:33:51.599 --> 00:33:57.279
And it was really what makes you better, like you and I as presenters, is we know the stuff cold in and out.
00:33:57.440 --> 00:34:12.960
There's not a question someone in the audience is gonna say that's gonna throw you off on a webinar, or and so I think there's that different thing in in this podcast, uh this YouTuber you're talking about, like just using AI and I'm gonna just gonna use it more than everybody.
00:34:13.119 --> 00:34:15.119
I think they're gonna run into the same challenge.
00:34:15.280 --> 00:34:23.440
If you're not using it for a purpose, or if you're not it's just gonna be something you do and you're gonna tell people you're doing it, but you're not really you're not really effective.
00:34:23.599 --> 00:34:26.400
And you know, totally you're you're more of a tech person.
00:34:26.480 --> 00:34:31.840
You like to beat stuff up and you know, what's your thoughts on this whole just use AI to use it and I'm gonna get better at it.
00:34:32.000 --> 00:34:33.360
I think you you hit it on the point, right?
00:34:33.440 --> 00:34:36.159
I think that's kind of uh pretty in the tagline, right?
00:34:36.400 --> 00:34:44.320
Not only do we allow you to increase your decision velocity, but we allow you to make fast decisions at a smarter rate, right?
00:34:44.480 --> 00:34:49.199
And that the smarter, the ability to make smarter decisions comes back from our scenario modeling, right?
00:34:49.360 --> 00:34:58.239
It allows you to say, you know, test out certain scenarios without having to risk any type of capital, any type of uh investment into it, right?
00:34:58.400 --> 00:35:02.960
And that allows you to appear up, be more confident when you walk into an outgoing meeting, right?
00:35:03.119 --> 00:35:09.039
Knowing that the decision you make knows exactly how that's gonna impact your capital ratios, your local ratios.
00:35:09.199 --> 00:35:10.960
And that's where I think Predian does.
00:35:11.039 --> 00:35:12.079
And I agree with you, right?
00:35:12.239 --> 00:35:15.039
Just making fast decisions is not gonna help you, right?
00:35:15.199 --> 00:35:17.599
Making fast, smart decisions is the key thing.
00:35:17.760 --> 00:35:23.440
And with Predian, because of the scenario modeling tool that we have, it gives people a lot of confidence that saying, hey, you know what?
00:35:23.599 --> 00:35:27.199
I feel really good about decision because I model this thing out a hundred different ways, right?
00:35:27.280 --> 00:35:28.400
Based on my expertise.
00:35:28.480 --> 00:35:32.079
And so I feel confident that the decision I'm gonna make, I know what the potential outcome will be.
00:35:32.320 --> 00:35:32.559
Excellent.
00:35:32.719 --> 00:35:35.199
Well, give you the last word, anything in partying?
00:35:35.440 --> 00:35:40.960
Yeah, I think we are a tool that, you know, I think we kind of uh talk about it.
00:35:41.119 --> 00:35:42.880
It's a tool that really levels the playing field.
00:35:42.960 --> 00:35:49.840
I think, you know, community banks are really struggling and under pressure from low profitability, uh losing key talent.
00:35:50.000 --> 00:35:52.079
You know, trying to level that playing field.
00:35:52.159 --> 00:35:55.039
It put us in the same ballpark as the big boys, right?
00:35:55.119 --> 00:36:04.400
On this end, meaning that we not only provide you the most advanced technology, but the analytical infrastructure to allow you to assess the data that's coming in.
00:36:04.639 --> 00:36:17.599
And you know, it immediately, I think, solve a huge gap in in the banking world right now, which is both like you know, how do you analyze clean data, get clean data, and how do you analyze it on this end without spending a significant amount of capital.
00:36:17.920 --> 00:36:18.159
Excellent.
00:36:18.239 --> 00:36:19.119
Well, thank you for joining.
00:36:19.280 --> 00:36:21.519
Thanks to the WrestleMania.
00:36:22.159 --> 00:36:22.880
It's been a lot of fun.
00:36:23.039 --> 00:36:23.519
Yeah, yeah.
00:36:23.760 --> 00:36:24.159
Excellent.
00:36:24.239 --> 00:36:24.719
Thank you very much.
00:36:24.960 --> 00:36:25.360
All right, thank you.
00:36:26.000 --> 00:36:26.559
Thanks, everyone.
00:36:28.719 --> 00:36:35.920
On behalf of NEET, your trusted resource in payments and our guests, I'd like to thank everyone for tuning in to the podcast.
00:36:36.079 --> 00:36:49.199
If you've enjoyed this episode and want to hear more about payments innovation and modernization, you can find the Wrestling Payments Podcast on the podcast page of Neach.org or wherever you subscribe the podcast.
00:36:49.360 --> 00:36:52.719
Just search for Wrestling Payments in your podcast app.
00:36:52.960 --> 00:36:54.159
Thanks for listening.
