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Redefine your business with technology
At this time I'd like to formally welcome everyone today to our May webinar focusing on the topic of redefining your business with technology hosted by your U.S. Bank Global Treasury Management partners. My name is Brianna Dunn, and I'm from U.S. Bank's Global Treasury Management Learning and Development Team. And I'll be moderating today's webinar.
Before we go ahead and start today's discussion, I'd like to introduce our speakers John Melvin and Lucy Diasio.
John is a vice president within Global Treasury Management's Working Capital Consulting Group and is also a certified treasury professional dedicated to helping clients optimize their treasury processes. In his role, he works directly with both internal and external partners to support businesses through innovative execution and change management expertise.
Lucy Diasio is our additional speaker today and is our receivables group product manager primarily responsible for the strategy and development of digital receivable services, helping to further drive efficiency and convenience to both business-to-consumer and business-to-business customers. She brings more than 25 years of financial expertise within Treasury Management, previously holding leadership roles in Corporate Treasury and Banking Product Management to improve customer experiences across the receivables and depository spaces.
We're extremely excited to have both of them with us today. So with that, I will hand it over to you, John.
Thanks so much, Brianna. And welcome everyone today. Greatly appreciate your time and attendance. On behalf of Lucy and myself, we're glad you're here.
Hey, as we look at AI, what do we think about when you hear the term AI? Because there's a lot of different things that go on through my mind in terms of just the applicability across the board, whether it's with Google Home, Alexa, or other activities, if you will.
But I think it really hit home for me recently with my cousin that lives in Phoenix. He was telling about this experience he had. It was really incredible because he went and bought a car all online. Never did a test drive, never even saw the vehicle until it showed up in front of his house. He logged in to the dealer website, engaged with a chat bot in terms of what he was looking for and what he was looking to spend on a new vehicle. The system came back, analyzed his information, approved his credit information and all that. And next thing you know, 48 hours later, he has a brand new car that's sitting in his front driveway.
And I think this is just really interesting of where we are today relative to all the activity that we don't even know that goes on in the background. I mean, I don't know. Lucy, what do you think? When you hear the term AI, what does it mean to you?
Well, it is easy to go to a consumer example like you just had. But because I'm on the receivables side, I think about a receivables example. And I think about receiving payments from a trading partner.
As everybody knows, electronic payments are increasing. And that's going up and up every single day. So a lot of times electronic payments don't have any remittance associated with it. Or they have remittance in the addenda, but it's not something I can decipher. So receivables folks have to go and figure it out. And that's normally-- either it's a pain to do or it's time consuming.
So I think about those kinds of things and can AI help in those kinds of situations. And machine learning as well-- you think about basic machine learning in a receivables environment is repetitive data. This is the bank account it came from, and this payer is paying from this bank account. That's easy to detect and to tie back to a receivable. But I think utilizing AI in a more deeper way can automate those kinds of postings within a receivables environment a lot quicker and a lot faster.
And in fact, there was a strategic treasurer survey done last year that said 75% of organizations are excited about new technology, which is phenomenal. Everybody's excited about it. But only 7% of them are actually leveraging it. And that new technology, one of them being AI.
So I have to wonder, John, how do you think companies are leveraging AI, looking at it from a business-to-business perspective?
It's really incredible because it hits so many different fronts when we talk about the vendor supplier relationship as well as the downstream customer relationship, as I talked a little bit about with my cousin in Phoenix as it relates to just the traditional consumer. But when you look at it in terms of how can it play a role, it really is about visibility and in the conjunction with creating an opportunity for the data to flow almost seamlessly in terms of that posting and application process for payments coming in, or whether it's doing the analysis on invoices that are coming in the door and then ultimately they're getting led into GL and they're creating a payment file.
So all of those processes are really cool, but it's also, in a lot of respects, all those things that are being done manually today. In a lot of cases, they're very repetitive transactions that are what I like to consider A to B. It would be great if we could predict the future behavior of both our trading partners and customers. But when you start looking at the tools perspective, it can make some predictive analytics usable when we talk about trading partner behavior or even consumer customer behavior, if you will.
Now how these are interacting with AI and who benefits the most are really key when we start talking about that true business-to-business applicability because, ultimately, when we start talking about how businesses are going to use this, it really comes down to, how did they want to interact with their vendors? How do they want to interact with their customers? And what is the usability use case for AI within those elements?
But I know, Lucy, we're seeing a ton of just applicability across the board. I think at this point and juncture, Brianna, don't we have a poll that's ready for
Yeah, I think Brianna is going to push out our first poll question. Remember that you've got 15 seconds to answer. But it's, what area of your business requires manual or human intervention the most? Accounts payable, receivables, data collection and posting, reconciliation, or liquidity?
While you're answering, I immediately go to my credit and collections example where I think of calling customers about past payments or short pays. The answer is almost always "the check's in the mail." But I think about how AI can help in that kind of a situation. How can AI enable companies to analyze their own data and find patterns? Like, how are customers paying you? Are they always paying you late? Are they always short paying?
AI technology can really digest a lot of this info and tie it back to the payers and give you the data that you need to predict how a client is going to pay you in the future and maybe get ahead of that, make it a more proactive call as opposed to a reactive call. Did you get your invoice? Is everything OK? Do you have what you need to be able to pay back? So those are the kinds of things that help you to predict behavior. And it falls into cash forecasting as well as just trying to manage your receivables in a better way.
It looks like those answers that came through, I think reconciliation is the highest one followed by receivables and payables. And that's good. That's true. Reconciliation is always probably top of mind for a lot of people because that is difficult. In some situations, it's time-consuming. And a lot of times, it's manual.
The one area that you always hear about with AI specifically is fraud and fraud detection because of course fraud never sleeps. Last year, fraud attacks increased by almost 20%. And it's expected to intensify, actually, now because of the changes in the economic condition we're currently under. It's going to become worse.
Legacy fraud systems just can't keep up with the changing behavior that debt buyers are having and the increase in sophistication, of course, of all the fraudsters that are out there. So it is one of the most common uses of AI, and huge growth is expected there. And 13% of organizations are currently using AI and machine learning here. 25% plan to adopt it in the next year or two. So most especially in AI, machine learning, predictive analytics, that's where the highest growth is expected. I think they're going to see a wide use in the credit card industry, but there's also applicability in other places as well. And predicting behavior can certainly lead to catching criminals.
So, John, from a practical perspective, what are some other practical applications of AI when you think about liquidity, for example?
Liquidity is a really hot topic right now simply because of all the market conditions that are prevalent today. And I think the one thing that really comes to mind first and foremost is cash forecasting because we're seeing more and more companies that are really focused on, OK, where are we? What is our burn rate on cash today? And how are we applying that cash? And how can we preserve it?
And so that cash flow forecasting modeling is critical in today's environment. I think that a lot of folks that have already implemented some versions of this in terms of AI and the ability to pull in information and do some of that predictive analytics have been really ahead of the game. If you're not ahead of the game in that and you're looking from the behind the lens, so to speak, I would focus on that because I think that's extremely important in today's world.
Now in conjunction with that whole idea of cash flow forecasting, is really wrapped around the predictive analytics of the spend analytics side. And what I mean by that is there's invoices that are coming in the door like we were talking about earlier that are already getting automatically fed into GL and creating that payment file. And really it's going through the approval processes to get out the door. I think being strategic with that spend analysis and leveraging that from what the information that is gathered and shared is really key in today's world.
Now the other side of this is investment modeling. Now we haven't seen a great deal of this just yet because more and more companies have been focused on the spend analytics and the cash flow forecasting side. But I think in the very near future, we're going to see more and more jump in to the investment modeling as market conditions warrant. And that investment modeling, we've seen some things that have been really cool from a think tank perspective.
I attended a brief webinar by a company that was able to show how they can tie the AI functionality into certain markets. So let's say we've got a very defined investment policy, but yet we're telling AI, OK, here are the key markets that we're allowed to invest in based upon our current guidelines. And then here's our thresholds. And then the system can come back and say, OK, here are your top three investment opportunities for today.
And I think it's really key when we look at all of this because this really drives deeply into how we can get into the whole DSO side of the house. And I know Lucy, you and I talk about the metrics of day sales outstanding and how that really is important in today's world.
Oh, for sure, yeah, especially today. I think it's not as important of a metric when the economy is doing well. But in a down economy, it does cause businesses to look a little bit closer at how old their invoices are and how quickly you're converting them from one side of the balance sheet to the other. Because if they have less cash on hand, then that is something that's really important to them. And I feel like technology can play a really significant role here and improve that whole process by reducing that DSO, reviewing behaviors of your customers, short payments, trying to get those accurate and corrected a lot quicker, and then looking at payment terms as well and maybe that will help to improve that DSO perspective and just how quickly your cash is coming in.
It's also really important outside of the technology is to really just look at your process. So in your existing orders-to-cash process, are there gaps? Are there gaps in any piece of that ordering, invoicing, delivery, or collection? Anything there that could cause issues or where there's a lot of manual processing going on because that's where you can really hone in on that and notice issues that can be corrected now. They become more prevalent now in this kind of an environment. And certainly with all the uncertainties that are out there today-- and that lay ahead of us, actually-- how can we look at AI to help us in the future a little bit more, John?
Well, great points, Lucy, in terms of just the uncertainty of today's future because there's a lot going on in the marketplace. Let's just be honest here because we've seen a complete dynamic change. I mean, we've gone from going into offices to this whole remote workforce concept now. And it's just been really interesting to watch how certain companies handle this.
I have a number of friends that are in the tech sector. And they've talked about just this crunch of companies that are just trying to get up to speed on working remotely. A number of companies have already addressed that. And they were ahead of the curve in terms of being able to really facilitate transactions quickly. But I think you know the key thing here is understanding what the expectations are of a company and their needs, and understanding where AI can play a role because I think that's extremely important as we look at how AI impacts everything that we do on a day in, day out basis from a B2B standpoint.
When we talked about just those expectations, the events of all of that really drives some very interesting dynamics particularly when we're talking about remote workforce. And Lucy, I know you and I were talking about how we've seen some companies have to manage their paper-based processes in today's environment.
Yeah, I mean, so many of us are not in the office and working from home. And even writing a check seems hard. And you don't want to necessarily mail it. There's so much around paper that have been brought to light more recently than ever. And especially you hear about all of the stories. I'm sure that many of you have heard about the US Postal Service and the problems that they're having from, will they survive? Will they be in existence years from now?
The thing we need to think about from a financial perspective is the remittance mail that the US Postal Service processes and delivers, that's the mail that is received by bank lockboxes. And it makes up the largest segment of first class mail. So if the post office were to go under, how will that impact lockbox? How will it impact the usage of that and checks in the United States?
We know checks in other economies and in Europe are way lower than what we use here. So how will something like that change how we look at paper and how it's scrutinized, and the delivery methods? And it's going to become way more costly to sustain than ever before. And just the sheer volume of and the nature of payments becoming more electronic, less paper out in the industry-- that's all going to tie back to how we utilize new technologies. And I think that will lead to a lot of different change.
You're absolutely right, Lucy. And I think that speaks directly to the former poll question, or the previous poll question we had, when we were talking about what areas are going to be key impacts. And the second level or the second issue was accounts receivable as being the most impact. And I think that's key in today's environment because there's so many different areas where accounts receivable is manual in nature. And I think that's something that we're seeing huge investment in technology.
And speaking that, our next poll question is really wrapped around the spending of AI. According to a recently updated International Data Corporation Worldwide Artificial Intelligence System Spending Guide, spending on AI systems will reach what level by 2023? And you can see the options on your right there-- 119 billion, 65 billion, 97 billion, or 82 billion? So please take a moment and answer that if you don't mind.
But I think it's really key when we start talking about the investments associated with AI because we're seeing more and more technology coming to the marketplace that is really usable. In days past, think back for some of us old people, EDI was a big player. But that was really the first elements of AI really getting involved, even though it was that A to B processing. But back then, it was very expensive and very laborious to implement. And when we start looking at where this is going in today's environment, it's really, really interesting to see how much more level the playing field is when we start talking about AI. And really where we're seeing a ton of investment is on the robotics processing automation side-- or RPA, if you will-- because that really speaks directly to the reconciliation poll question earlier because reconciliation is a great way to eliminate some of those manual transactions or manual costs-- king costs, if you will.
All right. It looks like our poll results are in. And, golly, it looks like-- boy, it's pretty close there, Lucy. Don't you think? 119 or 97.
Yeah, well, the correct answer-- well, what do you think, Lucy? What would be your guess on this? Because this is a big number.
I'll go with the majority. I'll go with the majority and say, A, 119.
Well, you know what? You're really close because the actual answer on this one is 97 billion-- all right, basically almost 98 billion. That's a huge number because when you think of this in the context of where spending was just last year-- just last year, we only spent 37 billion on AI. So that's huge-- that's 2 and 1/2 times more than what we're spending today. So I just think that's really incredible. And in terms of where we're going from an investment in technology and the back office functionalities to free up our staffs to do things that are far, far and away more important, i.e., handle exceptions because exception processing is really where a lot of costs are in a lot of shops.
So when we think about all of this, when we're thinking about in implementing any form of AI, what should you be thinking about? Well, there's a number of key areas that we can look at. First and foremost are those manual tasks. Remember, we were talking a little bit about reconciliation and how that is really a true what I like to consider A to B functionality. A, input, B, system-- those two have to marry at some point.
And if it's a repetitive task where there is that type of activity, that's a great opportunity to streamline that activity and put some form of AI. Whether it's machine learning or robotics process automation really doesn't matter. It's really up to you and your organization to figure out where and how you want to implement that.
The secondary piece to this is when you're making that decision-- we've talked about reconciliation being a true A to B, and that's very repetitive. So that's a really good use case for a AI to be employed. Now where we see poor candidates are really when it's wrapped around transactions that don't necessarily have any rules. Data is in disparate locations. Or they're having to access multiple systems to get information applied. That's when it gets a little tricky, particularly when there are some companies that have a number of joint ventures where multiple payments are being applied across the board. And so that takes a little bit more interaction and may not necessarily be a great candidate for AI.
Ultimately, it has to really be something that provides a true value-add to the organization because in a lot of instances, the investment is significant. We recently talked to a client that was looking at implementing a form of RPA for their processing. And the treasurer told me that while this would be really great, it would completely transform their entire back-end operations. And it would also be a $30 million investment. So as most of you can imagine, a $30 million investment is pretty challenging with where we are today.
So, Lucy, where do you see it fitting for some of our customers?
Yeah, I think that's a challenging number no matter what the economy's currently doing or will be doing. But I see a lot. And when I think about efficiency and processes, I go to AP and AR. And there's always that struggle between those two areas and improving the communication between the two, whether it's receipt of an invoice or receipt of invoice data, or the expected timing of a payment, or when the payment is being sent, how it's being spent. I think both areas can really benefit from increased visibility. And AI brings that increased visibility, whether that's looking at status of invoices, looking at behavior, payment dates, and terms. I think if you do anything at all manually. you have to start looking at that and looking at, how can I automate some of this? How can I make this easier?
And there's really easy ways to start to do that. You don't have to know everything there is to know about AI. I certainly don't. And a lot of people don't.
So where would you go? Look at what your objectives are and what you're trying to achieve because once you understand what your internal priorities are and practices are throughout your cycles-- so the order-to-cash cash cycle, the procure-to-pay cycle-- and develop a plan to get there. I mean, you bring in your partnerships, your expertise that those financial partnerships can bring, whether it's your bank specifically, whether that's a fintech that's out there that does much of this and can help you, or a combination of the two. Those are the areas that you can look at. And ask for help from those people because they have access to a lot of rich information that can help you improve your business.
Couldn't agree more, Lucy. Diving down into the details of the operational flow is extremely important.
Hey, guys, thanks so much for joining today. Greatly appreciate your time and the discussion around AI.
Hey, please join us next month as we look at the impact of innovation on processing receivables. That's going to be on Thursday, June 18. Please plan to join us. Once again, thanks for your time. Talk soon.