Latest / Leaders In Payments / Special Series: Modern Finance with Yogs Jayaprakasam, SVP, Chief Technology & Digital Officer at Deluxe | Episode 422

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Special Series: Modern Finance with Yogs Jayaprakasam, SVP, Chief Technology & Digital Officer at Deluxe | Episode 422 · Leaders In Payments

• 0:01
  Speaker 1: Welcome to this special four-part series titled Modern Finance, sponsored by Deluxe, where we explore the future of financial automation.
• 0:09
  Speaker 1: From treasury to accounts payable and receivable, we're diving into how AI and intelligent automation are transforming every corner of finance.
• 0:19
  Speaker 1: In each episode, you'll hear from leaders at Deluxe who are driving innovation and delivering real-world results.
• 0:26
  Speaker 1: Whether you're navigating compliance, fighting fraud or connecting the financial dots, this series is packed with insights you won't want to miss.
• 0:34
  Speaker 1: Let's get started.
• 0:37
  Speaker 2: Hello everyone and welcome to the Leaders in Payments podcast.
• 0:39
  Speaker 2: I'm your host, Greg Myers, and this episode is part of our four-part series titled Modern Finance, being brought to you by Deluxe.
• 0:47
  Speaker 2: So today we have a very special guest on the show, Yogs Jayprakasam, who is the SVP, Chief Technology and Digital Officer at Deluxe.
• 0:56
  Speaker 2: So, Yogs, thank you so much for being here and welcome to the show.
• 0:58
  Speaker 3: It's my pleasure to be here, Greg.
• 1:00
  Speaker 3: Thanks for having me.
• 1:01
  Speaker 2: Absolutely.
• 1:02
  Speaker 2: Let's start off having you tell a little bit about yourself, maybe where you're from, where you grew up, where you currently live, just a few things like that.
• 1:09
  Speaker 3: That's a fantastic question.
• 1:10
  Speaker 3: I'm originally from India.
• 1:12
  Speaker 3: I was born and raised here.
• 1:14
  Speaker 3: I did my master's in computer application.
• 1:17
  Speaker 3: Then I started my career with a startup company Now India has a very thriving startup ecosystem, but back then, when I started, I would say I'm one of the earliest cohorts in the startup community.
• 1:31
  Speaker 3: Then I moved on to consulting.
• 1:34
  Speaker 3: I did consulting for several years, which led me to american express where I spent 13 years of my career working in payments across b2b b, b2c merchant services network business, which brought me to fantastic opportunity with Deluxe and the current role that I'm in.
• 1:51
  Speaker 3: But coincidentally I happen to be here in India as we speak now.
• 1:56
  Speaker 2: Okay, great.
• 1:57
  Speaker 2: Well, so for the audience who may not know, can you tell us a little bit about Deluxe?
• 2:02
  Speaker 3: Absolutely.
• 2:02
  Speaker 3: Deluxe is a 110-year payments and data company with our original roots in check printing, our founder, wh Hotkiss, who borrowed $300 from his father-in-law.
• 2:16
  Speaker 3: So the story goes where he founded a little company to invent what is called, 110 years back, check and checkbook.
• 2:25
  Speaker 3: So he is the original inventor and we proudly call ourselves the original payments company Because, after the currency, check is the first alternative payment form.
• 2:36
  Speaker 3: From there today we are about $2.2 million revenue company, publicly traded.
• 2:42
  Speaker 3: Half of our revenue still comes from our original check and printing business, as we call, and the rest of the revenue comes from our payments and data side of growth business, which is composed of three growth businesses deluxe merchant services, b2b payments, which includes treasury management, receivables and payments payables automation solution, and we also have a fast growinggrowing data-driven marketing business.
• 3:07
  Speaker 3: We serve 4,000-plus financial institutions across the US and North America and millions of small business and mid-size companies through our solutions.
• 3:19
  Speaker 2: Okay, so tell us a little bit about your role today.
• 3:22
  Speaker 3: I think my role is a very exciting role.
• 3:24
  Speaker 3: I joined the company about three years back and the way our CEO defined my role is for a manufacturing company, using technology is more as a utility to support the business, but when it comes to payments and data, as you know, technology is the business.
• 3:43
  Speaker 3: So the products and services that we build is all pretty much technology, particularly in payments.
• 3:48
  Speaker 3: All things that we do is technology.
• 3:49
  Speaker 3: So my team's role is to help the company to leverage technology from technology being the utility to truly turning this into a technology-driven product development.
• 4:02
  Speaker 3: So we have been modernizing our technology ecosystems into more of a platform-driven company.
• 4:09
  Speaker 3: We created more data, ai and API foundational platform ecosystem for the company, which, by the way, won a CAO 100 award for 2024.
• 4:20
  Speaker 3: On top of that, we built a lot of innovative payment products, ranging from digital wallets, our payment gateways, various DeluxePay mobile.
• 4:31
  Speaker 3: Colleagues use services inside the company, whether it is HR, legal, finance department, sales, marketing.
• 4:38
  Speaker 3: We support every one of our internal colleagues as our internal customers in the way we use technology to help their jobs to become easier and to deliver their services to our customers.
• 5:08
  Speaker 2: Okay, great.
• 5:09
  Speaker 2: Well, thanks for sharing all that background information.
• 5:11
  Speaker 2: I think it's important to help level set.
• 5:13
  Speaker 2: So let's dive into the topic at hand, which is one that's on the top of mind for everyone in our industry and a lot of industries across the world, right?
• 5:21
  Speaker 2: So we're going to talk about AI and how it's really modernizing payments in the payments ecosystem, with a little bit of a special emphasis on treasury.
• 5:31
  Speaker 2: So, if you don't mind to level set, can you maybe describe what the typical treasury function looks like and maybe the kind of size and kinds of companies and financial institutions that you work with every day?
• 5:43
  Speaker 3: I believe treasury.
• 5:45
  Speaker 3: I always think treasury as one of the oldest functions in the world of business in my mind because ever since the first business would have been created, you probably created treasury function first, because money is the core foundation of how you operate your business.
• 6:02
  Speaker 3: In my opinion, treasury includes the core function of managing liquidity, cash flow management, risk, compliance, fraud all those foundational capabilities as well.
• 6:12
  Speaker 3: So the way money comes in and the way money goes out, managing all of that thing is what treasury's function is.
• 6:18
  Speaker 3: But in the past I believe, treasury was considered more of a back office function.
• 6:23
  Speaker 3: But more and more today, with the technological innovation, treasury is becoming more of a strategic advisor how quickly you can settle the cash and predict the cash flow and really figure out which product is generating the expected revenue, etc.
• 6:38
  Speaker 3: It all becomes part of the treasury management function as well.
• 6:43
  Speaker 3: So the type of customers that we serve we either serve through 4,000 financial institutions that we sell and they take our capabilities and deliver to their customers, or we also deliver our treasury management solutions to our end customers directly as well.
• 7:01
  Speaker 3: So typically the size that we work with would be a medium-sized company with a $50 million plus revenue range all the way to a multi-billion dollar revenue company.
• 7:12
  Speaker 3: So what works nicely for the kind of capabilities that we deliver is you have thousands of invoices that you are receiving and your customers pay through multiple payment modalities, and your payment also is associated with a lot of data, invoicing, addendums and evidences.
• 7:30
  Speaker 3: So, finally, applying more complex business logic to settle in your ERP systems and apply your predictive cash flow management and all of those capabilities is what potentially becomes the best target market.
• 7:47
  Speaker 3: We always remind our customers that treasury can be complex.
• 7:51
  Speaker 3: Complex is okay, complicated is not, and we apply technology to simplify the complex for our customers.
• 7:59
  Speaker 2: Okay, okay, great.
• 8:00
  Speaker 2: So, as you well know, and everyone knows, that AI is playing a bigger role in treasury operations today, so maybe can you walk us through how technologies like AI, ml, machine learning and agentic commerce maybe, or agentic AI, how that's sort of reshaping the treasury operations today.
• 8:20
  Speaker 3: Yeah, I think everyone in treasury would say AA is not new for them, right?
• 8:25
  Speaker 3: Ever since the machine learning statistical modeling started, treasury definitely is one of the areas that has been using and leveraging that technology and capabilities.
• 8:36
  Speaker 3: So the way I look at the spectrum is treasury management, and finance in particular, has been leveraging machine learning statistical modeling for quite some time, and now with the breakthrough innovation of generative AI, which allows tertiary management in particular to leverage the ability to reason with the information and allow for you to have natural language conversation with the machine, makes things a lot easier.
• 9:05
  Speaker 3: So the way to think about this is your statistical modeling created more predictive pattern recognition in a particular situation, whether it is cash flow, fraud detection and stuff like that.
• 9:19
  Speaker 3: Now with the generative AI, you can actually interact with the system, asking questions like define my FX rate, breaches of certain threshold and things like that.
• 9:31
  Speaker 3: But certainly the agentic AI is a paradigm shift.
• 9:34
  Speaker 3: I mean, generative AI is a paradigm shift on its own measure, but it lowers, in my opinion, the barrier for anyone to leverage this technology.
• 9:45
  Speaker 3: You don't need to be a statistician, you don't need to be a data scientist.
• 9:48
  Speaker 3: You should be able to ask the right question Any good domain expert in treasury management can ask a very good question to get the right answers from the machine.
• 9:58
  Speaker 3: But agentic AI becomes a lot more targeted in the sense that it can act as your agent extension.
• 10:06
  Speaker 3: So you can apply more reasoning into the capability.
• 10:10
  Speaker 3: You can let the machine define the strategy and threshold.
• 10:15
  Speaker 3: So in a situation like your Euro strategy, if it preaches 5% deviation, execute my hedging strategy.
• 10:24
  Speaker 3: Now an agent can actually define what that hedging strategy looks like.
• 10:29
  Speaker 3: Earlier you might want your expert to define it.
• 10:32
  Speaker 3: Now your agent can actually define it and even validate what that hedging strategy looks like.
• 10:37
  Speaker 3: So in the paradigm of technology, I think it is an exciting time for domain experts in treasury management particularly.
• 10:45
  Speaker 3: All you've got to know is how to use the technology and ask the right question that is pertinent in your domain.
• 10:54
  Speaker 2: What kind of manual workloads are being eliminated through AI, and how is that enabling treasury teams to maybe make more real-time data-driven decisions?
• 11:05
  Speaker 3: I think the immediate place that AI addresses a lot for treasury management is automating and improving the quality of the document extraction Because, as you can imagine, in B2B handling whether it is check images or invoices, even addendum data extraction of the image quality could be very tricky and with our specific agent capabilities that we have developed, our accuracy rate for such image extraction dramatically improved from roughly about 65% before to about 95%, so you can imagine a 30% bump in accuracy Handling.
• 11:48
  Speaker 3: Image extraction and error rate reduction is immediate uplift for treasury agents to now not have to do matching, reconciliation, exception handling, to really start to focus on your strategic decisioning, senior leaders, on what is driving your cash flow delays, potentially what is driving your increased dispute rates and how do you address those dispute rate challenges and things like that.
• 12:18
  Speaker 3: So that's where, immediately, we are seeing a lot of uplift.
• 12:22
  Speaker 3: But of course, we have a lot of exciting new features that we are in the development using more agentic capabilities as well.
• 12:30
  Speaker 2: Okay, Can you share maybe some real-world examples, maybe of a company or a bank that's successfully integrating AI into their treasury operations?
• 12:39
  Speaker 2: I know we've said it's sort of always been there, but I think it's gotten to the point where people are using it better differently, solving unique problems.
• 12:48
  Speaker 2: So maybe share some real-world examples, and maybe something in cash management, forecasting or a risk mitigation area.
• 12:57
  Speaker 3: That's a great question, greg.
• 12:58
  Speaker 3: I see not just with our clients.
• 13:01
  Speaker 3: There are many companies that are taking bold actions.
• 13:05
  Speaker 3: Even banks are known for leveraging newer technologies very risk averse, particularly in the banking domain, but now I see a lot of headlines where the banks are taking bold actions, partnering with companies like OpenAI, using a lot of AI capabilities.
• 13:21
  Speaker 3: So our clients, particularly.
• 13:23
  Speaker 3: You can go look up on our website.
• 13:25
  Speaker 3: We recently posted a case study with one of our direct clients, mcnaughton McKay, who published leveraging agentic capabilities and how they improved their cash management applications, reducing header handling, and particularly how it not only improved the receivables functionality but also credit management side of things as well.
• 13:46
  Speaker 3: We also recently published, in partnership with one of our banking customers, comerica, how they are leveraging receivables capabilities in partnership with one of our banking customers, comerica, how they are leveraging receivables capabilities in partnership with us, and how things are improving too.
• 13:58
  Speaker 3: As we speak, we are working on our next level, cash application module.
• 14:03
  Speaker 3: Some of our primary customers are working in alpha mode.
• 14:08
  Speaker 3: More to come on that.
• 14:09
  Speaker 3: I'm waiting to get those results out soon as well.
• 14:13
  Speaker 2: Yeah, and I think if you keep up with AI and you read about it, especially in regulated industries like banking, governance, compliance, risk mitigation are huge factors.
• 14:25
  Speaker 2: So, as more and more financial institutions are adopting AI, what are some of the best practices that you see or that you help your clients with when you know it comes to that kind of governance and compliance?
• 14:38
  Speaker 2: Especially, you know around the whole new AI thing, the agentic, maybe even the.
• 14:43
  Speaker 2: You know the tools that you bring to the table.
• 14:45
  Speaker 2: You know what are some of those best practices.
• 14:48
  Speaker 3: That's a wonderful question, because it's very easy to jump onto any shiny object and not forgetting the foundational discipline that we need to have.
• 14:57
  Speaker 3: We always tell that our approach to AI is more responsible adoption of AI, which, in my opinion, goes back to starting with the permissible use of the data.
• 15:08
  Speaker 3: So whose data are you handling and what permissions do you have to use that data?
• 15:14
  Speaker 3: So it goes back to the basic foundational 101 of data governance principles, so that we are very particular about.
• 15:23
  Speaker 3: So we have obligations to our customers and their customers about what we can and cannot do with our data.
• 15:30
  Speaker 3: So the approach that we take is where we have permissible rights to use the data, then we use the data to specifically train the model only for the use of that particular customer.
• 15:41
  Speaker 3: So our approach is not to universally take the data and train across the customer side, and we continue to elevate that conversation with our customers as well.
• 15:53
  Speaker 3: We have a dedicated advisory board where we periodically go back and talk about our approach to AI and how we are maintaining the data governance, and also sometimes we push the envelope a little bit more on how we may want to rethink the usage of the data.
• 16:11
  Speaker 3: The idea is we create a safe space for the customer community to feel comfortable with the way that we are agreeing to use the data.
• 16:21
  Speaker 3: That way, when we provide the value back to our customers, we all know that we still did right by our customers.
• 16:29
  Speaker 3: Our approach is not just do what law mandates.
• 16:31
  Speaker 3: It is what is right by our customers.
• 16:32
  Speaker 3: Our approach is not just do what law mandates.
• 16:33
  Speaker 3: It is what is right by our customers, and that's our principle.
• 16:38
  Speaker 3: So in order to do that, we work closely with the client advisory board.
• 16:42
  Speaker 3: We also internally have AI governance team, which includes, by the way, many of my peer groups, so we constantly review the approach to AI and data governance together as well.
• 16:54
  Speaker 2: Okay, great.
• 16:55
  Speaker 2: So obviously you know there's a lot of excitement around AI and what's possible and what's next.
• 17:02
  Speaker 2: But from your perspective I mean, you're living, breathing this every day, both what I think is interesting internally within your company, but also externally within you, externally within your client set.
• 17:13
  Speaker 2: So, from your opinion, what's real, what's next and what should treasury operations and treasury leaders really be prioritizing right now?
• 17:23
  Speaker 3: So that's a fantastic question, and I personally have been passionate about not only learning AI but seriously thinking about what's happening in AI and this important question around who's moving who's cheese, in other words, where the value is moving.
• 17:42
  Speaker 3: So I fundamentally believe that AI dramatically moved, much faster than any other technology I witnessed.
• 17:51
  Speaker 3: In my 25-plus year technological career, I witnessed multiple transformations.
• 17:56
  Speaker 3: I have been part of helping many companies drive those transformations too.
• 18:00
  Speaker 3: Ai is moving in a speed that I have not seen before.
• 18:04
  Speaker 3: The reason for that is AI is using many of the technological advancements that already took place.
• 18:11
  Speaker 3: Whether it is big data transformation, whether it is cloud transformation, or now even the chip-level hardware transformation.
• 18:19
  Speaker 3: It's all now colliding together at a point where AI is potentially becoming the user in that particular area.
• 18:26
  Speaker 3: So AI is real and it is fundamentally going to change, in my opinion, every layer of the software delivery that we have known.
• 18:36
  Speaker 3: That's why many companies and countries are actually investing in a lot of power plants, because AI requires a lot of computation, so you need a lot more power than what we can generate today.
• 18:48
  Speaker 3: So it starts with power, then it moves up to the infrastructure.
• 18:53
  Speaker 3: So there are a lot of new infrastructure being developed by many countries.
• 18:57
  Speaker 3: In many of our United States big states as well whether take Texas as an example, indianapolis, arizona there are new chip makers coming.
• 19:05
  Speaker 3: Then the value moves up from infrastructure and chip to how you are building the software and then to applications like threshold management.
• 19:14
  Speaker 3: So the point that I'm trying to make for the threshold management leaders is you might feel that it's not moving fast because you are not seeing it in your area immediately, right now, but I would like you to think that it's fundamentally changing everything.
• 19:30
  Speaker 3: So it will feel like it is coming slow and then it will feel it is sudden.
• 19:35
  Speaker 3: So the way to keep up with this is, in my opinion, each of us should be investing and learning about AI and how to use AI.
• 19:45
  Speaker 3: That's number one, because when you start to use AI, you will start to realize how to reimagine the way that you are solving problems.
• 19:54
  Speaker 3: So that's number one.
• 19:55
  Speaker 3: So number two is I think it's going to affect various career levels differently.
• 20:02
  Speaker 3: So if you were hiring an entry-level treasury analyst before, so now the entry-level analyst will come with AI technology learning already.
• 20:12
  Speaker 3: So the way you can help them to catch up on your soft skills, how to approach decision-making, exception handling inside a company, they can become very productive faster.
• 20:23
  Speaker 3: But if you are intimidated as a mid-career leader because they speak a lot about AI and you are not, then that's going to create friction as well.
• 20:32
  Speaker 3: So the leaders should one, learn about AI.
• 20:36
  Speaker 3: Two, try to combine the entry-level folks and their AI skills with your expertise and soft skills.
• 20:42
  Speaker 3: And three, facilitate more conversation with your organizations on how to reimagine the problems that you are already solving.
• 20:50
  Speaker 3: So, bottom line, my recommendation is AI is already here.
• 20:55
  Speaker 3: You may not feel the change fast enough, but starting to reimagine the way that you are solving solutions will tremendously help.
• 21:03
  Speaker 3: You will not know how to reimagine unless you really start to use this technology.
• 21:08
  Speaker 3: For example, I witnessed a lot of people using ChatGPT and they think that they are using AI, but many folks are using ChatGPT the exact same way that you're using Google search.
• 21:20
  Speaker 3: That's not what it is meant to be.
• 21:23
  Speaker 3: You've got to know how to prompt the machine learning model sorry, the large language model.
• 21:29
  Speaker 3: So the way to get the answer is about the way you ask the question and that is not like searching in Google.
• 21:36
  Speaker 3: So, learning about prompt engineering, learning about how to prompt it the right way, it's all going to be critical.
• 21:42
  Speaker 3: But on top of that, also figuring out what's your intellectual property for the company that you can't leave it out on the internet.
• 21:51
  Speaker 3: So working with your technology partners, or even partners like Dilex, to figure out how do you protect your intellectual property with the architectural approach, such as retrieval, augmented generative, which protects your IP while also taking advantage of the power of the large language model.
• 22:12
  Speaker 3: So this combination is what I would recommend for the leaders to consider going forward.
• 22:17
  Speaker 2: Okay, Okay.
• 22:18
  Speaker 2: Well, Yogs, this has been a great discussion.
• 22:20
  Speaker 2: I mean, there's so much here.
• 22:22
  Speaker 2: I feel like we're still in the early days, right, we're maybe in the top of the first inning, just getting started for a baseball analogy.
• 22:29
  Speaker 2: But maybe if you could try to summarize this conversation and provide treasury leaders maybe with just one key insight or one piece of advice I know it'll be really hard to boil it down to just one, but if you can try to boil it down to maybe just one piece of advice, what would that be?
• 22:45
  Speaker 3: My one piece of advice would be that AA is here.
• 22:48
  Speaker 3: It is going to fundamentally change everything, but what it will not change is your focus on the customer and the customer's problem that you solve In treasury.
• 23:02
  Speaker 3: Your problems are cash flow management, free cash liquidity management, risk and compliance management.
• 23:10
  Speaker 3: Those problems will always be there.
• 23:14
  Speaker 3: So how do you now reimagine those problems through the lens of ai and how do you solve it differently is what I would like you to take away, but you can only do it if you believe how this technology is fundamentally changing.
• 23:28
  Speaker 3: That requires self-learning and continuous learning so you can practically apply the technology to solve.
• 23:35
  Speaker 3: So the bottom line, your problem is not changing.
• 23:39
  Speaker 3: Your focus on customer is not changing.
• 23:41
  Speaker 3: Hang on to what is not changing and apply the technology on those problems.
• 23:47
  Speaker 2: Okay, I think that's a great way to wrap up the show, but I do want to open it up, see if there's anything else you wanted to cover, any topics we may have missed, before we wrap up the show.
• 23:56
  Speaker 3: So in treasury management.
• 23:58
  Speaker 3: I think we talked a lot about the domain of treasury management and how it is going to evolve with AI.
• 24:05
  Speaker 3: So I would like the treasury leaders to think that this is a great opportunity for each of you to become a strategic advisor for your business, from being a back office leaders providing some reports and liquid positions and things like that.
• 24:21
  Speaker 3: But the way you can approach this problem is also through the lens of talent development in your tertiary management domain.
• 24:30
  Speaker 3: So inside Dilex and also outside, I continue to preach talent approach, what I call three by three and I would like to conclude with that thought from talent development standpoint as well.
• 24:43
  Speaker 3: So what I mean by three by three is when you learn any new technology, you will always go into three phases, whether it is a beginner, intermediate and advanced.
• 24:54
  Speaker 3: But actually, when it comes to AI, I would like us to also think about based on which career level that you are in.
• 25:01
  Speaker 3: Your beginner, intermediate and advanced are going to be totally different.
• 25:06
  Speaker 3: So that is what the next three layers are in my opinion.
• 25:10
  Speaker 3: So if you are entry-level, intermediate career or an executive level, so that three combined with this three, which is beginner, intermediate and advanced level, forms your three-by-three metrics.
• 25:24
  Speaker 3: What I mean by that is, as a beginner learning AI, prompt engineering is something we all have to learn, but an entry-level person prompting the large language model would be different than a senior executive or a CEO.
• 25:40
  Speaker 3: So I would like the CEOs to be thinking about how do I prompt large language model to even develop a point of view for a new strategy?
• 25:50
  Speaker 3: For example, stablecoin is a big topic now with the Genius Act and we are all in payments world.
• 25:57
  Speaker 3: So even developing what is it going to do the stablecoin is going to do inside payments.
• 26:03
  Speaker 3: You don't have to wait for an expert to come and tell you a 30-page report.
• 26:07
  Speaker 3: You actually can go prompt to chat GPT or Perplexity or Gemini whichever is the large language model medium that you use, if you're prompted to say, okay, use jobs to be done framework or pick any strategic framework and say, give me a report on stablecoin and its impact on my business, so it will provide a preliminary strategic point of view.
• 26:34
  Speaker 3: That's fascinating for a CISO to quickly understand what's going to happen with the emerging stablecoin.
• 26:41
  Speaker 3: So I just wanted to give that one example to say that if each company should be thinking about creating a 3x3 talent strategy on how you are going to educate your organization on AI development, and that goes with the Y-axis entry-level, mid-career and executive level and X-axis beginner, intermediate and advanced level, and that's exactly what we are doing inside Deluxe as well.
• 27:08
  Speaker 3: So, again, to conclude, take any domain, but, since we are talking about, treasury is an amazing place to be in in the age of AI but take that, learn for yourself.
• 27:21
  Speaker 3: But also apply the three by three talent strategy, which will help you transform the way that you run your company and your business.
• 27:30
  Speaker 2: Well, yogs, I think that's an incredible way to wrap up the show.
• 27:33
  Speaker 2: So thank you so much for being here today sharing all your great insights.
• 27:36
  Speaker 2: I really appreciate your time.
• 27:38
  Speaker 3: Thank you so much for this great opportunity.
• 27:40
  Speaker 3: Greg, have a wonderful day.
• 27:41
  Speaker 2: You too, and to the rest of you out there, you listeners out there.
• 27:45
  Speaker 2: Thank you so much for your time as well, and until the next story.
• 27:50
  Speaker 1: Thank you for listening to today's episode.
• 27:51
  Speaker 1: If you'd like more information on the transformative potential of AI and automation in modern finance, please visit wwwdeluxecom.
• 28:01
  Speaker 1: Slash receivables hyphen management.
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