2020 AFP Technology Panel Discussion

INTRO: 

Welcome to the Treasury Update Podcast presented by Strategic Treasurer. Your source for interesting treasury news, analysis and insights in your car, at the gym or wherever you decide to tune in. On this special episode of the Treasury Update Podcast, Strategic Treasurer features its panel discussion on technology from the 2020 AFP Virtual Conference. 

INTRO: 

Moderator, Tom Gregory of TD Bank interviews Todd Yodder of Fluor Corporation, James Lock of J.P. Morgan Chase, Dr. Wolfgang Kalthoff of Coupa, and Craig Jeffrey of Strategic Treasurer on the value of new technologies and its potential future place on treasury management. Listen in to this lively panel discussion on how modern-day technology is transforming the way treasury and finance organizations do business. 

Craig Jeffery: 

I’m Craig Jeffrey, the Managing Partner of Strategic Treasurer. I want to welcome you to this FinTech hot seat on technology. I want to introduce you to today’s moderator. His name is Tom Gregory. He’s from TD Bank. He’s a longtime friend. 

Tom Gregory: 

Thank you all for joining us. It’s an honor for me to be moderating this panel, which as you will learn soon is made up of some distinguished experts and leaders in the rapidly changing field of technology … corporate treasury country. 

Tom Gregory: 

When I think about the treasury question and I often knew, and it’s a requirement that professionals be multifaceted. On one hand, this is a money and finance profession, but on the other hand, in many ways, it’s an information and technology. You might think of some of the critical daily functions like cash decision management, payments management, fraud detection. 

Tom Gregory: 

Treasury operations in general, the value of technology and the potential that modern day and evolving technologies provide, do make for a challenge to the treasury management professional that she or he stay current with what is happening, the trends and the promise of technologies. 

Tom Gregory: 

Today’s program is geared towards just that to learn about and explore the current and potential future place of technology and how to think about those things for your organization’s treasury management function. And we’ve assembled a fantastic and diverse panel that I’ll invite for each of them to introduce themselves to you. Let’s start off with Todd Yoder of Fluor. Todd. 

Todd Yoder: 

Happy to be here. I love to talk treasury, I love to talk technology. You’ve got a great panel here today, so I’m excited to be here. So I work for Fluor Corporation and have for about 10 years, I do derivatives and hedging strategy. Primarily I spend a lot of my time with that, and I also serve as the world headquarters innovation business transformation catalyst. 

Todd Yoder: 

So I do get to do a lot with technology and work with different groups around Fluor. Fluor is engineering, procurement construction company with a huge global footprint. So we do business in a lot of places where you wouldn’t drink the water, but it makes for a lot of fun and a lot of challenge. 

Tom Gregory: 

Terrific. Thank you. Let’s start. Let’s go to James Lock, who joins us from J.P. Morgan Chase. James. 

James Lock: 

Thanks Tom. And thanks Craig for inviting me, and thanks everyone for joining us this afternoon for this session. So my name is James Lock. I’m part of J.P. Morgan’s commercial bank dedicated industry’s team to healthcare, higher education and not-for-profits. And I lead a treasury sales organization in the southern part of the United States. 

James Lock: 

Really thrilled to be here to talk about treasury, education, technology and just help further the community of conversations to make us all smarter as we wrestle with technology and optimizing our treasury environments. 

Tom Gregory: 

Thank you. And we are thrilled that you’re here and that where community will come into play later on in this conversation. Thanks again. Also joining us from Germany and representing Coupa is Wolfgang Kalthoff. 

Wolfgang Kalthoff: 

Yeah. Thank you, Tom. Great, great to be here, looking forward to this technology discussion, which does play a very important role in treasury. I’m VP of Product and Technology for Treasury in Coupa. We’ve been building treasury products as BELLIN for decades before we were acquired by Coupa in June. And I’ve been building software ever since I left university for many different business clients. 

Tom Gregory: 

Thank you. I know two open emails that come from Coupa because that’s usually when I have to approve somebody’s expense report, which is something that makes me a good boss. Finally, we heard from him opening the panel perhaps a little bit more of an introduction from my longterm friend, Craig Jeffrey. 

Craig Jeffery: 

Yeah, thanks Tom. I said in the beginning, I’m the Managing Partner of Strategic Treasurer. Strategic Treasurer is a consulting firm that does a significant amount of research. So we’re both research consulting oriented based out of Atlanta and love working on technology. I’ve loved it for a long time and it’s good to be here with all of you who I’ve known for anywhere from one month to 20 something years. So thank you, Tom. 

Tom Gregory: 

Thank you. Finally, I’m Tom Gregory. I represent TD Bank. In the U.S. our trade name is TD Bank, America’s most convenient bank. We operate about 1,200 locations across the Eastern Seaboard. We are a part of the TD Bank Group and subsidiary of the Toronto-Dominion Bank of Toronto, Canada. 

Tom Gregory: 

Throughout this program, you’re likely to hear me reference some survey data that comes from something that Strategic Treasurer does and it does so for TD Bank. And it’s called the Treasury Perspectives Survey. Only last month, we’ve released the 2020 report. 

Tom Gregory: 

And in that survey among several other themes, we ask numerous questions about the use of technology, planned use of technology, and frankly, the level of optimism or lack thereof around technology to the corporate treasury practitioners. We asked those questions of bankers alike. And we asked several questions each year to see how sentiment might be changing or evolving over time. 

Tom Gregory: 

In 2020, we saw a rather dramatic shift in what corporate practitioners are doing or what they’re planning to do in the short-term future, like within two years, for example, APIs. In 2019, only 38% of corporates said they‘re using or planning to integrate APIs. 

Tom Gregory: 

In 2020, that number rose to 69%. And the percentage who said they’re not interested in APIs went from 49% down to 21. On the topic of SAS based or cloud-based financial technology, a similar shift, 55% adoption or short-term plans to 74% in just one year. And the not interested group went down from 36% to 20%. 

Tom Gregory: 

I’ll give you a third and last example here, commercial mobile banking. Up to 80% from a paltry 54% in 2019. And those not interested, the pool went from 37% down to, sorry, 17%. I can’t help, but attribute the pandemic to that particular swing as it relates to the use of mobile banking for commercial applications, maybe our panelists can provide some of their insights relative to the impact of the pandemic. 

Tom Gregory: 

But I want to start at a higher altitude and speak to treasury managers level of excitement in general versus let’s call it level of nervousness, intimidation, denial, et cetera. In 2019, 75% of respondents indicated that they are excited about such things as robotic process automation, artificial intelligence, machine learning with 20% indicating that they are not only not excited, but they’re admittedly nervous about these topics. 

Tom Gregory: 

In 2020, the cool kids that are excited went to four out of five, up from three out of four. So 80% are now excited, but there still remains 12% of corporates that are frankly fearful. So let’s go to the panelists. We’ve seen that AI can master the game of chess, that AI can, can fly an F16 bomber. 

Tom Gregory: 

And recently AI proved itself in that wildly popular Olympic sport of curling. Panelists can you speak to the sudden upswing in the embrace of technology, and why there remains 12% of the profession not so enthused? And perhaps we can start with Todd. 

Todd Yoder: 

Great question. And I would say over the past six months, it’s coming up even more and more, and I don’t know if it’s pandemic related or it’s just there’s a huge strides being made right now in programming. A lot of advancements there and a lot of new technologies. And there are great technologies proving to be very powerful. 

Todd Yoder: 

I think the fear that some people may have it’s been discussed is I train someone to do my job. We program RPA to do my job and then I lose my job. And Craig and I have had this discussion in detail. I don’t think it’s something that core treasury is going to see a huge change in, in the short run, but definitely in the longterm, there is going to be a change in the way we work and how we work across industries, across business. Some of them will be more effective than others. 

Todd Yoder: 

We were talking about one of the top employers in the 50 States in the United States is transportation. So drivers, whether you’re driving cargo or you’re driving people that is soon to be probably disrupted sooner than we think. And so there’ll be some disciplines there. 

Todd Yoder: 

So it’s retraining and re-skilling. I think that’s we’re going to be talking about that in depth for the next three years, but yeah, I think it’s just human nature, right? People are naturally afraid of change, and it’s those that are the most adaptive that will do the best with the changes moving forward. 

Tom Gregory: 

You think about how many people driving trucks became employed in the last few years as a result of the delivery business taking off. And you’re suggesting that, that may be a short-lived thing that can be quite startling. And I think you’re right. Human nature, do you really get excited about something that disruptive? How about you, Craig? What are your thoughts on this? 

Craig Jeffery: 

Todd had a lot of really good points there. I think the other things that I would add to it is in finance, some of this tech is going to be very, very disruptive and you see it in different areas. If you look at public accounting firms looking at auditing, some of this technology is dramatically changing where people work. And so there is a significant amount of change to that to what’s happening and there’ll be a lot less of some jobs and more of others. 

Craig Jeffery: 

The other point too, is that from some of the statistics that you said, you brought up the point that the level of nervousness has declined, and eagerness and excitement about some of these technologies increased. That’s very good. And I think we need to be mindful, as Todd said, is how do we scale up in these areas? You don’t have to know how to program bots, run machine learning and six other new types of tech, but you and your team need to be moving forward appropriately. 

Craig Jeffery: 

This idea of if you’re doing very, very manual or easily automated tasks, and you want to continue doing it the exact same way, you’re going to have these robots and APIs replacing those functions. And you will increasingly have to move around the organization that continue to find that type of work. There’s enormous amounts of work using the new technology and doing the same type of analysis. So I think it’s a really good opportunity, it’s changing quickly, and we need to stay on top of it. 

Tom Gregory: 

I couldn’t agree more. In fact, at my organization, we are almost mandating, not quite mandating, but urging employees to take advantage of personal development tools. And there are many of them, but one is called the digital journey. And we’re urging that everyone develop a level of curiosity and to continue to develop along that journey because it’s touching all of us. 

Tom Gregory: 

Since this is kind of the opening question, and it’s a kind of high level, I want to make time for all of our panelists to weigh in here. Let’s go to Wolfgang. 

Wolfgang Kalthoff: 

Yeah. So I think we’ve covered quite well the impact this is having on the workplace and the way jobs are structured and what the tasks are. Maybe there’s another reason why some people are still nervous, which is the whole question of what technology do I sensibly choose? It needs to be analyzed. You need to take a decision. Maybe that’s not an easy decision to take. 

Wolfgang Kalthoff: 

But in my opinion, that’s not really something you need to worry about. That’s the problem of us as a solution providers, we keep an overview of the technology that’s out there. We analyze it. We see when it’s ready for day-to-day use. And when we can really harness that in a way that it brings value to treasury, and that’s when we build it into our products. 

Wolfgang Kalthoff: 

So it’s really easy for you to just deploy these products and start using the technology and to profit from them, without having to understand them in all detail and without having to take your own decisions on which technology is best suited in this situation. 

Tom Gregory: 

Thank you. Yeah. And in fact your point here is very fitting with what follows in our panel because much of it has to do with how to make the choice and what is similar versus dissimilar relative to the choices that we may find ourselves making with the multitude of tech tools that are available now, and frankly, exploding. James, can you take us home on this first question? 

James Lock: 

Sure, sure. I agree. I think everything that panelists have said is, this is a very broad topic and just my opening thing would first be that I’m going to share on this panel my views, my personal opinions based on my experience in dialogue and conversation. 

James Lock: 

So on this particular topic, I take the side of the optimist. I certainly understand why some may be fearful. I think Wolfgang made some great points about just sort of the… and trying to understand it, there’s a lot of different technologies coming at us. 

James Lock: 

My perspective on this is… and this is why treasury as an industry of practitioners is such a great place to be because we have so much we can learn and we can learn from our peers and others. And so it’s having that network in that community. That’s something I’ve said earlier, and I’ll probably say it once or twice more because it’s very top of mind and important to me, but I think there’s just such a variety. 

James Lock: 

And I think if you look back at the history of mankind, change has been constant, and we’ve heard that certainly had been said many, many times, but I think we have to just remember that just because it’s going to be different doesn’t mean it’s necessarily something to be fearful. It’s to be something that’s an opportunity to learn and grow and develop. And so that’s the way I would round out that topic. 

Tom Gregory: 

Thank you. Great insight. And you’re right. The one thing that is constant is change, but to quote a line from the famous movie Wayne’s World, “We fear change.” 

James Lock: 

That’s because we are human. 

Tom Gregory: 

That’s exactly right. I also really wanted to come back to something that Wolfgang said, “When I look at the world of Fintechs, it is just an enormous and quickly expanding universe of all sorts of tools out there.” And I think one of Wolfgang’s points was there are providers that can help people that are trying to run a company, sort through all of that, understand the implications of choices and perhaps take some of these complex considerations off of their minds. 

Tom Gregory: 

Let’s get a little bit more specific and come down from the concept of general technology and get a little bit more specific. Let’s start out with a little exploration of artificial intelligence and machine learning. In my personal life, and I’m sure you’re all just like me. 

Tom Gregory: 

It’s pretty clear to me that every day the machines are learning more and more about me. They know where I am, they know what I like to eat. They have a pretty good idea, the things that I’d like to own. And it’s funny how… and by the way I told you earlier, I’m probably going to be quoting from the Treasury Perspective Survey, perhaps even more so than that, I’m going to be giving you an indication of how long I’ve been walking on this earth. 

Tom Gregory: 

So it’s funny when I think about treasury management, I’m sure James can relate to this, that we used to have this assumption that there was a trickle down dynamic that an innovation that was embraced in large multinational corporates would trickle down into the middle market, and then in some way at a small business, and maybe even into the world of consumer banking. 

Tom Gregory: 

Think about balanced reporting. If you’re old enough to remember the inception of that. But with AI and ML even in cash management, it seems to be gone in the opposite direction. Think about mobile banking that I referenced earlier, consumers have been doing it for years, but the level of skittishness on the part of the corporates perhaps has been broken through because of the pandemic, or maybe it’s just the natural course of things. 

Tom Gregory: 

But let me begin by asking Wolfgang to give us a little bit of an education on AI and ML, and perhaps an example or two of real applications in treasury. 

Wolfgang Kalthoff: 

Yeah. AI is a bit of a challenging term because where does artificial intelligence start and where not, it’s probably more in the marketing space. So I’ll settle for machine learning for now. And the basic technology behind machine learning is old. It’s neural networks. They’ve been researched since the 1950s. And it’s really a combination of mathematical innovations and the exponential increase of processing power that we have that now makes it so practical to apply these technologies in all sorts of fields. 

Wolfgang Kalthoff: 

And as you already mentioned, Tom, we use ML in all sorts of places. Maybe your car is doing a lot of machine learning to drive you places already. Your smartphone definitely is and hopefully some of the business applications you use. In treasury, for example, a key place to use it is in detecting payment fraud. I’m not sure if payment fraud is something that’s increasing due to COVID, but unfortunately it was already a hot topic before COVID as well. 

Wolfgang Kalthoff: 

And with machine learning, we can train the algorithm based on your past payment history and the past payment behavior. And based on that, we can detect potential frauds in your current payments and point them out to you before the payments go out. Technically, we do that with a support vector machine, but I don’t think we need to go into those details there. 

Wolfgang Kalthoff: 

Another very interesting application is the area of cash forecasting. Cash forecasting, I think we’re still getting there. One of the very important disciplines of treasury, but one which a lot of people ignore because there’s just so much work behind it. 

Wolfgang Kalthoff: 

But based on historical data and some additional key inputs, we can actually create a cash forecast. It’s probably not as good yet as what you would manually do, but most, or at least our customers don’t have time for the manual forecast. And if the machine learning burden is good enough, why invest the time and the tedious manager work. 

Wolfgang Kalthoff: 

At the end of the day, though, machine learning is just technology. And the real question or one key question behind it is what data are you applying it to? The same old rule of garbage in garbage out still applies. And the question is, do you have enough data, are you feeding it with the relevant data, and are you setting the parameters correctly? 

Wolfgang Kalthoff: 

So getting machine learning to deliver sensible results is a bit of an art. It takes many experiments with different algorithms and fine tuning of the parameters to get good results. The question, do you do it with open source, do you do product specific AI or machine learning? As a vendor, we use the open source libraries. There are great implementations, and we can use them to experiment with multiple algorithms before choosing one. I know it would be very inefficient to implement them all ourselves. 

Wolfgang Kalthoff: 

As a user, you’re much better off using the machine learning that’s integrated in the product that you use if it’s available. Don’t underestimate the effort it takes to configure and tune it. We do that for multiple users and customers, and that obviously makes it much more efficient. 

Tom Gregory: 

As you’re speaking, I’m thinking of a solution that the TD offers as I’m sure many banks do. Well, we used to call auto cash. Remember auto cash relieving receivables in AR from usually a lockbox transmission, but now we’re applying machine learning and AI to ingest and interpret and convert data into accounting entries that really drive the straight through processing of cash application and accounts receivable. 

Tom Gregory: 

And as you point out, this is an example of a product specific machine learning model. And as you point out, the machine learning is very much what made its way into the world of open source software, perhaps Todd can give us his perspective on that this whole idea of product specific versus open source. 

Todd Yoder: 

Sure. This is a topic that I’m really passionate about, very interested in. Back in October last year in Copenhagen, at a EuroFinance event, I did a live demo of open source programming. And anytime you do anything live in front of hundreds of people, you’re really asking for trouble, but it worked and it was a lot of fun. 

Todd Yoder: 

And I will say yesterday, I don’t know if you saw it, but Bloomberg had their November Quant webinar series call and ironically SVM came up, Support Vector Machines was one of their clients, did a presentation in the lightning round about SVM. So very relevant and very interesting. 

Todd Yoder: 

From a corporate standpoint, I’ve worked in treasury, multinational corporate treasury large footprint for manufacturing companies, orthopedics. And then I went into financial services and worked in treasury and financial services, and now I’m a project-based company. 

Todd Yoder: 

So Fluor does mega projects up to $15 billion for a four to five year project, but we also do million dollar feed projects that are small, but a lot of them are in the hundreds of millions or billions. And again, these are in places where people don’t drink the water. So they’re big, they’re challenging, and the business is forever changing, right? 

Todd Yoder: 

Because we’ll finish a project in Kuwait and we’ll start a new project in Canada, and then we’ll have three more starting, maybe one in China and one in Japan. And so we’re all over the place. So when I think of Craig knows, I don’t like the word artificial intelligence, because I think it’s been bastardized to just to be a selling tool, but ANI, artificial narrow intelligence. 

Todd Yoder: 

It’s amazing the power that it has and I’ve seen it, other treasurers that I’ve talked to colleagues and how they’re using it and the Fintechs, how they’re using it, it’s extremely powerful in the applications. I think are going to grow and it’s going to get more powerful as we continue to learn and get more data. 

Todd Yoder: 

But for me right now, my big challenge, and maybe it’s a question for Wolfgang offline, but it is as a project-based company where we don’t have… in a manufacturing environment, I would have been ready to implement two years ago because there’s a lot of rhythm and there’s a lot of signals in the market as well that you can capture with the machine learning application. So I’m super excited about it and think it’s going to continue to grow. And it sounds like Wolfgang is on the front end of that growth. 

Tom Gregory: 

Well, if you were one of the people that took the Treasury Perspective Survey, I think I know how you answered the question about the level of excitement for technology. And I think that for a company like yours, with the numbers that you were just talking about, that are massive that perhaps there can be a trickle down. You have scale economies and probably projects that have enormous ROI opportunities with the application of technology. 

Tom Gregory: 

And as you have success in those areas, which I wish you, perhaps those can be object lessons for smaller organizations. So we heard from a vendor, a practitioner, and so fair’s fair, let’s hear if the banker has something to add to this. James. 

James Lock: 

So it’s kind of hard to follow those two great expert responses. What I would say is, I am certainly not in this space of building tech like Wolfgang and how Todd deploys it and building some, it sounds like his own. Just from a reading and learning and talking to people, my view is that open source is likely going to be more common for a couple of reasons that Wolfgang mentioned and more than is past my level of knowledge in the space. 

James Lock: 

But what I would say to you is on the sort of practitioner side in the traditional treasury viewpoint is I think we’re going to get to a place where this technology is really going to help us get to where treasury can be highly automated for the routine things that have always happened, like you said, at the beginning balance reporting and the auto cash, and those things that I haven’t heard some of those terms in quite a while. 

James Lock: 

Those things have been automated, what we thought automation was five years ago. I think we’re getting to a place where the Treasurer and the Treasurer’s team walks in the morning with their cup of coffee. And half of the old day is done in 20 minutes. 

James Lock: 

And now they can spend most of their time looking at data, the analytics, and really like solving the real challenges because the Treasurer is finding themselves more and more at the decision table where at five or 10 years ago in the past, it wasn’t like that in my perspective. So I really think this is driving huge transformation and it’s all upside positive in my perspective. 

Tom Gregory: 

Let’s move to another set of technology tools, and Craig by all means I’m not deliberately keeping you out, but we’re getting some really good responses here. Let’s move on to robotic process automation or RPA and APIs, application programming interfaces, and I’m going to come back. You know what? Let me throw a curve ball and invite Craig to just give us a quick thumbnail sketch primmer on what these things are, what they might have in common and how they differ. 

Craig Jeffery: 

RPA and API, or all those things that you talked about, including machine learning? 

Tom Gregory: 

API, RPA, and nothing more for now. 

Craig Jeffery: 

Okay. So RPA, robotic process automation, robots, or bots, you can think of those as really powerful macros that move things from one system to another. They don’t sit just in Excel or a spreadsheet, for example. So there’s a lot of formatting, checking, validating, moving data around things that might grab something from a website where you can have a bot do that. So it’s bots that move and reformat data. 

Craig Jeffery: 

APIs, application programming interfaces. These have been around a long time, but the newer technology for APIs is so much better and more superior, and it’s simpler. And so this is pushed by different organizations and people that should allow an explosion of development. In other words, if you create an API tool that can provide connection, let’s say to your bank to download information, or to check, to see your balances, or to push a transaction somewhere, when you open something up, it does it. 

Craig Jeffery: 

That’s just an easier method of connecting and saying, let’s set up a secure FTP site, much less care and feeding. It’s more out of the box. And it’s more aligned to how we are personally, when you pull up your app, when we didn’t use to be locked down in COVID and nobody traveled anywhere, if you traveled to a city, you pulled up your ride chair, you poked where you were going. The person came, you saw where they were. You paid them, you stepped out without having to do anything. Your receipts were managed. 

Craig Jeffery: 

All of those were different types of APIs, calling on things like maps and payment activity, very seamless and just combined together was excellent. So this idea of APIs helping with connectivity and helping with innovation is massively important for treasury and for our overall economies. 

Craig Jeffery: 

RPAs or robotic process automation or bots are very helpful because there’s so much data and activity between systems. We have to clean that up while if you could replace something with an API, as opposed to a bot, you would do that because they don’t break like bots may break because people change things. There’s so much cleanup to do. There’s a lot of room for bots. 

Tom Gregory: 

So as we were perhaps being playful in planning this panel, we had the provocative question, is this town big enough for both of us? Those two gunslingers, RPA and API, the way you describe it, it sounds like the situation will dictate one or the other, but given the choice API. 

Craig Jeffery: 

Yeah, I think the way you described it’s good. I know James thinks about that a lot too. 

James Lock: 

Sure. I think there’s certainly room for both. I do think what you just said, Tom, it’s going to depend on the application and the purpose, but my view is I think APIs are really leapfrogging a lot of ways. With bots, which are great and they do wonderful things, but you need to have a bot supervisor because bots can get stuck. And what happens if the bot doesn’t realize this screen layout changed, or it wasn’t smart enough to like using AI, sorry, Tom, Todd has been using that term, the technology term. 

James Lock: 

But sometimes they’re like just remembering, okay, four spaces to the right, two rows down, but if they’re not as sophisticated, then they’re going to get stuck. And they’re going to pause until someone comes and fixes it. To use the macro is the best analogy to show the macro, the fields, columns changed one over or something. 

James Lock: 

I think API is don’t, from my perspective, understand they don’t have that risk or limitation to, so to speak. They’re very super fast. And I think everyone’s been using it in favor of taking a ride share or try to find a location of something, that’s APIs have been around for a long time. To me, the APIs probably have a longer runway in terms of the future and potentially could be the dominant factor, but I think today they’re both have a place. I would use both depending on the situation. 

Tom Gregory: 

Well, let’s ask Todd, do you have any issue with the references to RPA or API? 

Todd Yoder: 

No. And just a little more context on the AI, AGI, artificial general intelligence or ASI, artificial super intelligence, which is what a lot of really smart people are working on. And neuroscientists and when I think of AI, I think that’s what I think of. So not that AI doesn’t exist, it’s just, we’re towards closer and closer to consciousness and we don’t want to get sidetracked with that right now, but no, I use RPA, I have for years and use APIs both. 

Todd Yoder: 

And I think to me, they’re two different technologies. I have project one right now that I’m super excited about where I’m using both. And there are certain portions of the project that I could use RPA, where I could use an API either one. And if you can use an API, rest API, I think the benefits are huge. 

Todd Yoder: 

And so I do agree with that. It’s a lot less maintenance, but I will say on the flip side of that is RPA just since I’ve been using it more and more over the last 18 months, it’s getting smarter and smarter and the cognitive RPA. And I think there’ll be a lot of advancements there as well. 

Todd Yoder: 

So I think it’s two tools that we as treasurers and bankers and Fintechs, we want to keep them, we want to use them both. And different tools in the tool box, but there’s definitely a time when one will outshine the other. Macros don’t really go out onto the Internet and log into a bank account and pull specific data and then pull that back into a SQL database. And then I can run Python on that. So there’s certain things that RPA can do, and it does it very well. So different tools for different jobs. 

Tom Gregory: 

We’ve been talking about these tools that all do things with data. Processing data, moving data, turning data into intelligence, turning data into accounting entries, but let’s now talk about the data itself. And I’m going to ask us to take the audience to school on some terms that may be new to some of us, maybe we’ve heard them before, but aren’t quite sure exactly what they are. 

Tom Gregory: 

The theme here is big data and data analytics. Good gracious, how far have we come in our capacity to create, store and move huge amounts of data almost instantaneously. I’m really going to show you my age when Craig and I were talking about this topic. We were remembering EDI where one of the pillars of EDI syntax was the absence of fixed length fields and records. 

Tom Gregory: 

And instead there were variable length segments and elements with data elements, separators at way, a three character data element will only take up three bites instead of 15 bites in a 15 bite fixed record. And people that have come along to the world of data in the last 15 or 20 years or so would think that that is a hilarious thing to be talking about because of how data according to Craig is now doubling in volume every two years. 

Tom Gregory: 

These terms here, I’ll start with Wolfgang. Can you help us understand these terms, data lake, data cube, and again, a term for my past blob, B-L-O-B, which in this case is not the title of a horror film. 

Wolfgang Kalthoff: 

Maybe it’ll become a title of a horror film. So yeah, Todd, I can give it a shot. So data lakes are really just large collections of data, large collections of whatever data be it structured, or be it unstructured or very different types. They’ve come into fashion, or we start using them because we now have so much processing power that it makes sense to collect all this data and then just start looking at it. 

Wolfgang Kalthoff: 

Though there is still the risk of a data lake becoming a data swamp because it’s just such a mess that no one is able to make much use out of it. To make use of data lakes, typically, what you do is from the unstructured data, you do try to take a structured extract, which you can then easily use with your different analytics methodologies. 

Wolfgang Kalthoff: 

Data cubes are really a different way of storing data than with our normal relational databases. And this data stored are optimized in a way for analytics, for queries, for looking at it from different dimensions, as opposed to our relational databases, which are great for our typical operational tasks. 

Wolfgang Kalthoff: 

And in memory, just means we have all the data in main memory instead of our desk making access and processing much, much, much faster than with the price of memory going down. So far we can actually afford to do that in with large amounts of data. Coming to the famous blob, so the horror movie for the future. That’s just a binary large object, that piece of unstructured data, which we still do and use to write to databases that way. 

Wolfgang Kalthoff: 

These technologies all get along well. And what you really do need depends on what problem you’re trying to solve. In treasury space, I think we tend to have rather structured data. So if it’s just treasury, you’re looking at, you’re probably not dealing with a lake. You do want to be fast and flexible. So there’s probably some sort of data cube and maybe some in-memory data, some in-memory infrastructure involved in the backend. 

Wolfgang Kalthoff: 

Independent of what technology we use, we are now capable of collecting and analyzing large amounts of data. And that gives us new opportunities of what we can do in treasury. On a simple level, we can visualize the data and give the user tools to analyze it and find insights. This is extremely valuable and then we see a lot of customers using it, but it does quickly get to the limits when you look at the vast amount of datas we have, we need more efficient ways to understand and to learn from that data. 

Wolfgang Kalthoff: 

And looking at that and understanding that even when we sort of crunch it and visualize it in a great way, tends to be beyond our human capacity. And that is where we get back to where we were before with machine learning. That is the next big step, which belongs to, we collect these vast amount of datas, we structure them. And then we use machine learning to gather insights out of them. And that gives us a much more efficient way of learning and understanding that data and acting on that data. 

Wolfgang Kalthoff: 

The one example I already gave before, I’d love to get to the point where we have this automatic forecast just based on data. We have all the data. Now we need a great forecast out of it, not perfect, but better what we do today. 

Tom Gregory: 

I’m going to come back to this topic of forecasting in a bit. But before I do that, I’m going to ask Todd to weigh in on this range of topics about the types of the ways that we store data, the ways that we can get at it, whether it’s structured or unstructured, and how best to make use of it. 

Todd Yoder: 

I think Craig is one of the smartest I know in this space and we’ve had some lengthy discussions on data lakes and data warehouse and data universe. I haven’t heard data galaxy yet, but yeah, data blob, yeah and horror movies, but I was actually motivated on… I have a data warehouse, and I’m sure I can’t trademark this. It’s called TDW, Treasury Data Warehouse, very original, and I took it from inspiration from George Zen, the Treasurer at Microsoft. 

Todd Yoder: 

And that’s kind of the approach that they’ve taken, and they’ve been able to do a lot of really cool stuff with that and I found the same thing. So I have a treasury data warehouse. I feed it with internal private data, exposure data primarily. And then I have market data that I feed into my TDW. And then from there, I can use more traditional my Microsoft Power BI, I can use open-source programming languages, and I can scikit-learn. I can run all kinds of statistical analysis. 

Todd Yoder: 

I just had a call earlier with two guys, professor in London School of Business and a developer that used to work for hedge funds, and they do open source programming languages, and they do it full time. And so they were showing me kind of what they were up to, and it’s just amazing what we can do. 

Todd Yoder: 

So I think as treasurers again, Fintechs, banks in this space, as long as we’re creative and we’re thinking more and more about how do we solve challenges, how do we add value to the business, we’ll get smarter and smarter about what data we need and how we use the data. 

Tom Gregory: 

Maybe some day to day, it will be as smart as your friend … And you know, he and I were talking about this topic just today, and he actually introduced the new term to me, and it’s called Boomer Technology. I played it cool. I played it cool. I didn’t commit to anything that would have him realize that it was a new term for me. So I quickly went out and did some research. 

Tom Gregory: 

And the first thing I saw was a YouTube video of this person who hollowed out an empty a can of Budweiser, put a light bulb on it and strongly the cord through it. And I said, “Aha, Boomer Technology.” And for people who know me, they know that there could be nothing more fitting than for that particular thing to be my introduction to a boomer technology. So Craig, tell us a little bit more about the Boomer Technology mindset and this conversation about big data and data analytics. 

Craig Jeffery: 

Yeah. That reference was meant to not be shared publicly, but it was really… there’s this whole thing going around, if you look at some of the current social media and in some, “OK boomer”, that’s how things were done in the past. And it’s really this idea of technology comes and replaces other elements. 

Craig Jeffery: 

And so sometimes we can get stuck in our generation, whatever generation we’re sitting in, whether it’s the good music you have or the style of clothes you wear or whatever those things are, but the tech moves on and we have to adapt to it. So what’s Boomer Technology? When we talk about storing data, we have data lakes, data marts, blobs, data warehouses, data cubes. 

Craig Jeffery: 

Data cubes where you have to load the data, you pre-position totals. There’s a use for that, but it’s not nearly as essential as having access to data because you can process so quickly if you have it, you’ll keep it, but you’re not necessarily going to say, do I do a data cube or do I use a data warehouse? You’re not going to spend a lot of money on some of the older setups. You’re going to move to the newer technology. 

Craig Jeffery: 

I’m going to stick it in a data lake where it’s hierarchical, storing, or I’m going to put it in a more of these large tubs, these containers in a data lake because I can load everything into memory. I can calculate things rapidly and do fast discovery. And all of that is to say, I guess, how should a treasury person think about it versus a technologist is that, I have the ability to process things far faster and I don’t have limitations on how I store data essentially. 

Craig Jeffery: 

Now I have the ability to do rapid analysis, sequentially and instantaneously this self-discovery model, as opposed to asking people, here’s what I want to get. And they find out what data they need to find throughout the universe or throughout their system, pull it together, normalize it, give you a report. And then two seconds later, you look at that and say, I have two more questions about that. 

Craig Jeffery: 

And then the response is you should’ve told me you needed that information at the beginning and they have to go out and hunt it. That is definitely how we had to do things in the past is very linear process. And what we’re saying now is we don’t have problems with storage of data or the ability to take a long time. We can move into more self-discovery. 

Craig Jeffery: 

And for treasury it’s, now we can do that analysis, but we have to be aware that you can store it in different areas. And I don’t care if it’s a blob or a lake from a treasury perspective, a treasurer perspective. It’s a place to store things. And the technologists will tell you about the differences and how you provision it through the cloud or how they set it up. But you can store this massive explosion of data and you can analyze it in a natural process in its discovery process in a way that was not available five years ago. 

Tom Gregory: 

If you do make the investment, take the plunge and put some of these things into action in five years where there’ll be new things. And will some of this become obsolete some costs? 

Craig Jeffery: 

No, I don’t think so. I think there’s been a significant change. If you talk about versions of where we are with tech, we’re on, I don’t know, version eight, version nine and how we handle formats. We’re on XML, not on delimited. We’re worried about storing spaces we can transfer at home. I can download 300 megabits per second, right? That’s just an unseemly amount of data that you can move almost instantaneously. So these restrictions we had span data formats, our processing power. 

Craig Jeffery: 

And so this idea of we can put things together, structured, semi-structured and unstructured, and then call them to play using tools, business intelligence tools for self discovery is not likely to be lost. We’re likely to improve different elements, but we can connect to more places we can put in a best of breed arrangement and people like Todd are doing and playing and learning, but none of that’s wasted. All of that is the new platform is significantly more advanced than others. 

Craig Jeffery: 

And I’m not going to say 10 or 20 years down the road, but certainly five years everything you build now should take advantage of higher processing power and expectation of a doubling of storage in a short time period. And you need to make your data available to analyze it. 

Craig Jeffery: 

I always like someone’s quote that the Head of Jeff Bezos said to everyone at Amazon, “If you have data, you have to make it available for analysis, or you’ll be fired.” That idea of data is also power. And we need to be in that mindset. This intelligence is vital. And so no, it’s not a waste. Unless you put in Boomer Technology, if you put an old stuff, that’s a waste. It’s like don’t say this is going to be a little bit faster and you’re putting stuff that’s already obsolete. Don’t do that. 

Todd Yoder: 

Speaking of Boomer Technology, I have a 22 year old who is a senior at the Indiana School of Business Bloomington the Kelley School, and she’s in data science or neuroscience, and she’s studying a lot of this. And what is Boomer Technology? I think there’s, I don’t know which I forget which one I’m close to a Boomer Technology, but now they’re starting to talk about… she’s educating me that we’re talking about computing and are we computing, do we store data internally, do we store it in the cloud, are we computing in the cloud? And she’s already starting to talk about computing on the edge, right? 

Todd Yoder: 

And so she thinks, she says, dad, in two or three years, you guys are going to be talking about computing on the edge, instead of, so you don’t have the latency of, it’s more real time, right? So instead of the data going back to the cloud and then back to the device, it’s where the compute engine is in the cloud, you’re doing more of that on the edge and more real time so. 

Tom Gregory: 

Well, so I’m going to take us down from the cloud and acknowledge that there is so much that is possible right now with readily available technology that storing data is cost-effective, moving data is fast and easy, the tools to make something with it, they’re available, yet I’m going to go back to the Treasury Perspectives Survey. 

Tom Gregory: 

Last year, we asked to corporates, what three areas do you spend most of your time working on? And the top three cash forecasting, payments management and cash positioning and reporting. So we’ve nibbled around the topic of forecasting a few times here today, it still appears to be in the main something that’s not become a click of the mouse after several APIs or RPAs have been invoked to make for a highly confident forecast. 

Tom Gregory: 

So Craig, what’s driving the focus here? Why is it so important? And given that this is a technology discussion, what do you see as the appropriate use of the treasury practitioners time, money and effort? 

Craig Jeffery: 

Sure. Forecasting, like you said, is one of the top areas of focus on treasury and it’s been that way for at least four or five years. People spend way too much time on it. They’re not getting enough done. It’s an area where they’re committing to spend more money on things like algorithms, programs, variance analysis. So this is also an area where they think there’s a significant amount of value. 

Craig Jeffery: 

And during a pandemic, things are challenged. There’s different expectations about how broad your range of outcomes could be. And so you’re running more models and more forecasting models. So that pushes everything in the direction of we’ve got to make forecasting easier. How do we make it easier? We’re not going to hire a ton of people to do that. You might hire some, but you need to take advantage of it technologically. 

Craig Jeffery: 

And so tools that help us adapt and see why these variances is important and machine learning, which is good at detecting patterns is also highly effective. And so now we can direct some of these tools that help us do a ton of this work very quickly. And part of the challenges, many organizations have multiple lines of business that act differently, and those flows can have different cycles or periods or be impacted by different areas. So it’s complex. So it’s the right type of tool to start directing at that area for economic reasons, primarily. 

Tom Gregory: 

Wolfgang works with a company where a center of excellence happens to be technology and forecasting. He’s probably has something to say here. 

Wolfgang Kalthoff: 

Forecasting is inherently extremely difficult because we’re trying to predict the future or to grasp the future. And that’s something which is inherently not possible. We’ve talked a bit about it. One of the key aspects here is to use technology, to use things like machine learning, to use things like good visualization to help get a good forecast. 

Wolfgang Kalthoff: 

Another second important topic here is really having the relevant data to do the forecast. Part of the challenge is collecting this data and the organization because a lot of this data is there, but if you can get it, you don’t have the basis to do a good forecast. 

Wolfgang Kalthoff: 

One way of getting it is obviously collecting it via APIs or RPA. A lot of it collected is collected today manually. And this is one area where we profit from now being part of Coupa because we now have access to that whole procurement data and with the recent Lama Software acquisition, now also the supply chain data, and you can immediately see what is that impact on cash and to immediately make that part of our models. 

Tom Gregory: 

Much of what you just said, I think Todd alluded to earlier in this conversation, having the right data and making automated use of it. Todd, as a practitioner, I’m going to go out on a limb here and state that you and your organization are likely to be highly evolved as it comes, as it relates to forecasting. What can you tell us? 

Todd Yoder: 

Thank you for the compliment. We’ve got ways to go. This is actually an area that is on my list for the coming years just to do smarter and not harder and more automation to it. Again, we’re a project based company, which I think brings in a whole new set of challenges versus a manufacturing kind of environment. 

Todd Yoder: 

And then if you’re going to use the power of machine learning or deep learning in cashflow forecasting, to me, it’s about what are you going to use, what kind of signals are you bringing in? I attended a conference last month and Nassim Taleb was there. And I asked him a question on stuff we deal with every day. So how do I model based on history using statistical analysis and his response was, “Use extreme probability theory.” And I said, “Nassim, I’ve got a day job.” Okay. 

Todd Yoder: 

So to me, it’s going to be how are we going to use it? We’ve got stochastic modeling, but at the end of the day, it goes back to Daniel Economen and a lot of this stuff. And overconfidence in history gives us the idea that we think we can predict the future. There’s just so much progress that has already been made by a lot of the Fintechs in the cashflow forecasting and utilizing AML. 

Todd Yoder: 

And so I’m looking forward to exploring that more and getting more involved with that. I wish I could tell you we were market leaders and on the edge there, but that’s an area that I think we can get a whole lot smarter with. We’ve made investments in Watson Technology and as it deals with the operations of the business and different risks for mega projects, but we need to bring some cash and some investment into forecasting smarter and utilizing some of these technologies for that. 

Tom Gregory: 

So now I can say that the successful treasurer not only masters the world of volume command, and masters the world of information and technology, but now you have to have an advanced degree in regression analysis and high probability statistics. 

Todd Yoder: 

Only if you want to understand. And sometimes it’s better when you don’t understand. 

Tom Gregory: 

Hey, but we’re all members of the treasury community. It’s nice to be members of such a highly evolved species. James I think about your customers in healthcare and especially in higher ed, what must they be going through relative to their ability to forecast given what’s going on in that sector? 

James Lock: 

Well, like you’ve probably seen in most of the papers, it’s a regular topic that’s been out there. It’s a really challenging time. And I think there’s a variety of factors which too numerous to go into today. But I would say cash forecasting has always been top of mind. 

James Lock: 

I was just looking at a survey the other day about digitization and just technology. A lot of the same topics we were talking about this discussion and cash forecasting still is at the top of every treasurers and CFOs list of important priorities. It says the number one from what I’ve been studying. 

James Lock: 

And I would say to me that says, we haven’t figured it out yet. I mean, we have a lot of tools. I think Todd gave some great examples. He’s still wrestling with it. And he rattled off a few things that are like, I think light years ahead of where I think most people are, but I still think that if the treasury community and the leaders in finance are still saying that cash forecasting is the most important opportunity from a digitization using APIs and technologies and all these things we’ve been talking about, to me, it says it’s still right for improvement. 

James Lock: 

And we’ve seen Fintechs and stuff in the past couple of years do a lot of things. I think Todd just referenced one or two examples. I think that’s been some great strides. And have we made improvements? Yes, but I think there’s a lot more out there. And I think there’s a lot more opportunities and options to study and those investments, I think organizations are continuing to make as a priority. 

Tom Gregory: 

That is a great segue to our next and final topic because I think you suggest here that forecasting continues to be a challenge that we all have in common in the industry whether we’re practitioners seeking to have the decision support that forecasting gives us, whether we’re a vendor to provide the information management tools to help them do that, whether you’re a consultant, Craig helping them figure out things like what are the right data elements for your particular situation or a banker like you and me that sees a lot of different companies and can perhaps bring some best practices to across the communities we serve. 

Tom Gregory: 

And the last topic is about just that, our community. And a new term that a one of many that I learned from Craig, community intelligence. And maybe I’ll start with a rhetorical question for the audience. How impressive would the community intelligence level of this panel be if you took me out of it? Self-deprecation is one of my core competencies. So clearly, I need a lesson or at least a definition on the term. And I’d like to ask Craig to kick us off here since he was in the room where it happened to quote from Hamilton. 

Craig Jeffery: 

Well, all, I mean here is that it’s the power of networks, the networks, anybody who’s in your community, in your business, maybe in your industry or in the profession, I know everyone’s involved in finance at different areas and maybe I know James is one of the executive officers of the Atlanta AFP, and brings people together to address common problems. 

Craig Jeffery: 

So this idea of how do we come together to solve problems where it’s not at odds to competition and it could be ideas about what criminals are doing to commit fraud. How can we defend against that to what’s changing with technology? How can we learn from one another, because there’s an aspect of competition, but there’s also an aspect of, we need to leverage tech to do better and gaining efficiency helps everyone. So that’s how I would do it. 

Craig Jeffery: 

And I’ll leave it with one more point on that is you talked about Hamilton. I haven’t seen the play, but the Alexander Hamilton Awards, which is run by Treasury & Risk magazine is something that I‘ve been pretty well acquainted with. I’ve been able to be a judge for over a decade now. And we had our calls yesterday. 

Craig Jeffery: 

It’s fantastic to see what people are doing and submitting, and then that gets bubbled up and you’ll see how people are putting new ideas together, leveraging new tech or new ideas in a process to make things better. And I think that’s something we can definitely learn from a community intelligence perspective. 

Tom Gregory: 

Yeah. Thank you. Since this is the final question, I’m going to invite all the panelists to weigh in, how can we profit from each other’s insights and experiences to improve the treasury practice, the treasury profession? We’re all kind of locked at home, but we’ve managed to adapt haven’t we?. We’re all doing this virtually. We’re collaborating, we’re sharing each other’s ideas. 

Tom Gregory: 

And it used to be, you went to your local chapter of AFP and maybe to the national AFP conference to benefit from some of the networking opportunities that Craig was just describing. So what are your thoughts panelists? I’ll start with Wolfgang. 

Wolfgang Kalthoff: 

Yeah. I would actually tie this topic of community intelligence to two things. We’ve already talked about machine learning and big data. So now that we can sensitively process and analyze vast amounts of data, why limit ourselves to our own data. In a lot of places we actually profit from working together. Sure, though, there are those spaces where we are competitors, or obviously going to very much safeguard that data, but there’s a lot of places where we’re sitting in the same boat. 

Wolfgang Kalthoff: 

Coupa calls this smarter together, and it’s actually something we invest quite a bit in and in leveraging the whole intelligence and the data we have from the community to give that additional advantage back to everyone in the community. It starts with simple things like community information on how many others are paying to the bank account that I’m about to pay to. 

Wolfgang Kalthoff: 

And if I’m the first one, it might be worth an extra validation if that’s really the account I want to pay to, or if I’ve been caught by some fraud. What is the counterparty risk of my supplier or buyer? Again, a lot of information which can be taken out of the community around that. Maybe not for the bankers in the room, but how good are the terms are being offered by my bank fees or rates compared to that on average in the community? And maybe there’s some room for negotiation there. 

Wolfgang Kalthoff: 

On the more sophisticated side, there are probably joint factors affecting our future cash flows. Maybe we can distill them out of our joint data to improve all of our forecasts to pick up one of the topics we’ve already touched multiple times on. 

Tom Gregory: 

Thank you. I think given what’s going on in society, that’s a really good message. Let’s all acknowledge what we all have in common because those things are numerous in what is available to all of us if we think like a community. James, what are your thoughts on this? 

James Lock: 

Thanks for the question. I think what Craig said was really sort of very much hit the nail on the head. And I think Wolfgang gave some great examples of have that community of data taking it to the next level. I think it’s really just a matter of this collaboration doing what we’re doing today. We’ve got on this virtual call, we’ve talked about some really top of mind in key topics and sharing that. 

James Lock: 

And I think that you can use that in your AFP chapters. Like you talked about Tom, there’s places within your organization, how do you bring everyone’s knowledge of treasury up and in a virtual environment, it’s much easier to do now than it would have been last year, just because we can all do this real time in our places and not to worry about the logistics of getting together. 

James Lock: 

I think that’s something that’s really important. And to me, that’s why treasury has always been a place I’ve been for 20 years is, you’re constantly challenged. And there was a comment earlier about treasury and then technologist. 

James Lock: 

And I would say treasury people are becoming much more technologists, not coding and programming, but more so learning how these technologies operate and making them fine tuned to really drive the treasury operation because it’s all back to the goals of that organization. So I think the lines are blurring a lot and I think the way we have to stay on top of that is by being educated. 

Tom Gregory: 

Thank you for that. And Todd last, but certainly not least, what are your insights on the topic? 

Todd Yoder: 

I can tell you with certainty, Craig Jeffrey, he may not be a boomer, but he’s been doing this a long time. And so he’s been a wealth of knowledge for me. His podcast, I’m one of the nerds that when I’m getting ready in the morning, taking a shower, if I’m not listening to Bloomberg, I’m listening to one of his podcasts and learning and growing. 

Todd Yoder: 

And actually Craig invited me to do a podcast. So it’s out there. If you want the longer version of what I’m saying right now, he’s got a podcast on it where I just talk about, we really share ideas. We have similar thinking in this area and it’s about building relationships. And you help someone out whenever you can. And if you can help someone out, there’s no greater blessing in life than to be so blessed that you can help someone else and not expect anything in return. 

Todd Yoder: 

And if you take that approach to it, and you’re humble and humility to me is one of the most important things to being a leader and to building relationships and being a true, authentic leader, but things come around. So you start to build relationships, you help people out, and then when you need help, I don’t know how many times I have received help from people. I never thought I wouldn‘t need help from, but I have a question that pops up and I know who to call and ask. 

Todd Yoder: 

So a lot of great resources, so learning from each other, I think it all comes back to the most important thing, which is people and relationships. But I do definitely think Wolfgang has some great ideas there on data and how can we learn from each other on certain data that may not be restricted or classified that we can share and learn from each other. 

Tom Gregory: 

That is a terrific closing message. Be hungry, be humble, leverage relationships, give oneself to what is in the community. Super great lesson. I think the Watson Institute is referenced one in play here today. 

Tom Gregory: 

Just the other day, I watched and listened to Mark Blyth of the Watson Institute speak. And he suggested that since the pandemic and the work from home new norm, that on average the corporate workplace is getting an extra two hours a day from workers that the time we spent driving to our office and home from the office, there’s data that suggests that employers are getting an extra whole day a week from employees. 

Tom Gregory: 

And it occurred to me that we could stand to reflect on that and make those extra hours purposeful. Purposeful to develop in many ways, but certainly technology. I think this is a wake up call for anyone who’s been procrastinating on this, purposeful in developing one’s mastery of all of these technology topics and purposeful in terms of growing and nurturing a network so that we can keep the community intelligence on pace with the level of technology advancements. 

Tom Gregory: 

So I am going to offer a heartfelt thanks to our panelists personally, for helping me become more informed, grounded, and in possession of a variety of perspectives to consider as I go forward. So to Wolfgang and Todd and James and Craig, excellent discussion. Thanks so very much. 

Outro: 

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