Introducing the ModSquad: Testing AI Financial Modeling Tools, So You Don't Have To... with Ian and Giles.

In this special launch episode of The Mod Squad, host Paul Barnhurst is joined by co-hosts Giles Male and Ian Schnoor to explore the evolving intersection of artificial intelligence and financial modeling. Together, they introduce a new podcast series, The ModSquad, designed to test AI tools built for financial modelers, FP&A professionals, and analysts. The trio shares their excitement, cautious optimism, and the pressing questions they hope to answer through hands-on experimentation with cutting-edge tools that promise to transform the modeling landscape.

Paul, Giles, and Ian bring decades of combined experience across FP&A, corporate finance, Excel training, and financial modeling accreditation. This introductory episode sets the tone for a candid, unscripted, and deeply analytical journey into the practical realities of AI in finance.

Expect to Learn

• Why AI won't replace financial modelers, but will transform the way they work

• How AI tools are being developed to serve finance professionals specifically

• The importance of judgment, communication, and scoping in effective modeling

• What The Mod Squad plans to test: from three-statement models to Excel esports

• How to prepare your career for the AI revolution in modeling

Here are a few quotes from the episode:

• “Just because it looks cool doesn’t mean it gives you a finished product.” - Giles Male

• “Modeling is just as much the journey as it is the destination.” - Ian Schnoor

Follow Ian Schnoor:
LinkedIn -  https://www.linkedin.com/in/ianschnoor/

Follow Giles Male:
LinkedIn -  https://www.linkedin.com/in/giles-male-30643b15/

In today’s episode:

[01:21] - Introduction of the Episode

[04:57] - Why This Series? Helping Modelers Cut Through the AI Noise  

[09:14] - Fear & Job Security: Will AI Replace Financial Modelers?  

[14:14]- The Human Element in Modeling: Judgment, Communication, Scoping  

[21:03] - Series Format: Tools We’ll Test & Why  

[26:03] - Testing Assignments: Who’s Doing What?  

[29:03] - FP&A Use Cases Paul Will Explore  

[35:21]- What to Expect: Raw Prompts, Real Results  

[36:52] - Closing: Why This Series Matters & How You Can Engage 

Full Show Transcript

[00:01:21] Host: Paul Barnhurst: Welcome to a special episode of Financial Modeler’s Corner. I have a couple of special guests or co-hosts with me. I will introduce them here in a minute and let them tell you more about themselves. But my name is Paul Barnhurst. Many of you know me as the FP&A guy, and I'm super excited for this series that we have coming. We're going to be talking about AI tools. All of you have probably seen videos, clips, things on social media where they promise to build your financial model, do all the work for you. We're as excited as everybody else about that in this series, we're going to talk about how we see these tools working. We're going to share things about them. But before we get into our purpose, I want to take a minute and introduce my co-host because I'm thrilled to have two of them with me. One, I have the humble MVP himself. Giles Male, Giles, tell the audience about yourself.

[00:02:17] Co-host 1: Giles Male: Well, you know, I like to keep things brief. So for now, Paul, just. I'm pleased to be here. My name's Giles, co-founder of Full Stack Modeller, lifelong modeler. And yeah, really, really keen to kind of get under the hood of all these AI tools with you and the other special guest whom I won't reveal.

[00:02:33] Host: Paul Barnhurst: Would you like to introduce our other special guest, Giles? We'll just kind of give you the opportunity.

[00:02:38] Co-host 1: Giles Male: I love it, we'll pass it around. The other special guest is Ian Schnorr, founder of the FMI. Ian, why don't you tell the audience who you are?

[00:02:47] Co-host 2: Ian Schnoor : That is kind of you, Giles, and thrilled to be here in partnership with the two of you. Yeah. I mean, my background, like you my entire career, more than 25 years has been spent focusing on financial modeling. I was an investment banker for a number of years. I started and founded, actually a training company called the marquee Group, where I probably taught 20 or 30,000 people. Financial modeling all over the world sold that business a couple of years ago. I've been building models, thinking about models, teaching people, and modeling for my entire career. So really excited to be here with two of my favorite financial modelers.

[00:03:21] Host: Paul Barnhurst: So that's what I like to be with you. And I usually don't get called a financial modeler. I think of myself as FP&A and you build some budgets and forecasts. I've had to admit this before. You know, the first time I built a three statement model for work was for work.

[00:03:34] Co-host 2: Ian Schnoor: You said work. I know you've probably completed the AFM program, so you learned and built a three-statement model of a company. But you said for work. So I'm going to guess it is something other than that? Three years ago.

[00:03:45] Host: Paul Barnhurst: The first time I ever did one, I didn't do one in my entire full-time FP&A career because I worked for American Express. You're not building integrated three-statement models. You know, I worked for large companies. And so it's funny when I'm with all these three statements and we talk about it, I learned so much, even though I've built some now, but I have never thought of myself as that traditional modeler when you think of investment banking and modeling. So I feel like I'm the odd man out with this group, but I love it.

[00:04:09] Co-host 2: Ian Schnoor : The new kid on the block, the new kid with the longest beard. So that is just fine. But you know what? It's modeling. It's modeling. Right, Paul? Just because you don't need a balance sheet or a cash flow statement disciplines the same. No matter how you do it, you're building a forecast in Excel. Doesn't matter if it's internal for FP purposes or to evaluate a whole company or asset management or research. There's some differences, but you've been modeling for a long time. We're proud to have you. Right, Giles. We're proud to have him referred to as a financial modeler 100%.

[00:04:36] Co-host 1: Giles Male: But I remember Paul having that conversation with you a few years ago, and it didn't surprise me that you have really good modelers like you that have not built three models. We could probably do a whole episode on what financial modeling means, but it's not just three modeling.

[00:04:52] Host: Paul Barnhurst: That is the thing we see, right? I interview guests that do them all the time and others that don't do them as much. And for this episode, really what we want to get into is how is AI going to help us as modelers, right? I'll set the stage a little bit about the project, and then we'll start going around here and we'll all be asking each other some questions. So this idea came from when we started to see all the tools out there. As I mentioned, we saw a lot of hype videos, a lot of promise, and we're all wondering, what are these tools capable of? What should people think about them? We're getting modelers asking, hey, is my job going away? Will AI build the model tomorrow? What can it do? What tool should I be using? And so we said, okay, how can we cut through the noise? How can we help the market, the community, the modelers out there understand what could be done. So we got together and we chatted and we said, well, why don't we do a podcast series where we go through and test these tools and talk about them and see what they're capable of, and give people our thoughts and our insights of what they're doing and how we think about them. And so I think that's how I think about the project. Giles, anything you'd add?

[00:06:04] Co-host 1: Giles Male: For me personally, I'm trying to come at this positively as possible. I want these tools, the founders, the teams to work with us. I want them to help us understand how they do something really special. I think we all know that AI is definitely going to kind of change the way we model. I just really want to cut through the hype and help people and help myself and the two of you collectively understand, all right, what do these things do and where does it add value and where is it worth me and other people investing my time to learn something that genuinely could make my life better in financial modeling? So that's probably what I would add.

[00:06:44] Co-host 2: Ian Schnoor : Yeah, I mean, wow, it's first of all the opportunity to work with two of my favorite financial modelers. See, I said that again. Paul called you a financial modeler, but always a top criteria. And I gotta tell you, in my career I have seen a handful of pretty major transformational changes in technology and in things that have happened. And this is probably the biggest one that we have all seen is this new inclusion of AI in our workflows and in our lives. I'm really excited about it. I am excited about adopting new technology and watching people use new technology and helping with that. At the same time. I'm a big, big believer and have always been a big believer that in the world of modeling, there is still going to be a requirement for human knowledge and for someone there will always be a senior person asking another person, what do you think? How did you come up with the view? What is your interpretation? And so there will still be I think there will need to be a fusion of technology and of human insights and human knowledge. So we're going to have to use these new tools. I, I heard a talk not long ago by one of the top AI, you know, academics in the world. And he said back 40 years ago in the advent, when when spreadsheets first came out, there were all sorts of dire predictions that there would be no longer any accountants.

[00:08:00] Co-host 2: Ian Schnoor : Um, accountants would no longer be needed because accountants spent most of their time building giant ledgers and adding up numbers, and that's how they spent the vast majority of their time. And so, of course, when you could do that quickly and automatically in a spreadsheet, the entire profession would die out. Well, of course, the opposite happened. Um, now, of course, spreadsheets add up columns of numbers for us. But accountants are on to doing different things, better things, more insightful things. And I believe that this technology will allow us to work better, smarter, faster. And do you know other things? You know what else is interesting to me? So, as you know, heading up the Financial Modeling Institute, I lead webinars almost every day all over the world to help teach people, help people learn and understand modeling discipline. And, you know, this morning I led a program for, you know, an hour a session for a group in the UK. And, uh, tonight, my time, I'm doing a webinar for a group in Australia. And I was in China recently in India. And everywhere I go, people ask me what's going to happen with AI in financial modeling, and I really want to be able to like you, Giles, help people navigate this new, uh, almost feels like a minefield of different possibilities and help people understand what's going on, to help, you know, ease their own transitions into this new way of working.

[00:09:14] Host: Paul Barnhurst: I love something you said there about ease the transition. There's a lot of fear out there. People are worried they might lose their job. They're concerned they're getting left behind. They're like, should I be using tools that I'm not using right now? What do I need to do to prepare for the future? I love that this series can help with that, right? I think if we accomplish one thing, it would be awesome to hear people say, you helped me prepare. I feel better prepared. For what? Where we're heading. Not that we have all the answers, but hopefully we can at least help guide people a little bit of where we think we're heading and what we're seeing today.

[00:09:51] Co-host 1: Giles Male: I was going to say, you know, we're all trainers, so we are all very familiar with this experience of, you know, you go into a room at a company full of finance people and the vast majority to this day are still struggling with the basics. Maybe they know a Vlookup or they've moved on to an Xlookup or they know a sumif, but they might struggle with tables, pivot tables. They've never seen a best practice model before. And suddenly you've got all this hype coming out over on the left hand side of AI and copilot and all this stuff, and, oh, there's a new tool and you must learn how to prompt so you can I can totally empathize with that feeling of like, My God, I already felt behind, and now there's this AI monster on the side and you're going, okay, well, now I now I really don't know what to do. Is it even worth my time keeping up when I'm just going to lose my job in six months? So I would like to. I think that's part of the cutting through the hype. So okay, what can these things do? What do I need to do with my time, money and effort? And you know, hopefully we can help there.

[00:10:51] Co-host 2: Ian Schnoor : I kind of feel similarly to you. I mean, first of all, will some people lose their jobs? Probably because there will be some base minimum level of transition needed. We all, I think any professional, any numeric professional, whether you're in accounting or finance and some all of whom use models, I think we would all agree that continuous improvement and continuous professional development is necessary in all of our careers. Careers are a long time. If you were still an accountant that refused to use a spreadsheet and tried to run your business by hand, you're probably not doing a lot of accounting these days. So there is some transition and transformation necessary. But you know, it can probably be managed in stages and in a reasonable way. And I would love to help, you know, with you and with you help people figure out what are the steps, what can we do, and what will be necessary to help you stay. You know, stay current and stay relevant.

[00:11:48] Host: Paul Barnhurst: And I think you make a great point. As you mentioned earlier, you know, accountants didn't go away because the spreadsheet jobs didn't go away because the internet technology comes, jobs do go away, jobs change, jobs are created. We've never had a time where it's like, all right, everybody's out of a job and modeling isn't going to go away. Oh, but maybe they hire a little less. Maybe you need more experience. Maybe there's certain things you need to do to be ready. If you stay static with all these changes that are going on and in the next few years, you're probably at risk. But if you prepare yourself, you're going to be in a much better spot than if you don't. And that's all you can do. None of us can control the future, but we can assure you that the modeling industry isn't going to be gone overnight. There's going to be six months from now where AI is doing all the modeling. At least I see no scenario in the next five years that that's the case.

[00:12:44] Co-host 2: Ian Schnoor : No. And it's interesting you say that because, you know, we are starting to see some signs of that in some jobs and some professions. Right. You keep hearing that. We all keep hearing that in the technology space, in the computer science space, in the programming space, it's getting a little harder and harder that AI is replacing the need for, you know, large numbers of programmers. Um, and so I think people will have to adapt. I'm with you, though. I happen to believe that financial modeling is and I'm prepared to kind of go, you know, put I'm prepared to go on the record saying this. And I think you both would agree with this, which is that financial modeling as a profession is a little different than some professions, like computer programming, perhaps, or even data science, in the sense that financial modeling is a journey as much as a destination, maybe even more. I mean, in some professions, it's just get me the right code that will get us this answer. And I don't care if it takes you four days or four minutes. We just want the right sequence of code. And so if you can speed that up, you know you're better off and there's no reason not to. But modeling is just as much as the journey of getting smart, getting insightful, and learning because you, I believe, are still going to need to be the person in front of the board of directors, making your case, making your claim. Uh, and so, uh, there is still going to be a requirement to learn all the things you need to learn to look intelligent in front of the room. And so I'm less worried about financial modeling dying off as a profession. I don't know what you guys think about that. If Giles or Paul, if you would have a feeling on that topic.

[00:14:14] Co-host 1: Giles Male: I've spoken to a lot of good financial modelers. And again, um, many of you are aware listening to this has dim early. I've spoken to dim before world champion financial modeling. And he's always said, you know, that phase at the start project, when you're finding out information is generally where he feels like he adds the most value. The modeling is the modeling. But that bit when you've got to get data or information from people and challenge and push and listen and go through that very kind of soft skills based phase is that that's quite hard to replicate just with, you know, prompt.

[00:14:50] Co-host 2: Ian Schnoor : And I'll just add, I mean, modeling, you know, people sometimes ask me in a modeling project, um, how much of your time are you spending in front of a spreadsheet? And I say, rule of thumb, about half, right? I mean, modeling is not just a profession where you're sitting, you know, in a dark room in front of a spreadsheet for 24 hours a day. Right? Half the battle is the nuance of talking to people and researching and getting insights and trying to ask good questions and learn and take all of that and take that. What the tax guy told you and the salesperson and cost team and kind of collectively, you know, frame it all together into a beautiful tool. I mean, that process is so incredibly important for getting smart and demonstrating, um, great insights. And that where that brings is judgment and that requires a lot of judgment. And, you know, one of the, again, one of the top AI experts in the world. I've listened to it a couple of times. He's a professor at U of T here in Toronto, and has said on a number of occasions that, you know, while AI is getting better and better at prediction, it is still nowhere in terms of judgment. I'm not able to decide why I should make certain decisions. And that's where human, you know, insights come into play. So for sure, I think there's going to be a need. Paul, what are your thoughts on that?

[00:15:59] Host: Paul Barnhurst: Yeah. I mean, I'm similar to both of you, right? We're brought in to help make judgment, to understand things, to validate assumptions. Sure. Can I help with assumptions? But scoping really means that scoping to try to make sure one what the person says they need and what they need are the same thing. How many of you I'm sure you've had multiple you start a project I want this, you're thinking, that doesn't make sense to me. Why would you want that? You start drilling in and what you end up with is almost something totally different than what they thought they wanted. That's really hard because. Right you. If someone goes to AI and tries to build a model, they're going to tell them what they think they need and AI is just going to go down that route. It's not going to be smart enough, maybe with few exceptions, because predicting to be like, is that really what you need? You sure. And help guide them to where they need to go. So there will always be a need for humans. Can elements be substantially reduced?

[00:16:57] Co-host 2: Ian Schnoor : What you're really saying is a financial model is much more than just a spreadsheet person, right? The best financial modeler is a spreadsheet person, but also a coach, a sounding board, a therapist, a psychologist, right? Often a problem solver. Someone who is a referee who can break up internal fights in an organization's a good listener, you know, and someone who can synthesize and a great communicator to boot. Right? So all of those things.

[00:17:22] Host: Paul Barnhurst: I once saw, you'll find this really interesting. And I know you have some I can tell Giles is about ready to say something, so we'll give it to you there in a second. Giles, I saw a graph and it was in the upper right corner. The strongest combination of communication and technical skills of any profession had corporate finance for the strongest combination, right? You have to be good with both. There's still the idea by many people where spreadsheet jockeys and yes, we love our numbers. We enjoy a good spreadsheet. I like to model from time to time and just put my head down and do that. But that's not what I love about finance. It's the conversations. It's crafting the story. It's helping somebody get something that helps them accomplish what they want a business to achieve its goals. And that is the soft side that I don't see any time in the future, at least in the near term, that AI can do that.

[00:18:12] Co-host 2: Ian Schnoor : That's right. Giles.

[00:18:13] Co-host 1: Giles Male: Yeah, a couple of things. I know we're probably going to move on to doing on this series and what the project is, but but actually, weirdly, as I was listening to the two of you talk about what, uh, a good modeler does, it's almost like we're taking that experience and that skill set, and we're now applying it to this AI Air bubble or the hype around it, because I think what we're probably going to start seeing more and more is heads of finance, CEOs, board teams on on C-suite boards saying, wow, I've seen this thing. I want that. I want that in my company. I want my finance function doing that. And I hope what we're going to do is exactly what we just described in the financial modeling sphere. We should just go, hang on a minute. Let's just assess all the information. Let's see what we actually think is going on, and we'll try and help people with that judgment. And the only other thing I would add, this isn't coming from any anti-i position. I don't think for any of us, I'm really pro AI and pro, you know, the idea that it's going to make modelers' lives more efficient and they can do even more of the analytics and the fun stuff and the insights. So, yes, we defend what financial modelers do, but it's not in any kind of anti AI. There's no sort of dogmatic anti right.

[00:19:27] Co-host 2: Ian Schnoor : I mean we might see some great tools and some poor tools out there, but at the same time, we all want to see this succeed and we want AI to work, right? I think we all feel the same way about that. We all use AI tools in our day to day job. I would also say that we all can, I think, agree that there're some pretty awful models out there right now. There are some terrible human made, I should say, human made models out there that are not working very well. And we've all experienced models that are terrible, that are filled with errors and mistakes. And if AI can help us reduce that and mitigate that, improve upon that, I think we'd be thrilled. Right?

[00:20:02] Co-host 1: Giles Male: Absolutely.

[00:20:03] Host: Paul Barnhurst: I dare say we will all see some things where AI is better than most humans. As we go through this process, we'll see areas where I hope so too, right? But I think we'll all see areas like okay that I can use again. Again, I've already seen some, right? I've talked to a lot of founders and stuff as we've been preparing for this project and I've seen, you know, videos and different things, and there are definitely some cool things out there that can be done. It's just I think sometimes also making sure the proper nuances are there. Right. Just because it looks cool and it shows you something, that doesn't mean you have a finished product. And so it's kind of trying to, like we said, bring the entire picture here to people so they can make informed decisions versus gut decisions. When they see something cool, then they buy it and they're disappointed.

[00:20:53] Co-host 1: Giles Male: As you mentioned, you've been talking to people. Is it worth us pivoting a little bit over to sort of, what are we doing? Who are we going to be talking to? What are we going to be looking at? How is it going to work?

[00:21:03] Host: Paul Barnhurst: I think that's a great idea. So a little bit of how we're thinking about this series is, you know, we've looked at a list of a lot of tools at this point, different founders reaching out to us and things. I think I have a spreadsheet with, you know, when we started 20 something and we're trying to figure out, okay, how do we what do we test? How do we look at this? Because you see a lot of different things. There's spreadsheet tools, new spreadsheets. They're saying, hey, we can build a financial model for you. There's tools in Excel where they're saying, hey, we're going to bring in the LM. These language models Claude Anthropic, OpenAI, you know, whatever Lama all these different models in and allow you to work with in Excel or in some cases even in Google Sheets. So what we've tried to do is go out and look at all these different tools. What's the different things out there? What are they focusing on? And really we've tried to dial in on what are those tools designed for the finance use case accountants, financial modelers FP&A professionals where they have a strong interest there and can help in our daily work and narrowed it down to, you know, tools in that area and said, hey, how could we go about testing these? And so that's from my view. That's where we started. And, you know, we have a good list of different tools. And there's a couple different types. There's some that have said, look, we're going to give you a new spreadsheet. It's time for a new spreadsheet experience. Or at least AI is going to work better in a spreadsheet. And there's others that have said Excel is 40 years old. We are going to build Excel. It's not going anywhere. And we're going to give you that AI tool. But we've also looked at and people have asked, you've probably been asked this, Giles, and have you been asked why do we have all these tools out there? Can't we just use Copilot or, you know, Gemini for Google Sheets? Giles, have you got that one?

[00:22:47] Co-host 1: Giles Male: I ask myself that question all the time as well. It's you know, the three of us have talked a lot about the millions of dollars that are being invested in this space. I hope we uncover all those answers as well. Why would you choose an add-in over going to an LLM directly, or hopefully Copilot in Excel is going to get better and better and better. Why would you choose a separate third party tool over that? I don't know the answer to those questions, but I'm sure lots of people are asking them.

[00:23:15] Host: Paul Barnhurst: Your thoughts there on that whole.

[00:23:17] Co-host 2: Ian Schnoor : Yeah. I mean, just generally, I would say we're very quickly evolving and right at the beginning of this wave. And, you know, I saw a stat not that long ago that said in the I think it was the 1920s, maybe the 1930s, about 100 years ago, there were approximately at one point, there were approximately 3000 car makers in the United States. And slowly over time, Paul, you know what happened to some of them, don't you? But, uh, there's not 3000 car makers today. So we're in a bit of a gold rush mentality right now. Everyone's trying to rush to build things, and I love it. And I think it's great. And so we want to try and help people sift through, um, the noise, what's real, what's not. And Paul, throw it back to you because I've heard you say this. If there were 3000 car makers a hundred years ago, and now there's not 3000. Uh, I think you think that there might be a similar evolution in the AI modeling space. Is that true?

[00:24:11] Host: Paul Barnhurst: Definitely. There'll be some consolidation. How much? It's hard to say, but, you know, because if you look at I do two comparisons, many of our listeners will all know what a CRM is, right? Your client relationship management, if you look out there, there are basically, I think, three tools and almost two that own like 90% of the market, right? Salesforce, HubSpot, Pipedrive in Europe, and then everything else. But go look at an FP&A tool for financial planning. Just because of the differences in the industry. There are 100 plus tools and nobody outside of Excel, which isn't an FP&A tool but can be used for it, owns the majority of the market. So how much it will consolidate what that looks like? I'm not sure yet. I haven't given that a ton of thought. What I do, I do think and why I think it's important to attest. Tools beyond Copilot is Copilot by Microsoft right now is designed for the masses or what? A billion people that use Excel, 750 million, whatever the number is, it's huge. Right? And are most of those financial modelers? No. There are all kinds of different people. Our financial modelers generally more advanced in finance. People in Excel, right? I'd say on average, as a profession, we're top 10% of the professions out there. Not to say everybody is, but, you know, we spend more time in Excel than a lot of areas. And so the question is, right now, Microsoft isn't designing a tool for financial modelers or finance. It's designing a tool for the billion users in Excel. So at least until they they show us that they're going to focus in that area, it makes sense to learn at what else is out there. Because if it can give me 20 hours of productivity extra beyond copilot, I might be willing to spend $500 or whatever the subscription price is $50.20, you know, depending on what tool and their pricing model. Right.

[00:26:02] Co-host 1: Giles Male: Really good point.

[00:26:03] Co-host 2: Ian Schnoor : So should we talk for a second and tell everyone where each, you know, bringing a slightly different perspective. And we're each bringing a slightly different lens. We have different backgrounds. And maybe what people should know is that we're each going to have a different responsibility, a different area of focus that we are going to be looking at, each of us as we test a given tool during an episode, each of us will have a different area of focus. And Giles, do you want to go first and talk about how you're going to try and push and challenge some of these tools?

[00:26:31] Co-host 1: Giles Male: Yeah, sure. So there's two things I'm going to be looking at. One is, uh, data analytics. I'm not going to specifically say big data. I almost think I'm going to keep this simple. And it's probably just going to be a reasonably large, raw data dump in a tab of Excel and see what these tools can do with it. If I prompt it to analyze the data and provide insights, create.

[00:26:56] Co-host 2: Ian Schnoor : A million rows. You know, that's still pretty big in my books. My books. That's pretty big. If you, I hope you don't get anywhere near that big.

[00:27:02] Co-host 1: Giles Male: The point for me is I'm not going to add complexity at all. We're trying to get something from SharePoint or Power Query or whatever else, or the data model is just let me make this as easy as I can. On that side, the data is in the grid of Excel. Analyze it, give me insights. And then the other thing I'm going to look at is seeing as I've been very heavily involved in the Excel esports kind of world, this last, I'm going to look at Excel Esports and I'm going to throw a case, maybe a couple of cases of varying difficulty at these tools. Now that's quite that's not a normal financial modeling challenge. But for me I just think it's an angle that's a little bit fun, you know, can it interpret quite different data set up in an esports battle workbook and figure out what to do?

[00:27:48] Co-host 2: Ian Schnoor : The reality is, even though esports makes Excel fun, it still is the same raw data skills that you need to work on your work data, right in a fun environment. I mean, you're still doing those esports challenges, and are still solving the exact same types of data problems that we all encounter at our jobs. So you're going to put it through the wringer and try to get it to solve some of those problems, which requires cleaning up messy data, analyzing data, aggregating data, etc., but in a fun realm.

[00:28:16] Co-host 1: Giles Male: Yeah. I mean, there's a slight caveat that, for example, one of the battle challenges is getting a monkey to ice skate across a lake and bounce off the walls at the end of it. So there might be the odd challenge that steps a little bit into the weird.

[00:28:30] Host: Paul Barnhurst: You don't do that in your normal Excel modeling.

[00:28:33] Co-host 1: Giles Male: I know there's also after the UK finals, by the way, there's a case all about me, so I'm very tempted to bring my own humble MVP case and use that.

[00:28:41] Co-host 2: Ian Schnoor : Well, American Express, there were monkeys going across lakes on your spreadsheets. I thought, no, maybe not monkeys.

[00:28:48] Host: Paul Barnhurst: Yes, but we didn't have ice.

[00:28:50] Co-host 2: Ian Schnoor : We didn't have ice. Okay. And that's true. But still, the skills required to do those things right, Giles, are applicable transferable. So anyway, that'll be fun for your testing. Some esports and Paul, what about you? What's your focus going to be on trying to push these tools a little bit?

[00:29:03] Host: Paul Barnhurst: I think of the guy I've been thinking about a number of different use cases, and I'll throw out some of the things I'm thinking about. We'll see what we end up with. But one like a waterfall chart that is so common. In fact, what happened last year and to the budget or to the next year and giving that bridge, asking these tools. Okay, here's some data that was set up as a bridge. What chart should I use? Can you create it for me? You know, kind of pushing to see what they can do there. I thought about headcount planning. So giving them some headcount and some assumptions and seeing if they can help build a spreadsheet, some kind of deferred water revenue, water waterfall schedule, or maybe a churn analysis. You have a list of 7 or 8 different things. We'll see what we ultimately end up with. But those are the type of things I'm thinking about. Also, some kind of analysis similar to what Giles mentioned. Mine would be, you know, more different in FP&A, but a revenue. Here's a revenue data set. Analyze it and tell me, give me some recommendations. So some things like that is where I'm thinking at the moment.

[00:30:02] Co-host 2: Ian Schnoor : You're going to think about the FP&A user, the internal user, the operating user, someone who's modeling internally, who's who's looking to kind of create really compelling insights and build, you know, automated, uh, you know, tools to help with their internal planning.

[00:30:17] Host: Paul Barnhurst: Yeah. Like another example is have a model that's built and having it, you know, you put in the extra month of actuals and ask it to update, roll forward to the next month and see how it does.

[00:30:27] Co-host 2: Ian Schnoor : Rolling a model. One of our most fun tasks in modeling. Right, people? Um, the bane of some people's existence is rolling a model.

[00:30:35] Co-host 1: Giles Male: I will be impressed if any of the tools can build or, you know, a proper dynamic actuals plus forecast model. I say that sounding cynical. I'm just saying I will be impressed.

[00:30:45] Host: Paul Barnhurst: But I think what we'll start with is give them a model where it has a few months of actuals and ask them, saying, here's the new month of data. As you go to the bottom of the table or whatever, please roll forward the model and see how it does right. Have something built and so keep it a little simpler. But even if it can do that, I'd be happy, right? It saves me a bunch of time.

[00:31:04] Co-host 2: Ian Schnoor : That'll be awesome to see. Yeah.

[00:31:06] Co-host 1: Giles Male: Ian, what about you?

[00:31:07] Co-host 2: Ian Schnoor : So my area of focus will be on sort of the corporate finance world, uh, corporate finance modeling, the type of world, the the type of modeling done by corporate finance teams at big, you know, financial service firms, accounting firms, banks, pension funds, asset managers. Three statement modeling. So, you know, we might be feeding it some cases from FMI from the AFM program, the first level accreditation, which simply says, can you take a set of historical financial statements and build us, uh, a beautiful three statement forecast model of a company? We'll look at corporate finance forecast modeling for all three statements. We'll look at editing, uh, so doing some auditing, some checking of models, maybe some modifications on models, some model builds. We'll also push I might try and push into the valuation sphere. I've taught modeling in my past life for over 20 years. I love teaching, modeling and incorporating modeling, uh, evaluation into models. And we'll see what it can do on that front. Um, also see if we can even go if some of the tools can handle some of those tests, we might get into more complex modeling requirements into some of what we do in the second level of accreditation at FMI. The CFM program, can it model a complex capital structure? Can we suggest that we've got some mortgage bonds and some term loan A and B and maybe we've got some mezzanine debt. Maybe there's debt service coverage ratios and various restrictions and reserve accounts. Does it understand what some of those concepts mean? Can it help us build complex modeling concepts into our, uh, into our kind of traditional finance models? And that's kind of the lens that I will be looking at as we look at these modeling tools. Will you be impressed if you can do that as well? I know you were impressed. If you can do Paul's things, will you be impressed?

[00:32:56] Co-host 1: Giles Male: I'll be blown away. And and again, I really hope it can. And I suspect even if we find that some or all of the tools can't, it would be really interesting. I don't know where we're going to go with this podcast in six months, a year, two years. But I just get the feeling like by the time we get to the end of the first batch of tools, you could probably go back and look at them again and it's probably all evolved again. So just because we're saying, oh, it might not do some of this stuff now, I'm sure it's not going to take long for that position to change again.

[00:33:25] Co-host 2: Ian Schnoor : You said you were hoping we would be rivaling the Joe Rogan Experience in terms of viewership within, what, the first nine months or 12 months? Oh, I'm.

[00:33:33] Host: Paul Barnhurst: Thinking three weeks. I mean, you know, someone called me the Joe Rogan of FPA, so why not be as big?

[00:33:39] Co-host 1: Giles Male: Well, we've got this wrong and we need to get some conspiracy theorists on. We need to get some UFO conspiracy theorists on. We need to. We probably should probably get some people to talk about Google Sheets or something else controversial.

[00:33:52] Host: Paul Barnhurst: Did you say Google Sheets and conspiracy theories? Is that what you said? Giles?

[00:33:55] Co-host 1: Giles Male: I was going to let you get away with this, but I saw that your, uh, management spreadsheet for this podcast was Google Sheets. Outrageous.

[00:34:03] Co-host 2: Ian Schnoor : I think for everyone listening, if you're still with us, that's where we're each going to focus. We're each going to try and push and test these tools in different ways and see how they can perform today. And what we do know is they will evolve. And in six months and nine months and 12 months, they will be better. And maybe even with some of our, you know, feedback and the feedback that you all provide, uh, from, you know, listening, watching and sharing your own experiences, uh, maybe that will help these tools continue to evolve.

[00:34:30] Co-host 1: Giles Male: I really hope that when we get into the testing, you're going to see me prompt and it goes badly. You know, I want to see those things happen where I might have to go on a bit of a learning curve of like, okay, there might be more specific things I can say to get a better answer, but we're not expecting to be heavily editing what we do, to the point where it always looks like we just give the perfect prompt and we get the best answer. I think we're all going to go on a bit of a journey as we go through this.

[00:34:56] Host: Paul Barnhurst: Yeah, and speaking to that, I think a couple of things. You know, our plan is to release the unedited video, but also do a shortened episode that people can watch. This will be released podcast audio everywhere you find financial modelers corner will have a special name that you'll know of by the time we start. Maybe we'll tell you that at the very end. Give Giles the opportunity to introduce that. But I think what's important to remember is this is really designed for it to be raw and real. We recognize some of our prompts are going to fall flat. We recognize some things are going to work better than others, and that's expected. And we want you to see that none of us are experts here. None of us expect us to just be able to start prompting and get amazing results. We're going to be better at the end of the series than we were at the beginning. The tools are going to be better. That's reality. That's how the market's working. But that doesn't mean there's not a lot of value here. And so I think our goal is to make this raw. This is real. You're going to see the video. We encourage you to watch and enjoy. We want to make this fun and educational. If the education isn't there we've failed. And we want you to come away with something that can help you be better in your job. At the end of the series.

[00:36:02] Co-host 2: Ian Schnoor : We are looking to have fun. We're excited about the future. We're excited about modeling and how these tools will help. And we are looking to help all of our watchers and listeners, all of our viewers become better, make better decisions, and, uh, and help you on your own modeling journeys. And it should be a fun one for us as well. So guys, it will be a thrill doing this together. I'm excited to do it with the two of you. And I think we, uh, we'll have something that people find helpful.

[00:36:31] Host: Paul Barnhurst: Should we reveal what our name's going to be here? They may have already seen it, because by the time this comes out, it will have been on LinkedIn.

[00:36:38] Co-host 1: Giles Male: Where are you going to be called? The Mod Squad. And we had a really cool tagline, which I can't see, but I also wonder whether we should share the tagline.

[00:36:46] Co-host 2: Ian Schnoor : We are the Mod Squad testing AI financial modeling tools so you don't have to.

[00:36:52] Host: Paul Barnhurst: Thanks everybody. And just as a wrap up, feel free to reach out to any of us with your questions and thoughts. We'd love to hear them and we're thrilled to be doing this series.