Innovative Approaches to Financial Modeling and Risk Management

Written By Ankita Ghosh

Show Notes

Welcome to the Financial Modeler's Corner (FMC), where we discuss the Art and Science of Financial Modeling with your host Paul Barnhurst.

Financial Modeler's Corner is sponsored by the Financial Modeling Institute (FMI), the most respected accreditation in Financial Modeling globally.

Raphael Benhamou, an independent consultant and fractional CFO explores the art and science of financial modeling. Raphael shares his experiences, insights, and a few horror stories from his extensive career, highlighting the importance of operational understanding and the advantages of using modern tools like Causal for financial modeling.

Sample Causal Financial models if you want to review how Causal works

Key takeaways from this week's episode include:

  • Raphael emphasizes the critical need for regular audits of financial models to prevent errors and ensure accuracy. He shares a horror story about a model that wasn't audited for five years, leading to significant financial discrepancies.

  • While Excel is known for its flexibility, Raphael discusses the trade-offs of using more structured tools like Causal, which can help reduce errors and provide more insightful analysis through its object-oriented approach and natural language variables.

  • The importance of integrating operational data with financial models to provide actionable insights. He stresses that understanding business operations is crucial for building effective and useful models.

  • Raphael shares his journey from using traditional Excel spreadsheets to adopting Causal, a modern financial modeling tool. He explains how Causal's dynamic dashboarding, probability distribution modeling, and seamless integration with other systems offer significant advantages over Excel.

  • The discussion focuses on the potential of AI in financial modeling, Raphael notes that while AI can build models, the real question is whether it should. He also touches on the importance of human judgment and expertise in interpreting and using these models effectively.

Quotes:

Here are a few relevant quotes from the episode on financial analysis and modeling:

"My perspective on this is what I was saying earlier about operating connecting to finance. I think to be a good financial modeler, you need to have a good understanding of what what's going on in the business as well, not just at the finance level.”- Raphael Benhamou

“It gave me a great grounding in best practices for modeling across all modeling platforms, especially Excel. Because one of my big nitpicks with Excel and Google Sheets is that it's too freeform.”- Raphael Benhamou

This episode provides valuable insights into the evolving landscape of financial modeling. The discussion sheds light on the benefits of modern financial modeling tools like Causal, the necessity of regular audits, and the critical role of understanding business operations. Whether you're a seasoned financial modeler or just starting, this episode offers practical advice and thought-provoking perspectives on how to enhance your financial modeling practices.

Sign up for the Advanced Financial Modeler Accreditation Today and receive 15% off by using the special show code ‘Podcast’. Visit www.fminstitute.com/podcast and use code “Podcast” to save 15% when you register. 

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In today’s episode:

[00:03] - Introduction

[01:01] - Guest Introduction

[03:47] - Key Takeaways from the Horror Story

[04:55] - Guests’ Background

[07:13] - Introduction to Causal

[08:03] - Transition to Finance

[12:29] - Love for Modeling

[14:00] - Early Career and Tools

[17:46] - Flexibility vs. Structure

[23:50] - Types of Models Built in Causal

[39:43] - Rapid-Fire Session

[48:12] - Get to know the guest

[49:46] - Conclusion

Full Show Transcript

Host: Paul Barnhurst:: Welcome to Financial Modeler's Corner, where we discuss the art and science of financial modeling with your host, Paul Barnhurst. Financial Modeler's Corner is sponsored by the Financial Modeling Institute. Welcome to Financial Modeler's Corner. I am your host, Paul Barnhurst. This is a podcast where we talk all about the art and science of financial modeling with distinguished financial modelers from around the globe. The Financial Modeler's Corner podcast is brought to you by the Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling. I'm excited to welcome you to the show, Raphael Benhamou. Welcome to the show, Raphael.

 

Guest: Raphael Benhamou:: Thanks, Paul. Good to be here.

 

Host: Paul Barnhurst:: Excited to have you. One of the first questions we start off with every guest before we even do introductions is tell me that horror story. What is the worst financial model you ever seen? I know you have one.

 

Guest: Raphael Benhamou:: It depends on how you define the worst really.

 

Host: Paul Barnhurst:: I'll leave that up to you.

 

Guest: Raphael Benhamou:: Well, there's the worst impact in terms of damage done to the company. There's the worst in terms of actual modeling quantity, or quality, I should say, or lack of actual knowledge. So let's go with the first one. In terms of impact, I think that's probably the most important and relevant to people. So I was working on a reinvestment model for a global mining company, and they had a model that was tracking 300 different mines, doing discounted cash flows for all of them, and doing in-industry accumulation, sort of aggregation of all that information and then doing some decision making off the back of it. And they hadn't audited the model in for five years, or some political stuff going on between the person who'd built the model versus the new manager in charge. When I came in and audited the model to rebuild it into new software, I identified something like 30 plus line errors, mostly cell reference errors. So basically some of the gold price tied up with the molybdenum production and stuff like that, and it was causing errors of plus 10% on a multi-billion dollar investment plan. And it was quite painful to show that to the client because they've been using this to form their investment strategy for years. And so as a consultant, there's all the sort of soft side, which is client expectation, client management, communication, getting them to understand what the impact is, and then explaining that to their managers and superiors. So the model itself was a monster beast. There were lots of mistakes in it, but the main issue was how that impacted the business and how that affected decision-making further down the line. And there's the scale of the model because we're talking about billions of dollars of investment is quite significant. So I would say that that was probably one of my worst experiences. It was also my first experience. It was a lot of learning very early on in my career.

 

Host: Paul Barnhurst:: I bet that was a great learning experience. What was the key takeaway from going through that kind of nightmare, so to speak?

 

Guest: Raphael Benhamou:: I'd say two key learning points. One is to regularly audit your model and make sure that if you've got big models like this, you have someone on your team that is sort of technologically literate and financially operational, literate, so they can understand what's going on at every line item and reviewing it on a not daily but on a regular basis. The other aspect is in terms of communication, making sure that people know what they're using, why they're using it, and what the implications are. If there are mistakes that people align a set of expectations, they know that sort of even from a risk management perspective, I've got a background in operational risk management is having some kind of checks and balance system in place to prevent this kind of thing from happening, and also communicating regularly to avoid these things happening, sort of going on for a long time without any kind of check.

 

Host: Paul Barnhurst:: Make sense. It is, basically, having a better check process and audit process to review these models.

 

Guest: Raphael Benhamou:: Exactly.

 

Host: Paul Barnhurst:: Can you next tell us a little bit about yourself, your background, what you're doing today and how you ended up where you're at.

 

Guest: Raphael Benhamou:: Going back a little bit. Got an undergrad in physics and languages as a master's in engineering and finance. And for me, that sort of molded the world that I see in terms of finance strategy and modeling role, because I learned very early on how to model the microscopic and then go out to the macroscopic and see how small things can impact big things. That actually, ironically, is the case study I just described. Sort of shows. So being able to understand things on a very detailed level and the engineering degree afterward helped a lot in understanding the operations and the mechanics of a business, and then combining that with the finance and seeing what the long-term impacts are molded the way that I think about the world and the way I think about modeling and business in general. I've gone I've worked in consulting basically my entire career, apart from two years or so industry, working in a sort of a fintech and then a mining project in fertilizers. But most of my career has been in boutique consulting companies, and then the E in the risk management, data analytics and regulation, compliance and robotic process automation teams. I went independent about four and a half years ago, just before COVID. I started off as well, actually. I started off as a sort of generalist startup business consultant, just helping with business planning, go-to-market fundraising, getting people into incubators, that kind of stuff, and then sort of migrating it from that into more FP&A consulting, helping out with financial modeling.

 

Guest: Raphael Benhamou:: So helping people with, again, their fundraising but modeling aspects. Modeling has been a big part of my career, but it just wasn't what I started off doing when I became independent because of connections. So helping out with modeling, with investment, with bespoke solutions like hiring, planning, optimizations around remuneration packages, all that kind of stuff, and eventually migrated again from that into fractional CFO services. So today I do a lot more fractional CFO services. I work on a retainer with a couple of clients. So essentially doing the modeling aspect and then the advisory and insights and strategy afterward, once the analysis has been done, or at least the first part of it's been because I never consider a model to be finished. It's always iterating and sort of being updated. So being able to derive valuable insights from the models that I've built and the fact that I've built them and understand the model on a very granular level means that I can understand all the moving parts and mechanics of a business, and I can say that's something we need to focus on. That's too high a level. Let's go and work out how to achieve that pragmatically, etc. So that's more or less where I am today.

 

Host: Paul Barnhurst:: Great. Appreciate the background. Just so our audience knows, one of the things we're going to be talking quite a bit about in this episode is Causal. It's one of the tools using that goes beyond Excel. So I just want to kind of let everybody know that's going to be a focus here.

 

Guest: Raphael Benhamou:: Should I give you a bit of context around my connection to Causal then?

 

Host: Paul Barnhurst:: Let's get to that in one minute. We'll get there. Give me a second. There are a couple of other questions I want to ask. But we're going to spend a lot of time on Causal. Raphael is going to share why he's using that tool, and how he uses it, so people can learn about some other tools. But before we get to that, in your background, there's a question I want to ask because I'm kind of curious. You mentioned how your background helped you, but you started with physics and languages in undergrad, what made you become interested in getting a metals and energy finance master's? That's quite a switch.

 

Guest: Raphael Benhamou:: This is a big switch. It was a very big adjustment at the time. I understood nothing about the mining industry or the energy industry at the time, apart from the general impact it had on society, I guess. So during my my physics degree, I did a year abroad in Switzerland as part of my language component, and I was sort of studying in French and I was working in as part of that, I had to work in a lab. I was supposed to do just what's called travel practice, which is just practical laboratory work. But the lab work was boring because of the stuff I'd done in my first year in the UK because there's got a different curriculum in mainland Europe. So I signed up to work in a PhD lab on high-temperature superconductors for a year which gave me a taste. And there was a specific goal for this, which was to rather than just doing general theoretical research, was to have an application which was to build maglev trains. So magnetically levitated trains for people who don't know about them basically reduce resistance and move fast and sort of reducing energy consumption. And it gave me a taste of the practical applications of physics. So once I finished my undergrad in physics, which happened to be in 2009 when there was no economic crash, I started looking at sort of masters in different, more engineering-related roles.

 

Guest: Raphael Benhamou:: So Masters, I applied to a couple of in the environmental space and in space, because those are two areas that had a lot of interest in from my undergrad in physics. Both of those were complete, and there's a backup I kind of applied to this metals and energy finance course, mostly because I thought it'd be interesting to learn about because I sort of knew about the applications. I talked to industry and to infrastructure around the world, and I thought that it had a lot of courses on engineering, accounting, finance, strategy, languages, and stuff like that. I thought it would be an interesting, well-rounded course to do, even if I didn't want to go into that specific industry later, I'd learn a lot from it, and it would give me a lot of skills for later. And it turned out it did as it turns out, I ended up loving the mining industry. I thought it was an interesting one. And another sidebar on that is that I got a very, very passionate about environmental issues, food security, and stuff like that, and now less so, but especially when I was studying like 80% of the world's energy comes from conventional energy sources.

 

Guest: Raphael Benhamou:: I can make a small difference on the renewable side, and it won't make a difference. But if I can make even a smaller change on the conventional energy side or the mining side, it could have a much larger impact in my lifetime. So I thought, okay, it's it's a way to potentially help the environment, help the world later on from the other side. So that was kind of my motivation. It turns out that I enjoyed the mining industry as a sort of an area of professional interest. It's a very varied industry. Every mine site and every geological site is different. So, there are lots of different problems and challenges that you have to resolve. There are a lot of sort of more current issues around sort of a skilled workforce, environmental impact, renewing, using renewable energy, smart technology, and having automated machinery. So there's lots of moving parts to it that are really, interesting and sort of having the opportunity to work on sort of a mining project that was going to produce 20% of the world's fertilizer and feed, essentially feed the world, have a material difference in feeding the world is pretty exciting to do. So there are some definite upsides to working in a less-than-popular industry.

 

Host: Paul Barnhurst:: I can see giving it a lot of thought. You enjoyed it. It sounds like it's had a lot of variation. One other question. I'm curious. I know you've studied in Europe, you've lived in Europe, you've lived in Israel. I know you speak a number of languages. You studied languages. How many languages do you speak?

 

Guest: Raphael Benhamou:: Fluently, sadly, only two now, French and English. I used to be fluent in Spanish, but lack of practice has meant that I can speak it. But I have a block in my brain. Sometimes Spanish and Hebrew come out of vice versa. I used to think that I was very skilled at languages because I picked up all the romantic languages very easily Italian, Portuguese, French, Spanish, and so on. But once I moved here and started learning Hebrew, I realized that I'm very good at picking up languages that are connected to each other. But once you take a language that's completely different, like Hebrew, it all falls apart. So I'm fluent in French and English. I'm more than passable in Spanish, I understand Italian, I understand Portuguese, and I get by in Hebrew, so.

 

Host: Paul Barnhurst:: Got it. So there's a number in there that you can at least carry on a basic conversation.

 

Guest: Raphael Benhamou:: Yes, I'd say, 4 or 5, I can have a basic conversation.

 

Host: Paul Barnhurst:: That's more than I can do. So good for you. It sounds like you've spent a lot of your career building models. What attracted you to building models? What is it you enjoy about putting a model?

 

Guest: Raphael Benhamou:: I always like puzzles. I always like strategy. I was very much into sort of, well, risk. Yes. More chess when I was younger thinking five steps ahead. My father, and my grandfather both taught me chess when I was about four years old. So it's been something some of a family activity for years. I like. it comes back to physics, breaking things down into their core parts and then building back up again an understanding of what the overall behavior trends are and how something a small change here can have a massive impact. It talks to sort of physics and the mechanical level, but also chaos theory, and stuff like that. So how a tiny little change here that you intellectually don't think will have a connection to something over there? But maybe unconsciously there's a connection, but you don't make the mental leap because there are so many different moving parts along the way, and building the models to understand that mathematically creates logic to decouple that and break it down into component parts. There are things that I find interesting. And then applying that to business is where I went from enjoying it from a physics perspective to enjoying it from a sort of finance strategy perspective. I like application rather than theory. I like application more than theory. I should say I was. I love physics, but I prefer using it in real-world environments.

 

Host: Paul Barnhurst:: No, I get it. You like to see the real-world application. That makes a lot of sense. The idea of being able to break it down, build it back up, and understand the different parts. There's a correlation between modeling and physics in that sense of how you're breaking things down. I know you started modeling obviously with Microsoft Excel.

 

Guest: Raphael Benhamou:: A little bit. But not really.

 

Host: Paul Barnhurst:: What did you start with? I know it wasn't Causal.

 

Guest: Raphael Benhamou:: No. Of course, it wasn't Causal. Ironically, it was something very similar to Causal, which is how I ended up enjoying Causal. So much. So my first job, I was working in a boutique consulting company. There were two, basically two directors and me. I was the first guy or the first consulting guy. They had an HR person, but they were essentially building solutions in software for the mining industry developed by mining as a mining software tool in South Africa. The director had a connection to the founder of that company. They'd worked together on a previous project or something like that, can't remember. And so I started my professional career sort of working obviously in Excel, Excel, but also in parallel on this other software that's called Coronet. And it's kind of like Causal today, but more programming in the language. it's more the language is more akin to Matlab, which I used extensively, obviously during my physics degree. So it was very familiar to me, and it had a lot of structure of it was very similar to Causal. Today it's object-orientated rather than cell references. You create a single variable and then you create dimensions in it rather than categories. The naming nomenclature is slightly different. You can create scenarios very easily. They had a sort of integrated dashboard. And that's what we are using in my first job. I used that every day for two and a half years while I was in that first job, and I felt like it really, massively influenced the way that I then later started building models in Excel and Google Sheets, because I started thinking a lot more from an object variable perspective, rather than a line nine line perspective that a lot of people use Excel in.

 

Guest: Raphael Benhamou:: It helped me to structure models a lot better because you can create hierarchies in the model, you can create templates and you can say, okay, this is I'm going to think about building a general template that I can use, like with the first project I mentioned two, 300 times. So creating sort of logic that is easily repeatable and stuff like that. And it helped me to think about how I can do that from an Excel perspective as well. So creating Templatized logic is very easy, using dollar signs to sort of block things and copy things down easily, stuff that seems obvious to a sort of a competent Excel user, but to a beginner not so much. So it gave me a great grounding in best I feel best practices for modeling across all modeling platforms, especially Excel, because one of my big nitpicks with Excel and Google Sheets is that it's too freeform. It's great if you're competent, a good user, but if you're a bad user of Excel and you don't know you're a bad user of Excel, you can make lots of mistakes really, easily. And a lot of the software that I've ended up using in a sort of sense kind of take Excel and put limits around it to prevent that kind of behavior from happening, which is one of the reasons I think that they're useful because they enable people who are not so sort of competent. If they're not using them daily, it allows them to use them and still get insights, which is fundamentally the important point of modeling.

 

Host: Paul Barnhurst:: So there's a lot to unpack there. So that's helpful.

 

Guest: Raphael Benhamou:: It's a very long answer.

 

Host: Paul Barnhurst:: Started with mining for a couple of years using mining software. You used Matlab in college, you had that programming background. You came from the object kind of variable approach versus the freeform spreadsheet nature. One thing you said there that I like is Excel almost two Freeform. It's the most flexible tool out there. I get asked all the time well will my planning tool be as flexible as Excel. And I always say no. You're trading off some flexibility for structure. That's not a bad thing. You just have to recognize that because if you're talking to large planning tools, you need the database, you need all those other things. And it sounds like many of the planning tools you've used have traded off some of that flexibility for the structure and rules to make it easier to understand and make it less likely to make mistakes. That has worked better for you. Is that kind of a fair way to kind of think about that?

 

Guest: Raphael Benhamou:: Yes, and it's not necessarily because I'm making the mistake so much as the people that I'm working with mistakes. After all, I'm a consultant. I've worked as a consultant my entire career. So my daily activity is seeing other people's bad models. I know that most people don't spend their days working in models in Excel or Google Sheets, and so any tool that can enable them to still have the insights and the ability to carry out the analysis that they need to do to be able to make informed decisions, but reduces the risk of having of creating inserting mistakes into them is a win for me, because at the end of the day, as long as you're not trading on core functionality that allows you to do the analysis, limiting your freedom is not a bad thing. I think in that respect, and I'm talking just about modeling, by the way. I'm not talking about society.

 

Host: Paul Barnhurst:: We won't go there. We'll stay away. Yeah. So I know about four years ago you came across Causal. Were you looking for a tool to kind of replace Excel as you started your business or walk through how that happened, how you came across Causal, and maybe a little bit of why you switched to using them? I believe you use them. What about 95% of the time now. Is that?

 

Guest: Raphael Benhamou:: Yes, if not more.

 

Host: Paul Barnhurst:: Walk through that journey.

 

Guest: Raphael Benhamou:: It's very spontaneous. So I became independent just before COVID. I sort of signed on for a contract with a startup that needed help on the basis of essentially business consulting, and business analysis perspective. And when COVID happened, they dropped me because they're in some crisis in a crisis and worrying about sort of cash and all that kind of stuff and all my leads obviously, for. So everything that I was in my pipeline disappeared as well. So I spent about six, or seven months just trying to reinvent myself and work out how I was going to communicate myself in an online world to the world about what my services were. And I struggled with it because I've got a very wide range of experiences. I've worked in everything from risk management to business optimization to M&A and everything in between. So I couldn't just say I'm a business analyst or business consultant. No one understood what that meant. So I was having these calls with people. I was on this platform called, well, I am still on this platform called Lunch Club, and I was meeting people from all around the world because it went from in-person meetings to online because of COVID. I was connected with a guy in an investment company in the US, and I was talking about my background in modeling and strategy and finance, and he told me he just heard of this software that someone that you knew had invested in or started using. I can't remember exactly what you said and that I should try it out. I was like, okay, sounds interesting.

 

Guest: Raphael Benhamou:: Why not? I've got nothing better to do at the moment. I didn't have any clients, so I checked it out. Happened to be Causal. I tried out the free trial for a week within about two hours. I was getting comfortable with it because it was the same, but a more user-friendly version of what I used at the beginning of my career. Current. I started building out as it happened. I was taking a mortgage brokering course. I built my mortgage model on there as well, just for a bit of fun, just to see what I could do with it. I love the platform. Like quickly. I could see the potential of it. I could see where it was going, the sort of functionality it had already, and where they could potentially go with it in terms of added functionality capabilities because I'd seen this other software 15 years earlier that was very similar, and I'd sort of been part of their development process. So a little bit of background on that is they were constantly iterating the software, and when we were pushing the bar with some of our clients, we would go back to them and say, hey, can you add this functionality? Can you put this and can you do that? And so I'd seen I'd been part of that product development process, that company. And so I could see where Causal could go potentially because I could see how they'd structured the software. So I reached out to Lucas, who's one of the co-founders. At the time, it was just Lucas Moura and his brother who were working at what time say they were,

 

Guest: Raphael Benhamou:: I think, still bootstrapped at that point, just the three of them. I reached out, and I said, look, I love your product. I think it's great. I see the potential. I've got a background in consulting, financial modeling, and so on. There's anything that we can do to help each other from a business perspective. And he said, look, we're a SaaS platform where 95% of our clients sign up and then say, can someone help us build our models because we don't have the time or resources or expertise to build stuff? So if we don't have the time to do it because we're building out the product and we also don't have the expertise because we're software developers more than anything else. So we're in this more product. But if you want to take that on for us, great. So that was the start of a beautiful relationship with them. They would get people, they would do their marketing, they'd sign up clients. And then when someone asked, can I get some help with that? They then send it to me. Eventually, they even sort of started saying to Raphael would take the first call with him and then sell them on Causal. So I was also sort of showing them the added value of Causal as well as selling them my consulting services. But that's how it kicked off. And it went on like that for probably about 18 to 24 months, maybe before they got their first fundraise, and then after they started hiring people internally to do custom onboarding, and I started focusing more on my existing clients, and people would come back to me for, for repeat work and do my marketing. But in the beginning, it was great because that 1520 new clients a month without ever having to do any marketing, it's fantastic.

 

Host: Paul Barnhurst:: So it sounds like it kind of was born out of COVID and trying to find something and having free time leading to an opportunity to build.

 

Guest: Raphael Benhamou:: There's a lot of grafting, there's a lot of sort of networking calls and sort of there's a lot of stuff that I tried before something happened with cause I even. I was speaking to contacts in Australian mining companies and South America and all kinds of different things, just trying to see if anything could work. And because it was COVID, nothing was working because everyone was panicking. So it was sort of a serendipitous moment and it worked out. I think it did very well for both of us as a very symbiotic relationship because they needed someone to onboard clients at that point otherwise I have a feeling that a lot of people would have signed up and then lost interest within a week because they wouldn't have been able to use it. So it helped them to lock in their early clients. So that helped me obviously to build up my consulting practice. Yeah.

 

Host: Paul Barnhurst:: So let's talk a little bit about Causal. I want to spend maybe about ten minutes kind of diving a little further into that. So first question is what kind of models are you primarily building in Causal or are they three statement M&A operational a little bit of everything?

 

Guest: Raphael Benhamou:: A little bit of everything. So as a fractional CFO, there's a three-segment model that is core to everything. But because I've got a background in engineering and operations and so consulting from a consulting perspective, a lot of operations, I've always fundamentally believed that a business model that doesn't include operations isn't an effective tool because if you try to build everything from essentially the revenue line on your PNL and you don't have anything going on in the background, that's saying, okay, how are you driving revenue? How are your costs driving revenue? How is marketing expense driving your revenues, all that kind of stuff, or hiring decision-making? You're essentially fighting with one hand behind your back. So I've always even before, as a fractional CFO, long ago, I processed my first job. To be honest, I've always built models from operations and finance. So breaking down a model into what the revenue drivers are, how the expenses work, what your working capital is, what your HR planning is, and driving the three-segment model through with that. Then afterward add on valuation models. Sometimes a problem, sometimes with being multilingual is you have a blank insert in a language, but have a cap table in there. So having that how does that affect the balance sheet?

 

Guest: Raphael Benhamou:: So I try to build a unified business model, if you will, and add granularity where it's relevant, and juggling the return on investment and building the building with the insights and advice that it's going to give. So sometimes it's just not relevant. So one of my old clients had just come back to me yesterday and he asked me to build out a high-level model that he wants to go to investors with. I have to then clarify, okay, what do you define by high level? Because and he says, I want to integrate Xero into it. So it's already more complicated than he probably imagined. So there are always different levels of granularity. And sometimes you just need a three-segment model. Sometimes you want three segments plus HR, sometimes you want three segments plus HR plus revenue. Sometimes you want a separate marketing model, a sales, a salesperson model. So it depends. But generally, I try to go for as much as is useful insightful, and relevant to the client because, at the end of the day, a model is only as good as the insights it's giving, and if it's only giving high-level insights that don't drive accurate, don't give accurate advice, then there's no point.

 

Host: Paul Barnhurst:: In today's business world, financial modeling skills are more important than ever with financial modeling institutes. Advanced financial Modeler accreditation program you can be. Recognized as an expert in the field by validating your financial modeling skills. Join the Financial Modeling Institute's community of top financial modelers, gain access to extensive learning resources, and attain the prestigious Advanced Financial Modeler accreditation. Visit www.fminstitute.com/podcast and use Code PODCAST to save 15% when you register. So the next question I want to ask is with the models you're building, sounds like it's across the spectrum, a little bit of everything. What's maybe what do you like most about doing it in Causal like what's the main reason you're using Causal versus Excel?

 

Guest: Raphael Benhamou:: There are a couple of different aspects. As a consultant, again, I need to juggle my preferences as a professional model builder with my activities as an advisor in terms of strategy and then in terms of the client relationship, and how the client is going to onboard it. So one of the big challenges with any model is, whether the client going to use it. Are the team leaders if I'm talking to the CEO, the decision maker, and they're saying we want to build this model, we want to develop KPIs, OKRs, etc. for the team if the team doesn't want to onboard them, and accept them. Then again, there's no point in the project. So all of that is being juggled in terms of the decision of what platform to use. Sometimes they're open to using a new software like Causal, sometimes they're not. Sometimes you have to create a hybrid solution. In terms of what I like and Causal, I would say that from my perspective as a model builder, I love the fact that it's the language is easy. So rather than using cell references, use the names of the variables, which makes it much easier for me to audit and sense check things, but also easier for me to explain things to people because they can. I can show the model, I can show them the equations. I can say, no, that doesn't quite seem right. I think you've missed out on part of the expenses or whatever. Or you've captured everything that needs to be done. Then there's the aspect of how it communicates to the client and how the client is going to use it. In terms of the fact that Causal integrates with a lot of different systems. So I can use it to connect it to QuickBooks to Xero. Obviously, from a bookkeeping and accounting perspective, I can connect it to a lot of HR systems.

 

Guest: Raphael Benhamou:: So bamboo and a whole load of other CRMs, all that kind of stuff. So you can easily get connections to other software and get that live information to be able to show value in terms of comparing budgets versus actuals and all those kinds of insights. You've got dynamic dashboarding, which is, I think, great. you can do some things with Excel, but it's a lot easier and faster, and it saves a lot of time for the client in terms of chargeable hours. When I can create one chart instantly, that can simultaneously allow me to see things on a monthly, quarterly, and annual level while also seeing whether it's the delta change between previous months, previous quarters, previous years, and filtering out between the different categories and filtering out different things on the bottom, or singing aggregate version and one story, one chart in Excel. One table in Excel can do the work of an entire dashboard on Excel. And I think that that just from a time perspective is amazing. But then showing that to a client and seeing how they can then see things that even I didn't think about because they come from a slightly different perspective is really, exciting. Yeah, just there's a lot of functionality in it that's useful. Like I come from also a risk background. So a Causal is, as far as I know, the only easy, accessible career that does it as well. But there is an enterprise tool is the only tool that does probability distribution modeling, really, well. So in Excel, you can do it basically with glass. What's it called? glass ball or something?

 

Host: Paul Barnhurst:: Ball or whatever.

 

Guest: Raphael Benhamou:: Crystal ball. So crystal ball I don't know if they've developed it in the last couple of years, but it used to be that you could do one input to one output. So you are just seeing the range of the distribution rather than the behavior because the behavior is going to be the same every time. In Causal, you can build uncertainties and probability distributions on any input assumption that you want, and then build out your logic with some of your max, your min, your distributions, your sort of buckets, or whatever you want, and then see what the concatenated effect is on your outputs. And that from a risk perspective, from being someone who has been a strong proponent of quantitative risk analysis for the better part of a decade is gold. you can't get any better than that. And just on that perspective, just explaining that to clients, being able to show them that just the way they change their terms with clients, whether they get paid in 30 days to 90 days, or whether it takes them two months or six months or nine months to close a client and their pipeline, and how that can impact their cash flow and all those kinds of questions. But using risk and showing, okay, this is where you risk going under $0 in your bank account and so on is really, important and really, insightful. Again, providing those kinds of insights and value is what I think a model should be about, rather than the tool being the tool for the sake of the tool.

 

Host: Paul Barnhurst:: A couple of things I want to kind of recap there. If I heard it right first, you like the fact that the variables. You're referencing variables, not cells. It's not the traditional 2D spreadsheet. This is a multi-dimensional modeling tool where you can have multiple variables listed.

 

Guest: Raphael Benhamou:: So you say rather than saying C56 times Vlookup, whatever you're saying, for example, revenue is production times price. You're writing the name of the variable every time, correct?

 

Host: Paul Barnhurst:: Yeah. You're doing natural language variables. Like if you use the table in Excel, you're referencing those column names similar to a variable versus referencing C2 times B2. Yeah, totally. Get it. That's a common thing you'll see with a lot of planning tools. Sure is. They try to use those variables because it is more natural if you're learning from the beginning. Most people are used to B2 times C2, but if you're teaching somebody revenue, times quantity makes a lot more sense than B2 times C2. Exactly. So natural language referencing the name of the variable is one thing to the integration, the way it easily brings in different data sources. And then it sounds like one you appreciate is risk modeling. Yeah. Data of the probability distribution. There are some things you can do there easily that you can't do with other tools. And then it's dashboarding. Dynamic dashboarding is a kind of summary of some of the key things you like about modeling.

 

Guest: Raphael Benhamou:: Yes, I'd say some of the other ones that are useful are things like scalability. So using categories or dimensions you can very quickly build something that is single-dimensional. And then afterward add products, add currencies, add scenarios instantly, and the whole. And unlike Excel where you'd have to go and manually add it to every line, you can add it just at the input, and then Causal automatically feeds it through the whole model. And that is again, it's all about saving time and making it easier for people to use things and scale models easily. When a business inevitably evolves and changes over time. The sidebar on that is, that Causal does cohort analysis well. So you can build in there's a unique cohort category in Causal, which you can build in cohort analysis for any kind of subscription SaaS model or anything like that. And I've developed my own, ironically, based on my first job, my formula that a single formula that controls the entire cohort so it can save me hours of work just with that one formula.

 

Host: Paul Barnhurst:: Oh, great. I know cohort analysis is a good example. And yes, that ability to drop in a new variable and not have to sit there and update the entire model to get it to me. I've seen that a lot with different, different tools to take a similar approach to Causal. Sure. What's your least favorite thing about the tool?

 

Guest: Raphael Benhamou:: I would say it's probably the communication, the interface, human to model in the sense that it comes back to what I was saying earlier about sort of onboarding challenges. So, the decision maker in the business might be interested in using Causal. The rest of the team does want to use it. Then you have to work on some kind of hybrid solution, which is a problem. But then another factor of that is when you're talking to investors, for example, and they want to see something in Excel because that's all they're used to. And so you have to then export it and you lose all, all the added value of having built it in Causal. So that can be very, very frustrating because it's more about people's prejudices or biases or comfort zones, which sort of irritates me. It's not so much Causal, but it's so different in many respects from what other people do. You have to work around it sometimes to get to something that people want to see, and it's something that causes try to get closer to it. So it used to be that Causal model with cards. So you'd see just like a card for the variable, which would just show the data for the current time period that you're in.

 

Guest: Raphael Benhamou:: And you click on the card, you write the formula, and then you close the card and you just see sort of essentially a tree diagram, almost of cards. And they moved from that to a spreadsheet view because people said we're more comfortable with spreadsheets. And I was a big railer against them doing this because I found the card view to be much more intuitive for building things and moving things around, and understanding how the architecture worked. But again, it's what people are comfortable with. So it's it's that fitting the product to what clients need. I just wish that people were a bit more open to change because people are so ingrained, so used to the cell spreadsheet mentality of Excel and Google Sheets that they find it difficult to move away from it. And I find that frustrating sometimes because it's fundamentally Excel that was built as a database solution. It wasn't ever meant to be a modeling solution when it was designed. So it's been adapted to that because there was nothing else better in the market. But if there are better tools in the market. So it.

 

Host: Paul Barnhurst:: Sounds like you wish people would be more it's not so much a reflection of the tool of what you dislike, but the challenges that come from using it and how they got it.

 

Guest: Raphael Benhamou:: Because fundamentally, I think the tool is amazing. it had problems in the past. Any software does when you do updates and there are bugs and problems and stuff like that. But that's part of the, the sort of the startup's journey. So I'm not going to critique it on that because every software has that. Excel, how old is it? Like 40, 50 years old now? I break Excel on a semi-regular basis. It's normal that software breaks down or has bugs and stuff like that. But fundamentally the tool is, for me, leaps ahead of where most platforms are in terms of modeling.

 

Host: Paul Barnhurst:: So if somebody wants to start learning Causal building models they listen to this and they're interested, how would you recommend they start?

 

Guest: Raphael Benhamou:: So you can sign up for a free trial. There's a Causal University on the website. So once you've got a model you can go into the documentation. The Causal team is fantastic at creating short-form videos to explain how different functions work. There's a lot of documentation as well, so you can look at that. They also got a lot of templates so you can download it or not even download it, just apply it to your account. you can get one of the templates, and start playing around with it. A lot of them are built for businesses and a lot of it is automated now. So there's a fantastic integration with Stripe now where you connect your Stripe account. And with AI, it builds out a stripe analytics tool for you without you even having to do anything. You just sit there and wait for two minutes and it's done. And then you can go and investigate the logic. You can have a look at how it's built, the formulae, how it's done, the default charts, and stuff like that and play around with it. So there's a lot of functionality that is sort of already built in. It's built. This is what was going back to is that they've moved away from software that people need a lot of work to learn how to use or get an external consultant to use, to try to make it as easy as possible for people to onboard on. And so they've made huge steps and efforts in this in regards to there's a lot of documentation, a lot of material out there to learn how to use it.

 

Host: Paul Barnhurst:: So it sounds like sign up for a free trial. Just start giving it a try.

 

Guest: Raphael Benhamou:: Like any SaaS product.

 

Host: Paul Barnhurst:: Well, like any tool Excel or whatever. Started and start learning. Best way to figure it out.

 

Guest: Raphael Benhamou:: Generally my recommendation for people is and this is the way I did it as well is when I started using it is I just think, okay, if I was to have a fictitious business or if I've already got a business and I just want to sort of start at the top down and say, okay, let's build it very simplistically. Let's just say, okay, how do I generate revenue? Okay, I need an assumption about price. I need an assumption about production. Okay. What am I producing? What are the products. So dig deeper and deeper in that way. And the more you build out your thought process and put paper to pen, metaphorically speaking, the more you'll hit roadblocks in your learning that you'll then have to learn how to do things to do it. And that's the way. Well, at least that's the way I learned. I think this a very effective way of doing it, because then you're practicing what you're doing rather than just learning. Theoretically, if you just read a document, it'll go in, or at least just go in one ear and out the other. I need to practice it quickly.

 

Host: Paul Barnhurst:: What we're going to do here now is we're going to move to the Rapid Fire section. I'm going to run through the questions twice, I think is what we're first time. I want you to answer them for Excel. A few of them we won't ask the second time because some of them sure can apply across both. Some are strictly to excel. So I'm going to ask all the questions related to. If you're modeling in Excel traditional spreadsheet, then I'm going to ask you to give an answer for Causal. The goal here on each of these is on the Excel side, just a couple seconds. It has to be one or the other. Not it depends I know it can it depend for everything. Same with the second. And then at the end we'll let you elaborate on a few of those that may be different or that you're passionate about. So just to kind of keep it a little bit more of the Rapid Fire, first time we've done this where we're going through two different lists. So in my head, what's the best way to do this? We'll see how this works. So think traditional spreadsheets here. Circular or no circular references?

 

Guest: Raphael Benhamou:: No circular references.

 

Host: Paul Barnhurst:: VBA or no VBA?

 

Guest: Raphael Benhamou:: Sometimes yes.

 

Host: Paul Barnhurst:: We'll go with the yes, I get it. Excel's dynamic arrays in your models. Yes or no?

 

Guest: Raphael Benhamou:: Not so much.

 

Host: Paul Barnhurst:: Workbook links, external workbook links?

 

Guest: Raphael Benhamou:: Definitely not.

 

Host: Paul Barnhurst:: Yes, that's usually what people say. It's like no, no, never. Named ranges. Yes or no?

 

Guest: Raphael Benhamou:: Selectively, I'd say.

 

Host: Paul Barnhurst:: Usually everybody hesitates on that one. They're like, please don't go crazy.

 

Guest: Raphael Benhamou:: Exactly.

 

Host: Paul Barnhurst:: Do you follow a formal standards board for modeling like fast or smart or any of those?

 

Guest: Raphael Benhamou:: Not really. I've learned from my own experience.

 

Host: Paul Barnhurst:: And do you think Excel will ever die?

 

Guest: Raphael Benhamou:: Unfortunately, no. Kidding.

 

Host: Paul Barnhurst:: Do you think AI will build the models for us?

 

Guest: Raphael Benhamou:: This is an interesting question. I think it can. The question is whether it should.

 

Host: Paul Barnhurst:: Interesting. Yeah, there's definitely a question that. So you think it's capable there's a broader question of should we allow it to do it?

 

Guest: Raphael Benhamou:: Like all tools. So there's the tool can do something. And whether we should be using it for that is another question entirely understood.

 

Host: Paul Barnhurst:: So sheet cell protection, should you be using it in your models.

 

Guest: Raphael Benhamou:: Depends on the client. Generally I prefer not but it depends on the client.

 

Host: Paul Barnhurst:: We'll say generally no but there is use cases. Got it. All right. Do you believe financial models are the number one corporate decision making tool?

 

Guest: Raphael Benhamou:: I don't think they currently are. It depends on the business. I'd say, they're not currently, but they should be.

 

Host: Paul Barnhurst:: Fair enough. What do you think is then what do you think most often ends up being?

 

Guest: Raphael Benhamou:: I think it's less about a physical thing and more about politics, conversations, relationships between people. I think unfortunately we're not in a as much a data driven world as we should be yet.

 

Host: Paul Barnhurst:: Fair enough. So then what is your lookup function of choice? Do you prefer Choose, vlookup, index, match or Xlookup or something else?

 

Guest: Raphael Benhamou:: Index Match. For me, it's the most versatile, but I know that there are a couple of others that are more versatile now. But I'm used to it and I like it, and I don't see the point in changing something that I know works very, very well.

 

Host: Paul Barnhurst:: Yes, it's still incredibly flexible. I'm starting to use Index XMatch because XMatch gives you a few more options than match.

 

Guest: Raphael Benhamou:: Yes.

 

Host: Paul Barnhurst:: All right. We're going to run through that list. Obviously not all of them apply, but we're going to ask some of them. And you can just say hey doesn't apply at all or whatever we talk about at the end. So this is for Causal circular or no circular references?

 

Guest: Raphael Benhamou:: No, no.

 

Host: Paul Barnhurst:: VBA, I'm guessing doesn't apply.

 

Guest: Raphael Benhamou:: Doesn't apply.

 

Host: Paul Barnhurst:: This one I forgot to ask for Excel. Horizontal or vertical model? Let's go Excel. Horizontal or vertical. Let me explain what I mean. When I say horizontal, I'm talking multiple sheets. Vertical is kind of stacking it all. Sometimes people think I mean the time dimension being vertical.

 

Guest: Raphael Benhamou:: So in Excel I'd say horizontal in Causal. Up until now I've only used horizontal because and there's reasons for this which are more about functionality and sharing and stuff like that. Causal has recently developed a new functionality that allows vertical modeling or much more effective vertical modeling, which I've only tried out once so far, and it's so far it's been great. But I don't have enough of an opinion either way at the moment.it  to be honest, both of them work more or less the same way in Excel in Causal because you because it's object orientated, you create objects for blocks of logic either way. So whether it's across models or within sections of the same model, it doesn't make a difference.

 

Host: Paul Barnhurst:: Because of the object nature. I'm assuming dynamic arrays don't apply here.

 

Guest: Raphael Benhamou:: I mean, they're automatic in a sense. You create a variable and you've dynamically got everything inside it. And you either refer sort of like to like or a section of it or an aggregate or whatever. So it's almost like they're naturally.

 

Host: Paul Barnhurst:: Named ranges. You're naming the variables.

 

Guest: Raphael Benhamou:: It's not really. It's again it's automatic.

 

Host: Paul Barnhurst:: Again, I don't know the the formal standards have generally been designed for a spreadsheet so they wouldn't apply.

 

Guest: Raphael Benhamou:: But again as I was saying earlier on in the conversation, the way that I've learned how to build models has been heavily influenced by object variable structures from the current. When I was when I was in my first job. So I'd say I've learned how to build models even in Excel using an object attribute approach if that makes sense.

 

Host: Paul Barnhurst:: No, I get what you're saying. And then you don't have lookup functions.

 

Guest: Raphael Benhamou:: No. Again, redundant. It's automatic as part of the software.

 

Host: Paul Barnhurst:: Way of having the database and everything and the way it works.

 

Guest: Raphael Benhamou:: Yes.

 

Host: Paul Barnhurst:: All right. So I think that covered our Rapid Fire section. That was kind of a unique hybrid there. So hopefully people enjoy that. One last question I'll ask kind of for this section. And then we're just going to wrap up. What's your favorite shortcut in Excel?

 

Guest: Raphael Benhamou:: Let's say, f4.

 

Host: Paul Barnhurst:: That's high on my list. Definitely top ten for everybody for sure. Alrighty. So if you could offer one piece of advice to our audience to be a better financial modeler, what would that advice be?

 

Guest: Raphael Benhamou:: One is difficult, I would say. So my perspective on this is what I was saying earlier about operate connecting to finance. I think to be a good financial modeler, you need to have a good understanding of what what's going on in the business as well, not just at the finance level. If you want to be able to provide insights, be able to provide strategic insights and value forecasts that make sense. You have to be able to connect it effectively and accurately to your model, so to your operations. So to give an example I've seen, I can't even tell you how many times I've seen models where you've got the first line is revenue. And it says okay, the revenue in the first month is X, and we're going to grow revenue by Y percent every month. And that's going that's how we're building our model. And we're going to reach this goal at the end. That doesn't tell the story, because if you then take that as a decision maker and say to your team, okay, we need to increase revenues by month and month by this amount over the next couple years to reach our goal of this revenue by the end of the year or this profit by the end of the year, they're going to say, okay, how do we do that? So if you can give them some insights about this by building an operational model that says, okay, we're going to increase revenues by focusing on this, this one product line that's got a higher ROI than anything else, or we're going to increase our marketing spend to drive it, or we're going to optimize our marketing spend, because at the moment our business is seasonal.

 

Guest: Raphael Benhamou:: We'll be spreading money to much across the low seasons or anything like that. We're going to hire a new salesperson. We're going to be a bit more lean in this respect. We're going to push out this product three months earlier, whatever that is. That's what should be driving your revenue growth. And the more you do that, the more you build interrelations between what's on your your three segment model with the rest of the model, the more you're building a tool that will help you connect strategic insights to operational goals, which will, at the end of the day, help the businesses because you'll be able to say, okay, my goal is to reach 10 million an hour by December 2025. What do I literally have to do to get there? Rather than just saying, that's my goal, you go out and work out how to do it. And that, from a management perspective, also speaks volumes because it means that you're interested in business and you're not just sort of top down telling people what to do and not caring about how they go about doing it.

 

Guest: Raphael Benhamou:: Again, it's that interaction between the business model, the values that's providing and the management and the use of it in business is really, important. And I think that to be a good and effective business modeler, that you need to internalize that and understand that you can be great at auditing, you can check all the numbers. You can be an expert at the financial statement and creating your three statement models. But if you don't know how to use that to derive insights and give advice, or to give people the information that they need to make the decisions, it's just going to be a tool on the shelf that's never going to be used. It's going to be used once when you built it, and afterwards it's going to be forgotten. And I think the model should be a living, breathing part of a business in the decision making space. They should be reviewed on a regular basis. They need to be checked and sort of see what the KPIs are and everything. And obviously that's what the ideal is for every business. But most businesses don't do that. So I think that that aspect of it, the softer side of it is very important and it's often overlooked side.

 

Host: Paul Barnhurst:: So understand the operations of the business. I'll boil that down.

 

Guest: Raphael Benhamou:: I prefer to give context because otherwise it's very like tagline and not giving any insights.

 

Host: Paul Barnhurst:: I get what you're saying. You gave some great examples there and just I fully agree. I hear it all the time for FP&A professionals. What's the number one thing it's like learn the business, don't spend all day in a spreadsheet that applies to a model as well. So great advice. So if our audience wants to learn more about you or what you do, the services you offer, what's the best way for them to learn about you so.

 

Guest: Raphael Benhamou:: They can look me up on my LinkedIn, Raphael Benhamou. I've also got my website benhamouconsulting.com. Also on Instagram, fractional CFO, I think. I need to check that one. I'll send you that one afterwards. I'm not active on them. To be honest. I did it as a bit of a lark. But mostly LinkedIn or email. You can email me at raphael@benhamouconsulting.com as well.

 

Host: Paul Barnhurst:: Well, thank you. Raphael Benhamou, I appreciate you joining us today and getting the opportunity to chat with you. And we'll put that information in the show notes. And if you want to share anything around Causal, feel free to send anything you want me to put in the show notes. We'll definitely put a link to the to the software as well.

 

Guest: Raphael Benhamou:: I thought about this actually. So on my website, so Causal, you can embed models onto your, onto websites. So I've put two demos of course or templates of Causal on my website. So if you go on my website there's a section called demos. I think you can go in and have a look. And there's two models on a sort of a subscription based modeling bit for subscription based modeling and also for sort of CRM, sort of big client contract kind of or transactional modeling approach as well. So you can see what Causal looks like. You can play around with it. You can fiddle around with the assumptions and see how it impacts the dashboards, all without having to learn how to use Causal. So it's quite cool.

 

Host: Paul Barnhurst:: Great. Well, we'll definitely put a link out for that. I appreciate you joining us, and I hope our audience enjoyed this conversation as much as I did. I'm familiar with Causal. I've used a little bit, but it's always fun to see somebody who's using it all the time and and get their perspective. So thanks for joining us.

 

Guest: Raphael Benhamou:: It's my pleasure. Thanks for reaching out.

 

Host: Paul Barnhurst:: Financial Modeler's Corner was brought to you by Financial Modeling Institute. Visit FMI at www.fminstitute.com/podcast and use Code PODCAST to save 15% when you enroll in one of their accreditations today.

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