Financial Modeler's Corner

In this episode of Financial Modeler’s Corner, host Paul Barnhurst sits down with Karishma Ramnawaj, a financial modeler based in Mauritius, to talk about her journey in financial modelling, building and reviewing models, and the lessons she’s learned from both success and failure. Karishma shares her experience of learning on the job, why understanding the end user is critical, and how she balances practical standards with flexibility.Karishma is a Certified Advanced Financial Modeler (AFM) and FMVA® professional, currently working as a Financial Modeler Associate at Hawkins Eberdal Ltd in Mauritius. With a strong foundation in both project and corporate finance, Karishma specializes in building decision-ready financial models that support capital raising, risk evaluation, and business growth.Expect to LearnWhy using someone else’s model as a template can be riskyThe importance of understanding and communicating key assumptionsHow to tailor models for investors and third-party usersWhat it’s like to fail, and then pass, the AFM examThe value of applying both corporate and project finance in modellingHere are a few quotes from the episode:“If you're going to use someone else's model, make sure you understand everything inside it.” – Karishma“It's not just about Excel. It's about who's using the model and what story you're telling with it.” – KarishmaKarishma's story is a great example of growth through practice, persistence, and passion for financial modeling. Her focus on clarity, flexibility, and end-user needs brings valuable perspective to the modelling process. From overcoming early challenges to passing the AFM exam, she shows the importance of continuous improvement.Follow Karishma:LinkedIn: https://www.linkedin.com/in/karishma-ramnawaj/Follow Financial Modeler's Corner: LinkedIn Page- https://www.linkedin.com/company/financial-modeler-s-corner/Newsletter - Subscribe on LinkedIn -https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7079020077076905984Sign up for the Advanced Financial Modeler Accreditation Today and receive 15% off by using the special show code ‘Podcast’.Visit https://bit.ly/497oAqW and use the code “Podcast” to save 15% when you register.In today’s episode:[01:56] - Guest Intro[04:51] - Journey into Modelling[06:00] - Why She Loves Modelling[08:37] - Storytelling with Numbers[10:56] - Key Assumptions & End Users[13:00] - Project vs. Corporate Finance[14:26] - Renewable Energy Focus[15:44] - Modelling Standards & Reviews[22:16] - AFM Exam: Fail to Pass[29:18] - Tools, Tips & Final Advice

In this episode of The ModSquad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male welcome Tim Jacks, founder of Taglo, for an insightful discussion on the integration of AI in financial modeling. Tim’s expertise bridges the worlds of financial modeling and AI, and in this episode, he shares his journey and discusses how AI is reshaping the financial modeling landscape.Tim Jacks is the founder of Taglo, a company dedicated to improving financial modeling with AI technology. His career journey spans financial consulting and software development, including building financial modeling tools. Over time, Tim's interest in artificial intelligence grew, and he delved into how AI, particularly Large Language Models (LLMs), could be used to enhance financial modeling processes.Expect to LearnHow AI is revolutionizing financial modeling and the specific ways it’s being used today.The technical components behind AI agents and how they differ from simple chatbots.The importance of context and system prompts when working with LLMs in financial tasks.Insights into the memory limitations of LLMs and how agents work around this challenge.Here are a few quotes from the episode:"If you're using AI for Excel modeling, you need to remind it to follow good financial modeling principles, like the FAST Standard." – Tim Jacks"The beauty of LLMs is that you can go back and change the conversation, they're stateless, so it's like resetting the clock." – Tim JacksTim Jacks provided valuable insights into the integration of AI in financial modeling, particularly how LLMs and agents are transforming workflows. While AI can significantly enhance efficiency, human expertise remains essential for applying financial modeling principles. Understanding the technical workings of these tools helps users leverage them effectively. The future of financial modeling will be human-led, AI-assisted.Follow Tim:LinkedIn: https://www.linkedin.com/in/timjacks/Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/Follow Giles Male:LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/In today’s episode:[00:05] - Intro & Hosts[01:33] - Guest Introduction: Tim Jacks[02:42] - Tim's Background in Modelling & AI[04:16] - What Are LLMs Really?[09:55] - ChatGPT vs. LLMs Explained[12:09] - LLMs Have No Memory[15:02] - How Tools Add Context to AI[19:35] - What Is an AI Agent?[22:35] - How Excel Agents Work[30:08] - Demo: Tools in Action[35:03] - Defining an Agent: LLM + Tools + Prompts[38:49] - Key Takeaway for Modellers

In this episode of Financial Modeler’s Corner, host Paul Barnhurst welcomes Carolina Lago, a seasoned FP&A professional, to discuss how financial modelers can transform data into actionable insights while avoiding common modeling pitfalls. Together, they explore best practices in financial modeling, the dangers of hard-coded models, and why structure, flexibility, and clear purpose are essential for effective decision-making. Carolina also shares lessons from her international career, including her experience supporting a major IPO and leading global software implementations.Carolina Lago is an FP&A professional with over 15 years of international experience across multiple industries. She has played key roles in high-impact projects, including IPO preparation and enterprise-wide financial system implementations. Carolina is also the creator of the TACTIC framework, which helps financial professionals build models that are structured, insightful, and decision-focused.Expect to LearnWhy hard-coded models are a major risk to accuracy and flexibilityHow to turn raw data into insights that drive real business decisionsThe importance of starting every model with a clear question or goalHow the TACTIC framework improves structure and clarity in modelingWhy strong modeling skills matter at every career stageHere are a few quotes from the episode:“I inherited one, and I had to try to change it. I spent probably a couple of weeks trying to make it better, and I couldn't. It was just too full of hardcoded numbers and no design at all.” – Carolina Lago“Data is only useful if it can be transformed into actionable insights.” – Carolina LagoFollow Carolina:LinkedIn – https://www.linkedin.com/in/s-carolinalago/Website – https://www.tacticfinancial.comIn today’s episode:[00:00] - Trailer[00:50] - Guest Introduction[01:00] - Horrifying Financial Models[02:00] - Early Career Modeling Mistakes[03:10] - Carolina’s Global Career Journey[05:00] - Turning Data into Actionable Insights[07:30] - Introduction to the TACTIC Framework[09:50] - Learning Resources & Community Engagement[20:00] - Certifications and Continuing Education[22:40] - Rapid-Fire Round[24:50] - Advice for Aspiring Financial Modelers[26:00] - How to Connect and Learn More

In this special episode of Financial Modeler’s Corner, host Paul Barnhurst recaps an exciting 2025 and outlines what's ahead for 2026. Paul reflects on the top five most downloaded episodes of the year, shares insights from key guests, and highlights major developments in financial modeling, including Excel's newest features and the growing role of AI.Expect to LearnKey trends in Excel and financial modeling from 2025How AI is changing the way models are built, tested, and auditedThe importance of simplicity, documentation, and user involvement in model designWhy communication and business understanding are becoming essential skills for modelersHere are a few quotes from the episode:“Complexity can backfire by making you indispensable in ways that hurt your career growth.” – Paul Barnhurst“AI is a magnifier, it makes good modelers better and highlights weaknesses in those without a solid foundation.” – Paul BarnhurstIn today’s episode:[02:01] – Mod Squad Launch[03:02] – AI and Modeling[03:45] – Excel Feature Highlights[06:28] – Excel Championship Recap[10:12] – AI in Financial Modeling[12:54] – Time-Saving Modeling Tips[16:02] – Three-Statement Modeling[17:38] – Strategic Thinking for Modelers[20:30] – Final Thoughts and Certification Offer

In this episode of Financial Modeler’s Corner, host Paul Barnhurst welcomes Ian Bennett, Partner and Deals Modelling Leader at PwC Australia, to discuss the art and science of financial modeling. Together, they explore what makes a good financial modeler, how Excel has evolved dramatically in recent years, and how emerging tools and AI are shaping the future of modeling. Ian reflects on his decades-long career, from his early days discovering Excel during audits to leading a large team of modelers across Australia and India. Ian Bennett is the Deals Modelling Partner at PwC Australia and a Master Financial Modeler (MFM) certified by the Financial Modeling Institute. With 24 years of hands-on experience in building and leading modeling teams, Ian’s approach combines deep technical expertise with a strong focus on communication, design, and problem-solving. He leads a 50-person modeling team at PwC and is known for his passionate advocacy for best practices, new tools, and innovation in modeling, including integrating AI and the latest features in Excel.Expect to LearnWhy defining a model’s purpose upfront is essential to successThe most important listening and scoping skills great modelers must developHow Excel’s evolution over the past 18 months is changing the gameWhat it means to be model-first vs. outcome-focusedWhy curiosity and human insight are irreplaceable, even in the age of AIHere are a few quotes from the episode:“Every model tells a story, and that story should be known at the start of the project. It’s about understanding what questions the model needs to answer.” – Ian Bennett“Be curious. That curiosity is what drives innovation in modelling, learning new tools, asking better questions, and solving real problems.” – Ian BennettFollow Ian:LinkedIn - https://www.linkedin.com/in/ianrbennett/Website - https://www.pwc.com.au/deals/modelling.htmlIn today’s episode: [00:00] - Trailer [01:09] - Introduction to Ian Bennett [02:13] - Worst Model Ian Has Seen [06:17] - Ian’s Background & Early Interest in Excel [08:19] - Becoming a Master Financial Modeller (MFM) [09:43] - Global Excel Summit Highlights [11:53] - What Makes a Great Financial Modeller [16:38] - Importance of Listening & Understanding Client Needs [23:03] - Time Allocation: Design vs. Building in Excel [28:14] - Modelling Tools Beyond Excel [31:34] - Excel’s Evolution & Exciting New Features [39:08] - Rapid Fire Questions [41:50] - Will AI Build Financial Models? [47:12] - Final Advice for Aspiring Modellers

In this episode of The ModSquad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male are joined by Tea Kuseva, Community Manager at the Financial Modeling Institute, for a detailed discussion on the state of AI tools in financial modeling. The group continues its hands-on testing of seven tools, including TabAI, Excel Agent, Shortcut, and TrufflePig, evaluating how these platforms perform on real-world financial modeling tasksTea Kuseva is the Community Manager at the Financial Modeling Institute (FMI), the only global accreditation body dedicated to financial modeling. With her deep involvement in the modeling community and her role supporting professionals worldwide, Tea Kuseva brings thoughtful questions and provides structure to the discussion, helping translate technical insights into practical takeaways for finance professionals.Expect to LearnHow leading AI tools perform on real financial modeling tasksCommon issues like unbalanced sheets and flawed formulasKey differences between Excel-based and standalone toolsPractical ways AI can assist with analysis and reportingWhy Excel and modeling expertise still matter in an AI-driven workflowHere are a few quotes from the episode:“Even five years from now, you’ll still need to understand every cell if you're handing in a model.” – Ian Schnoor“Fast, consistent outputs are still better achieved by experienced humans than by today’s AI tools.” – Giles MaleAI tools show promise in assisting with financial modeling, but they are not yet reliable enough to replace human expertise. Strong Excel skills and sound judgment remain essential. Used wisely, AI can enhance productivity, but it should complement, not replace, technical understanding. The future of modeling is human-led, AI-assisted.Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=caFollow Giles Male:LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/Follow Tea:LinkedIn: https://www.linkedin.com/in/tkuseva/In today’s episode:[01:16] - Guest Intro[06:07] - Tools Under the Microscope[07:59] - The Testing Framework[13:43] - Lessons from the Esports Challenges[19:33] - Real Examples from the Tools[25:54] - Practical Use Cases for AI Today[33:56] - Variability in AI Outputs[39:40] - Looking Ahead: The Next Five Years[44:58] - Final Comments[46:13] - Final Thoughts and Key Takeaways

In this episode of The Mod Squad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male continue their hands-on testing of AI tools for financial modeling. This time, they put Subset, an AI-powered spreadsheet tool still in beta, through its paces. The hosts explore whether Subset can realistically handle core financial modeling tasks, including importing Excel files, building three-statement models, and applying basic accounting logic. Along the way, they uncover significant limitations, bugs, and logical errors that highlight the risks of relying on unsupported or immature tools.Expect to LearnWhat Subset promises to do and how it performs in real-world testingThe challenges of importing Excel files into non-Excel environmentsWhy basic accounting logic still breaks many AI modeling toolsThe risks of using outdated or unsupported AI tools found onlineWhat it would actually take for professionals to move away from ExcelHere are a few quotes from the episode:“There’s no AI on the planet that should tell you gross profit is revenue plus costs.” – Ian Schnoor“It’s clever, but massively flawed and unreliable in lots of areas right now.” – Giles MaleSubset shows ambition in trying to act as a full AI spreadsheet, but the testing reveals serious issues, from incorrect formulas to flawed financial logic and unstable performance. While the tool demonstrates how far AI experimentation has come, it also serves as a cautionary example of why finance professionals must validate outputs and maintain strong technical foundations. Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=caFollow Giles Male:LinkedIn -  https://www.linkedin.com/in/giles-male-30643b15/In today’s episode: [02:40] – Welcome back to The Mod Squad[05:04] – Introducing Subset and its promises[08:38] – Importing Excel files into Subset[11:27] – Errors, bugs, and beta limitations[13:50] – Building a three-statement model from scratch[19:25] – A Basic Revenue Reality Check[22:37] – Why Excel Is Hard to Replace[27:10] – Lessons learned from testing multiple tools[30:01] – Why Structured Data Matters

In this episode of The Mod Squad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male continue their exploration of tools for financial modeling. This time, they test Melder, a tool designed to streamline financial modeling tasks in Excel. The hosts evaluate how it handles various financial exercises, such as creating formulas and generating a deferred revenue schedule. While the tool shows promise, the hosts identify areas where Melder has room to improve, particularly with bugs and user experience quirks. This episode also highlights the challenges of using tools still in beta.Expect to LearnA detailed review of Melder’s features for Excel-based financial modeling.How Melder compares to other tools previously tested by the team.Challenges faced when using Melder for tasks like building formulas and financial schedules.The pros and cons of using Melder, especially when it comes to its unique features and limitations.Insights into tools’ development process, especially when still in beta.Here are a few quotes from the episode:"I appreciate the confidence behind the bold statements, but at the end of the day, tools need to make sure they’re doing the job correctly." – Ian Schnoor"When tools go wrong, it’s not just about fixing the error; it’s about understanding what went wrong so we can avoid future issues." – Giles MaleMelder offers some useful features for financial modeling, such as custom formulas and file handling, but it still faces challenges like data overwriting and slow performance. While it shows potential, especially in automating tasks, it needs further refinement to become a reliable tool for complex financial tasks. As it continues to evolve, we look forward to seeing how it improves and addresses these issues.Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=caFollow Giles Male:LinkedIn -  https://www.linkedin.com/in/giles-male-30643b15/In today’s episode: [00:31] - What is Melder?[03:30] - Melder’s Website and Features[08:40] - Testing Melder on Financial Modeling Tasks[12:00] - Exploring Melder’s Formula Creation Capabilities[14:30] - Overview of the LLM Model and Google Gemini Models[19:43] - Testing the Trial Balance and Tool's Thought Process[24:08] - Understanding Overengineered Formulas[32:05] - Testing the PVM Use Case and Encountering Errors[41:51] - Final Thoughts and Melder’s Future Potential

In this episode of Financial Modeler’s Corner, hosts Paul Barnhurst and Ian Schnoor continue their exploration of AI tools for financial modeling. This time, they test Trufflepig, a tool designed to help financial analysts automate spreadsheet tasks while still allowing them to focus on the insights. The hosts test Trufflepig on various financial modeling tasks, discussing its performance and how it compares to other tools they've used. They cover tasks such as building a DCF model for Nvidia, generating executive summaries, and creating a financial forecast. While Trufflepig performs well in some areas, there are still challenges that need to be addressed, particularly with certain financial concepts like working capital and net income.Expect to LearnA review of Trufflepig, an AI-powered spreadsheet tool.How Trufflepig performs on real-world financial tasks.The benefits and limitations of AI tools in financial modeling.Insights into how Trufflepig compares with other financial modeling tools.Here are a few quotes from the episode:“The biggest advantage of using Trufflepig is that it helps you with the repetitive tasks, so you can focus on higher-level analysis.” - Ian Schnoor“Trufflepig is an interesting tool, but as with any new software, there’s a learning curve. But if it delivers value, it’s worth it.” - Ian SchnoorTrufflepig is a promising tool for financial professionals, particularly those looking to automate repetitive spreadsheet tasks. While it performs well on basic tasks like building DCF models and creating executive summaries, there are areas for improvement, especially around financial concepts like working capital and the handling of complex formulas.Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=caTrufflepig: https://Trufflepig.ai/In today’s episode: [01:40] – Review of Previously Tested AI Tools[05:15] – Trufflepig’s Positioning and Messaging[12:00] – Trufflepig Attempts the eSports Modeling Case[22:00] – Challenges with TEXTSPLIT and Modern Excel Functions[30:50] – Executive Summary Generation[40:01] – Data Sourcing and Web Pulling Behavior[49:26] – Reasons for DCF and Market Price Differences[59:45] – Exporting to Excel and Formatting Issues[1:12:26] – Final Review and Closing Thoughts

In this episode of The ModSquad on Financial Modeler’s Corner, Giles Male and Ian Schnoor put Elkar to the test, a financial modeling tool that's been getting attention for its speed and slick design. From solving structured Excel challenges to building full forecast models, they push the tool to its limits. What follows is a revealing look at how Elkar performs when accuracy, logic, and professional modeling standards are on the line. Along the way, they uncover surprising strengths, critical flaws, and even moments of unexpected comedy. Whether you’re curious about automation or cautious about AI in finance, this episode offers plenty to think about.Expect to LearnWhat Elkar gets right: speed, formatting, and a sleek interfaceWhere it breaks down: logic errors, disconnected assumptions, and unreliable outputsHow Elkar stacks up against other AI tools like TabAI and AgentWhy using AI without understanding modeling fundamentals can be dangerousWhat it takes to turn a promising AI output into a reliable financial modelHere are a few quotes from the episode:"Right now, Elkar is like a junior analyst, you see potential, but you can't let them run unsupervised." - Giles Male"AI tools like this might build something that looks like a model, but without logic, it’s a house of cards." - Ian SchnoorIn this episode, Elkar proves to be a fast and visually polished AI tool with clear potential, especially in formatting and task execution speed. However, when it comes to financial logic, assumption structuring, and balance sheet integrity, it consistently misses the mark. The tool even resorts to shortcuts like hardcoding values and plugging imbalances. Follow Giles Male:LinkedIn -  https://www.linkedin.com/in/giles-male-30643b15/Follow Ian:LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=caElkar: https://elkar.coIn today’s episode: [06:48] - Exploring Elkar: Website, Pricing, and Features[10:34] - Elkar Takes on the Esports Excel Challenge[20:14] - Elkar Gets Caught Cheating[24:18] - Elkar Struggles with Complex Logic[35:45] - Cash Flow Logic & Balance Sheet Errors[46:38] - From Hardcoding to Dynamic Assumptions[53:45] - Balance Sheet Plugging and Logical Failure[57:34] - Reviewing Elkar’s Working Capital Assumptions[1:04:20] - Wrap up & Final Thoughts