Financial Modeler's Corner

In this episode of Financial Modeler’s Corner, host Paul Barnhurst sits down with Brian Jones, Vice-President: Microsoft Excel, Forms, Office Platforms, to discuss how Excel is evolving in the era of AI and large language models. They explore how Copilot, agent mode, and AI-driven features are reshaping how professionals build, analyse, and improve spreadsheets. Brian also shares insights into how AI can assist financial modelers, automate tedious spreadsheet tasks, and help users understand complex workbooks faster.Brian Jones is the Vice-President: Microsoft Excel, Forms. He has worked at Microsoft since 1999 across several productivity platforms, including Word, Office Forms, and developer extensibility tools. In his current role, Brian leads the Excel product team as they integrate AI capabilities like Copilot and agent mode to expand what users can accomplish inside spreadsheets.Expect to LearnHow AI and Copilot are changing the Excel user experienceWhat “agent mode” means for automating spreadsheet tasksHow AI can help analyse and improve financial modelsWhy Excel still requires strong modeling and formula knowledgeHow skill sheets and templates can guide AI inside workbooksHere are a few quotes from the episode:“Excel really is like a developer tool. When you're writing formulas, you're essentially coding.” – Brian Jones“One of the strengths of Excel is that the work is visible. You can see the formulas, the logic, and how the result was produced.” – Brian JonesBrian explains how AI is transforming Excel from a traditional spreadsheet tool into a more collaborative environment where users can interact with their data through conversation. Copilot can analyse spreadsheets, suggest formulas, correct errors, and even rebuild broken references automatically.Follow Brian:LinkedIn: https://www.linkedin.com/in/brijones/Website: https://learn.microsoft.com/en-us/archive/blogs/brian_jones/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:[02:53] – Brian’s Background at Microsoft[03:09] – Returning to the Excel Team[04:24] – The Rise of AI and LLMs in Excel[07:08] – Customer Questions About Copilot[10:39] – AI Rollouts and Product Updates[12:03] – How modelers Are Using Excel AI[17:52] – What Skill Sheets Are and Why They Matter[23:07] – Power Query and Excel Programming Languages[30:51] – Do We Still Need to Learn Formulas[36:07] – Getting the Most Out of Copilot[38:40] – Favorite Excel Shortcut and Functions[41:31] – Where to Follow Brian

In this episode of Financial Modeler’s Corner, host Paul Barnhurst sits down with Amber Johnson to discuss forecasting, financial modeling, and how operational decisions impact financial outcomes. They explore the connection between logistics forecasting and financial forecasting, the importance of tracking forecast accuracy and bias, and how small operational issues can create large financial impacts. Amber also shares lessons from her experience working with data, forecasting demand, and helping businesses improve their systems and decision-making.Amber Johnson is a fractional industrial engineer and the founder of Peachy Profitability, where she helps teams work smarter through process improvement, data storytelling, and automation. She began her career as a “beer psychic,” forecasting demand at Anheuser-Busch, and has since built logistics networks, optimized warehouse flows, and guided businesses through transformational change. Amber is also the creator of Office Hours with Amber, a weekly livestream that encourages continuous improvement with curiosity and confidence.Expect to LearnHow logistics forecasting connects to financial forecastingWhy forecast accuracy and bias matter in decision-makingHow operational drivers influence financial resultsWays finance, sales, and logistics teams can align betterHow Excel and data analysis support forecasting and planningHere are a few quotes from the episode:“Sometimes the smallest operational issue becomes the biggest financial problem later.” – Amber Johnson“Good forecasting isn’t about being perfect, it’s about learning and adjusting.” – Amber JohnsonAmber Johnson shared practical insights on forecasting, operational drivers, and financial modeling. She highlighted how understanding logistics and operational data can improve financial decisions and help businesses plan more effectively.Follow Amber:LinkedIn: https://www.linkedin.com/in/ambernjohnsonwmu/Website: https://peachyprofitability.com/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:[02:23] – Amber’s Background[08:21] – Founding Peachy Profitability[10:38] – Learning Excel and Forecasting[15:37] – Logistics vs Financial Forecasting[18:59] – Forecast Accuracy and Bias[23:59] – Finance vs Logistics Perspectives[31:41] – Aligning Teams Around Goals[36:07] – Favourite Excel Shortcuts[37:46] – Rapid Fire: modeling Opinions[44:39] – Where to Find Amber

In this episode of Financial Modeler's Corner, host Paul Barnhurst chats with Nick Boberg, a financial modeler and consultant based in Hamilton, New Zealand. Paul and Nick explore the essentials of building well-structured, effective financial models. They discuss Nick’s approach to simplicity and consistency, the importance of clarity in model design, and the role of competition in refining one’s Excel and modeling skills. Nick is a financial modeler and the co-founder of Boberg Advisory, a consultancy that specializes in providing financial modeling services to SMEs. With extensive experience as Finance Director at Anglesea Hospital and Associate Director at PwC, Nick has built and reviewed hundreds of models, many focused on cash flow forecasting, budgeting, and management reporting. He is also an accomplished competitor in the Financial Modeling World Cup and the Microsoft Excel World Championships, where he has achieved finalist and semifinalist placements.Expect to LearnWhy structure and consistency are key in financial modelingThe role of simplicity in building modelsInsights from Nick’s competitive modeling careerHow to balance technical expertise with user-friendly designHere are a few quotes from the episode:"The beauty of financial modeling lies in its simplicity. If you can make a model both functional and easy to follow, you've mastered it." - Nick Boberg"Consistency in formatting is key. A model that looks clean and well-structured builds trust with the user, especially when it's used for important decision-making." - Nick BobergNick shares that the core of building great financial models lies in structure and simplicity. He emphasizes the importance of creating models that are not only accurate but also easy to follow and understand. Whether in competition or in client work, Nick highlights that clear, well-structured models make a real difference in their effectiveness and usability.Follow Nick:LinkedIn: https://www.linkedin.com/in/nickboberg/Company: https://www.linkedin.com/company/boberg-advisory/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: [03:30] – Worst Modeling Experience[08:08] – Running Boberg Advisory[12:11] – Competing in Excel Championships[17:42] – Importance of Structure[20:00] – Risks of Complex Formulas[23:20] – Avoiding Hard Codes[27:13] – Building "Pretty" Models[30:10] – Dynamic Arrays in modeling[36:05] – Rapid fire Section[40:54] – Last Question & Wrap up

In this episode of The ModSquad, Paul Barnhurst, Ian Schnoor, and Giles Male put Claude 4.6 to the test on real financial modeling accreditation cases. From three-statement forecasts to complex debt sculpting scenarios, the team examines just how far AI tools have come. The results are impressive, but not flawless. The discussion explores what this leap forward means for finance professionals and whether modeling is truly entering a new AI-assisted era.Ian Schnoor is Executive Director of the Financial Modeling Institute (FMI), the global accreditation body for financial modeling professionals. He brings extensive experience in modeling, training, and industry standards. Giles Male is Co-Founder of Full Stack Modeller and a two-time Microsoft MVP. He specializes in Excel, financial modeling systems, and practical AI implementation.Expect to LearnHow Claude 4.6 performs on real financial modeling accreditation casesWhere AI tools still make subtle but significant modeling errorsThe difference between automation and augmentation in AI usageWhy strong modeling fundamentals remain essentialPractical ways to begin integrating AI into your modeling workflowHere are a few quotes from the episode:“I’m not even doing this from a testing perspective now. I’m just using it because it’s adding so much value.” – Giles Male“Modeling is just as much about the process as it is about the end result.” – Ian SchnoorClaude 4.6 marks a significant step forward in AI-assisted financial modeling, handling complex builds faster than ever before. However, subtle errors still highlight the need for strong technical knowledge and human oversight. The future of modeling isn’t replacement, it’s skilled professionals using AI to work smarter and deliver greater value.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:[04:06] – Testing Claude[11:14] – Augmentation vs. Automation in Modeling[18:14] – The Value of Documentation in Modeling[28:43] – Debt Modeling with AI[33:20] – Transition from Manual to AI-Enhanced Modeling[38:16] – Testing with New Tools[41:24] – Debt and Equity Modeling with AI[46:45] – Claude's Progress & Areas for Improvement[57:33] – Final Thoughts

In this episode of Financial Modeler's Corner, host Paul Barnhurst is joined by Chris Reilly, founder of Financial Modeling Education, for an insightful discussion on the importance of mastering financial modeling fundamentals in the age of AI. Chris shares his perspective on how AI is transforming the modeling industry, why understanding the basics is more important than ever, and how dynamic arrays are changing the modeling landscape.Chris Reilly is a financial modeling expert with over 91,000 students trained globally. He has a diverse background, having worked in private equity and FP&A, and has developed a range of training programs through Wall Street Prep, Wharton, LinkedIn Learning, and his own courses. Chris emphasizes that strong foundational knowledge in finance and accounting is crucial to leveraging AI and new technologies effectively.Expect to LearnWhy mastering the fundamentals of financial modeling is essential in an AI-driven worldHow Chris integrates AI tools to enhance his modeling workThe role of dynamic arrays in streamlining financial modelsThe balance between technical skills and business decision-makingInsights on why complex models aren't always betterHere are a few quotes from the episode:“AI is a magnifier. If you know what you're doing, it helps you get more done. If not, it makes problems worse.” - Chris Reilly“The real goal of any model is to help the business understand what to do next, not just to show off technical skills.” - Chris ReillyChris highlights how AI can help with dynamic arrays and improve model efficiency, but stresses that it should never replace the need for a strong understanding of financial fundamentals. His focus is on helping modelers leverage the right technology without bypassing essential knowledge.Follow Chris:LinkedIn: https://www.linkedin.com/in/chris-reilly-mission-capital/Homepage: https://www.financialmodelingeducation.com/Email list: https://financialmodelingeducator.com/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:[02:06] - Why Fundamentals Matter in AI Modeling[03:29] - Chris's Focus on Education & Clients[04:44] - How AI Amplifies Strengths & Weaknesses[05:39] - Dynamic Arrays: Excel's Game-Changer[07:09] - Should Dynamic Arrays Be Taught?[09:47] - Combining Functions for Efficient Models[12:01] - Struggles with New Excel Features[13:18] - Technical vs. Business-Focused Models[14:59] - The Joy of Mastering Balanced Models[15:44] - Closing & Chris' Educational Resources

In this episode of Financial Modeler’s Corner, host Paul Barnhurst welcomes back Chris Reilly for a deep and practical conversation on financial modeling fundamentals in the age of AI. Chris shares why mastering the basics still matters more than ever, how AI is changing modeling workflows in reality (not hype), and where automation genuinely adds value versus where human judgment is still essential.Chris Reilly is a former private equity professional and financial modeling expert with experience spanning restructuring, FP&A, treasury, and middle market private equity. He began his career at FTI Consulting during the financial crisis, working on major bankruptcies, including Lehman Brothers, before moving to Hilton Worldwide and later into private equity. Chris is the founder of Financial Modeling Education, where he has trained more than 90,000 professionals worldwide, teaching real-life models used to acquire and manage private equity-backed businesses.Expect to LearnWhy financial modeling fundamentals matter more than ever in an AI-driven worldHow Chris actually uses AI in real client models versus online hypeWhy simple, well-built models outperform overly complex onesHow to balance technical modeling skills with business decision-makingHere are a few quotes from the episode:“AI is a great accelerator if you already understand the fundamentals. If you don’t, it just magnifies the problem.” - Chris Reilly“There’s a missing middle right now where people are skipping the fundamentals and jumping straight to the results.” - Chris ReillyChris emphasizes that while AI and automation can improve efficiency, they do not replace the need for strong accounting knowledge, modeling fundamentals, and business understanding. He explains that the best modelers focus less on tools and functions and more on clarity, reliability, and helping decision makers understand what to do next.Follow Chris:LinkedIn: https://www.linkedin.com/in/chris-reilly-mission-capital/Homepage: https://www.financialmodelingeducation.com/Email list: https://financialmodelingeducator.com/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: [02:35] - Fundamentals Before AI[05:55] - From PE to Teaching[09:17] - Dynamic Arrays in Practice[13:25] - Limits of Dynamic Models[18:09] - Modeling vs Technical Debate[21:04] - AI Hype vs Reality[26:45] - Avoiding Bad Models[33:00] - Advice for Modelers[35:38] - Future of AI in Excel[38:02] - Closing Thoughts

In this episode of Financial Modeler’s Corner, host Paul Barnhurst sits down with Luke Phillips, Senior Business Analyst at Access Analytic. Luke shares how he transitioned from Division I basketball to financial modeling, and how he helps clients solve complex budgeting and reporting problems using Solver, Excel, and Power BI.Luke Phillips is a chartered financial modeler and the Senior Business Analyst at Access Analytic. He works with clients across mining, oil and gas, manufacturing, and professional services to streamline budgeting and reporting through Solver and Excel. Luke holds a BBA in Finance from the University of Louisiana at Monroe, where he also played Division I basketball.Expect to LearnHow a bad college project helped kickstart Luke’s modeling careerWhat Solver is and how it supports complex planning and reportingTips for simplifying models without losing valueThe role of communication in building useful modelsLuke’s take on AI and dynamic arrays in ExcelHere are a few quotes from the episode:“We're not hiring you for your technical ability, we're hiring you for your potential.” – Luke“I love spreadsheets, the way you can pull complex calculations together and form a view of a company.” – LukeLuke Phillips brings a grounded, real-world perspective to financial modeling, shaped by both professional experience and athletic discipline. His approach blends technical skill with a clear focus on communication and usability. Whether solving client challenges with Solver or exploring new Excel features, Luke keeps modeling practical and purpose-driven.Follow Luke:LinkedIn: https://www.linkedin.com/in/luke-phillips-75859013a/Company: https://www.linkedin.com/company/access-analytic/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:[02:09] - Life in Australia & Background[03:16] - First Model Mistake & Lesson[04:46] - From Basketball to Finance[08:49] - What is Solver & Luke’s Role[10:33] - Learning Supply Chain & Ops[13:11] - Simplicity vs. Complexity[18:24] - AFM & CFM Exam Journey[22:46] - XLOOKUP Over INDEX MATCH[27:46] - AI in modeling & Challenges

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

In this episode of The ModSquad on Financial Modeler’s Corner, Paul Barnhurst and Giles Male put Shortcut under the AI microscope, testing one of the most hyped AI tools in the financial modeling world. With claims like “the most accurate Excel agent in the world” and the ability to outperform human champions in modeling tasks, Shortcut has made a big splash, but does it live up to its own bold promises? Paul and Giles run it through a rigorous series of real-world modeling challenges, from esports cases and financial forecasts to dashboard analysis and deferred revenue schedules. What they find is a tool with clear potential, and some serious red flags.Expect to LearnWhere Shortcut impresses with formatting, speed, and usabilityWhere it fails, especially with modeling logic How Shortcut compares to Excel Agent and TabAI across key modeling tasksWhy reversing formatting in a model is a huge red flagWhat to consider when investing in premium AI tools for modelingHere are a few quotes from the episode:“Shortcut has potential, but right now it’s flash over fundamentals.” - Giles Male“Could you imagine an analyst reversing formatting to make a number look negative? They’d be out the door.” - Giles MaleDespite the hype, Shortcut proved to be a solid tool with promise. It delivered impressive formatting and UI, yet had some serious issues like incorrect logic, hardcoded values, and non-balancing models, which held it back. A promising AI assistant, just not a replacement for real modeling expertise.Follow Giles Male:LinkedIn -  https://www.linkedin.com/in/giles-male-30643b15/In today’s episode: [01:15] - Intro & Where the AI Modeling Journey Stands[05:11] - Shortcut: First Impressions & Bold Claims[14:28] - Viral Demo Video Breakdown[23:12] - Esports Challenge: Basic Excel Tasks[31:19] - Intermediate Case: Modeling Accuracy[36:10] - Building a 3-Statement Forecast[44:24] - Red Flags: Formatting & Balance Sheet Errors[50:27] - Deferred Revenue Test[56:32] - Trial Balance Dashboard: Visuals vs. Substance[1:07:22] - Final Thoughts & Shortcut's Ranking

In this episode of The Mod Squad on Financial Modeler’s Corner, Paul Barnhurst, Ian Schnoor, and Giles Male take a close look at TabAI, a tool designed to simplify and speed up Excel tasks using automation and intelligent suggestions. With more tools dropping out of the market and Excel’s own Agent feature gaining ground, the question is simple: Does TabAI offer something worth switching to? From cleaning data and building dashboards to attempting a full five-year forecast, the team puts TabAI through a series of real-world modeling challenges to see what it gets right and where it still falls short.Expect to LearnWhere TabAI shines in helping analysts and where it needs improvement.How does it compare to Excel Agent in terms of speed, usability, and accuracy?Why finance pros still need to understand what’s going on under the hood.What to watch for when relying on tools that promise “done-for-you” modeling.Here are a few quotes from the episode:“Agent was faster, but TabAI handled more advanced stuff better.” - Ian Schnoor“AI is great at building things fast, but one small mistake can make the whole model unusable.” - Giles MaleTabAI turned out to be one of the more impressive tools we’ve tested so far, especially when it comes to everyday Excel tasks and building dashboards. It’s not perfect, especially with full-scale models, but it’s definitely a step in the right direction. For now, it’s a great helper, but you’ll still need your own modeling skills to get the job done right.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: [02:28] - TabAI Leaves Retail[05:17] - Competing with Excel Agent[06:50] - TabAI Feature Overview[10:30] - The “Iron Man Suit” Claim[14:28] - eSports Case Test[23:12] - Dancing Fur Coat Model[29:14] - Trial Balance Dashboard[33:56] - Deferred Revenue Test[38:36] - Full Forecast Model Build[51:10] - Final Thoughts

In Episode 5 of The ModSquad on Financial Modeler’s Corner, Paul Barnhurst, Ian Schnoor, and Giles Male take a hard look at the changing landscape of financial modeling in the wake of Microsoft’s release of Excel Agent. Since launching at the end of September to coincide with Excel’s 40th birthday, Excel Agent has quickly changed the competitive dynamics for AI-powered modeling tools. The team explores the implications: how Excel Agent’s capabilities compare to other tools, why third-party platforms are shutting down, and what all this means for the future of work in modeling-heavy industries like investment banking.Expect to LearnWhy Excel Agent is pushing competing modeling tools like Rosie AI out of the market.What makes Excel Agent a “magnifier” of both modeling skill and error.How fast AI is evolving inside Excel and what that means for modelers today.Why AI won’t reduce hours in finance, despite speeding up modeling work.What OpenAI’s Project Mercury reveals about the next phase of automation in investment banking.Here are a few quotes from the episode:“You can't hit a prompt, go get a coffee, and expect a working model.” – Giles Male“If you don’t understand what the AI just built, you’re in trouble.” – Ian SchnoorThis episode makes it clear: AI is not a replacement for skill; it’s a multiplier. Excel Agent may be setting the new standard, but success still comes down to human understanding, judgment, and accountability. As the modeling world evolves rapidly, professionals who stay informed and upskill will thrive. The Mod Squad isn’t slowing down either; more tool reviews and sharp conversations are coming.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: [05:29] - AI Tools Recap[07:26] - AI Hype and Hidden Risks[10:23] - AI as a Skill Magnifier[13:48] - Microsoft’s Impact on AI Startups[16:15] - Rapid Evolution of Excel AI[21:29] - OpenAI’s Role in Financial Modeling[29:17] - Understanding Assumptions and Calculations[31:53] - Final Thought