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 Learn

• How a bad college project helped kickstart Luke’s modeling career

• What Solver is and how it supports complex planning and reporting

• Tips for simplifying models without losing value

• The role of communication in building useful models

• Luke’s take on AI and dynamic arrays in Excel

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 Learn

• Why using someone else’s model as a template can be risky

• The importance of understanding and communicating key assumptions

• How to tailor models for investors and third-party users

• What it’s like to fail, and then pass, the AFM exam

• The value of applying both corporate and project finance in modelling

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 Learn

• How 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.

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.

Tea 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 Learn

• How leading AI tools perform on real financial modeling tasks

• Common issues like unbalanced sheets and flawed formulas

• Key differences between Excel-based and standalone tools
  Practical ways AI can assist with analysis and reporting

• Why Excel and modeling expertise still matter in an AI-driven workflow

Expect to Learn

• What Subset promises to do and how it performs in real-world testing

• The challenges of importing Excel files into non-Excel environments

• Why basic accounting logic still breaks many AI modeling tools

• The risks of using outdated or unsupported AI tools found online

• What it would actually take for professionals to move away from Excel

Expect to Learn

• A 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 the tools’ development process, especially when still in beta.

Expect to Learn

• A 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.

Expect to Learn

• What Elkar gets right: speed, formatting, and a sleek interface

• Where it breaks down: logic errors, disconnected assumptions, and unreliable outputs

• How Elkar stacks up against other AI tools like TabAI and Agent

• Why using AI without understanding modeling fundamentals can be dangerous

• What it takes to turn a promising AI output into a reliable financial model

Expect to Learn

• Where Shortcut impresses with formatting, speed, and usability

• Where it fails, especially with modeling logic and financial accuracy

• How Shortcut compares to Excel Agent and TabAI across key modeling tasks

• Why reversing formatting to hide modeling errors is a huge red flag

• What to consider when investing in premium AI tools for modeling

Expect to Learn

• Where 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.

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 Learn

• Why 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.

In this episode of The ModSquad on Financial Modeler's Corner, Paul Barnhurst, Ian Schnoor, and Giles Male explore Microsoft’s newly released Excel Agent, a beta tool designed to bring AI into Excel Online. The team compares its performance to Rosie AI, running both through a range of tasks, including formula building, audit reviews, and a full five-year model forecast. Along the way, they test the tools against real modeling challenges like the Excel Esports case and a creative custom case, "The Humble MVP." This isn’t just about flashy tech. It’s a deeper conversation on where AI can help, where it falls short, and why core financial modeling skills still matter.

Expect to Learn

• What happened when Excel Agent tried to build a full five-year forecast model?

• Why solid modeling skills still beat "one-click" AI hype.

• Where AI tools succeed, and fail, in reviewing formulas and detecting issues.

• Why you should never fully trust AI without understanding what it’s doing.

In this episode of the ModSquad series on Financial Modeler’s Corner, host Paul Barnhurst is joined by modeling experts Ian Schnoor and Giles Male to evaluate Rosie AI, a new tool that integrates with both Excel and Google Sheets. Together, they test its capabilities in building financial models, solving complex FP&A tasks, and performing real-world use cases. They push Rosie through a series of tests, from basic formula creation to building full three-statement models, and discuss where it excels, where it needs improvement, and its potential future in the world of financial modeling.

Expect to Learn

• How Rosie AI performs in real-world financial modeling tasks

• The strengths and weaknesses of Rosie in building models and formulas

• Key insights into how AI tools are evolving in financial modeling

• The importance of knowing how to validate AI-generated models

• How Rosie compares with other AI tools and traditional financial modeling techniques

Expect to Learn

• How Tracelight performs across different financial modeling scenarios

• The impact of LLM selection and prompt design on tool output

• Where Tracelight excels, and where it falls short, in building models and running checks

• How Excel eSports challenges expose strengths and weaknesses in formula logic

• When these tools can save time, and when they create more work

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

Expect to Learn

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

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

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

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

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

Scott Strachan is a Chartered Accountant and Chartered Financial Modeler who has worked across the UK, the Arabian Gulf, and now serves clients in Qatar and London. He has built and reviewed complex financial models for deals worth hundreds of millions of dollars, and now contributes to the Financial Modeling Institute as a content developer and exam writer. Scott is known for applying practical thinking to modeling and helping bridge the gap between theory and real-world execution.

Expect to Learn

• Why annual models are often inappropriate for startups and small businesses

• The difference between one-time-use models and those built for regular updates

• How poor modeling leads to funding gaps and business failure
  Why EBITDA can be misleading in deal evaluation

• How preparing for the CFM exam can improve your modeling skills